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Distributed multirobot exploration maintaining a mobile network
Abstract
We present a behaviour-based architecture for multirobot exploration and mapping. The architecture is designed to guide the exploration in a decentralized fashion constrained to maintain local short-range communication in a mobile ad-hoc network. Exploration with multiple robots has been extensively studied but communication constraints have been ignored. We focus on the integration of such constraints and develop a behaviour-based approach. The behaviours are designed to enhance global performance and are triggered based on local information. Robots are encouraged to move together forming a mobile network and sharing relevant information for the team. Up to this date there is no research addressing the effects of communication and sensor constraints on exploration and mapping in structured (office like) environments. Our experimental results show that the increment in time due to the introduction of communication constraints is reasonable and decreases as the size of the team increases.
... Pour le troisième critère, nous trouvons que le niveau de propagation des données sur les positions de robots est en forte relation avec le modèle mathématique sous-jacent utilisé pour le maintien de la connectivité : une propagation glo- [Vazquez and Malcolm, 2004] distribuée globale graphe complet [Notarstefano et al., 2006] distribuée globale graphe algébrique [Sheng et al., 2006b] distribuée globale distance de proximité [Rooker and Birk, 2007] centralisée globale graphe complet [Stump et al., 2008] centralisée globale graphe algébrique connectivité algébrique Pappas, 2005, Srivastava andSpong, 2008] centralisée globale matrice d'adjacence [Gennaro and Jadbabaie, 2006, Zavlanos and Pappas, 2007, Zavlanos and Pappas, 2008, Micheal et al., 2009, Schuresko, 2009 distribuée globale graphe algébrique basé sur ST [Diosdado, 2006, Schuresko, 2009 distribuée locale / partielle ST bale dans la connectivité algébrique et des échanges locaux dans la connectivité basée sur l'arbre couvrant ST . C'est la raison d'avoir regroupé les deux dernières critères ensemble dans notre taxonomie : les solutions dérivées de la connectivité algébrique avec la propagation globale, les solutions basées sur l'arbre couvrant ST avec la propagation locale des positions des robots. ...
... Dans les premiers travaux intégrant les systèmes multi-robots et le réseau ad-hoc, les solutions pour maintenir la connectivité étaient étroitement couplées aux applications, et ont été conçues de manière ad hoc 8 . Un exemple typique est l'exploration d'un environnement inconnu par une équipe de robots qui communiquent afin de cartographier conjointement le terrain [Vazquez and Malcolm, 2004, Sheng et al., 2006b, Rooker and Birk, 2007. ...
... Afin de garder la connectivité de manière décentralisée, les robots d'explo-ration dans [Vazquez and Malcolm, 2004] analysent la topologie complète du réseau pour reconnaître les liens critiques. S'il y a un lien critique, la tâche consistant à maintenir le lien est prioritaire sur l'exploration. ...
Given the complexity of the tasks to be undertaken by a multi-robot system,
the approach most commonly adopted is to “divide and conquer”. It is to
decompose the complex task into subtasks, then each of these sub-tasks into
sub-sub-tasks. This process is repeated until we reach basic tasks that can be
made by individual robots. This approach, although attractive at first glance, has
the disadvantage of not always be easy to implement. Indeed, in the majority
of existing work, the decomposition of a complex task into elementary tasks is
performed in a rather ad hoc and dependent upon the target robot system. This
is indeed a barrier to reuse the result of the decomposition – both software to the
logic level with other robotic systems.
In this thesis, we propose a solution that allows any collection of heterogeneous
robots to organize themselves into teams and sub-teams and this, according
to both the requirements of the task at hand, robots available and resources.
Our approach based on the decomposition of a complex task roles, separates
the concerns of the design and implementation level. Thus, one logical solution
may be (re)used on multi-robot systems with varying abilities. Once the task is
decomposed into roles, we are facing a well-known, yet unresolved problem :
the general problem of assignment of roles to robots effectively. Faced with this
NP-hard problem, we propose heuristics based on the Contract-Net protocol to
assign roles to the robots to form coalitions. Each coalition is composed of robots
cooperating tightly to perform a single task.
Assigning roles to robots, as well as the cooperation of the latter requires
that the robots must be able to communicate frequently. As a result, the network
connectivity of robots is a prerequisite for communication. We propose a novel
solution to this problem based on our concept of “connectivity awareness”. It is
about equipping each robot with knowledge of the network structure. We show
that a partial and local knowledge in each robot can be exploited for maintaining
network connectivity in the robust distributed manner. Each robot can plan its
own move locally without jeopardizing the overall network connectivity. Indeed,
this local knowledge that is the connectivity awareness can be exploited so that
each robot can determine whether the global net
... In a multirobot search and rescue system [1][2][3], this concern becomes more serious because losing connection may cause unnecessary casualties. Maintaining connectivity of a robot team is also crucial in applications of persistent surveillance, joint exploration, and mapping, where robots must have the ability to align themselves along the boundaries of complex shapes in two dimensions while ensuring the successful transmission of critical data [4][5][6][7]. Other applications requiring connectivity preservation include rendezvous and formation control [8][9][10][11], wireless distributed computing [12], etc. ...
... Two behavioral assemblages were involved: wander-avoid-past and living-in-past. Vazquez et al. [6] presented a behavior-based architecture for multirobot exploration and mapping. In this architecture, if the signal strength among the robots on the bridge of the robot network fell below a threshold, the achieve-connectivity action was executed. ...
... Solve the bold TSP (lines[2][3][4][5][6][7][8][9][10][11][12][13][14]: First, enclose all the ISs with circles of radius r b centered at certain ISs (lines 2-12). To select proper ISs as the covering centers, the algorithm calculates the distance between any pair of ISs. ...
A multirobot system provides many advantages over a single robot. In certain situations, robots need to maintain global connectivity while proceeding through tasks such as traveling to spots of interest in an area. This paper formulates the problem of multiple robots traveling while constrained by connectivity and analyzes the limits of increase in total traveling distance (TTD) caused by connectivity constraints. A connection condition is proposed and proven, which can be used to direct the design of solutions. Two algorithms satisfying the connection condition, connected nearest neighbor (CNN) and bold Lin-Kernighan heuristic (B-LKH), are proposed to solve this problem, which consider the connectivity constraint in planning paths. Simulations are designed to investigate the in uence of important parameters, and comprehensive comparisons among algorithms are also conducted. The results show that CNN and B-LKH significantly outperform previous systems with respect to TTD.
... Network connected multi-robot control is an active field of research. Most studies on connectivity aware multi-robot systems focus on cooperative exploration and target tracking [15] [14][11][6] [5]. Vazquez and Malcolm [15], developed a behavior based scheme for multi-robot exploration under connectivity constraints. ...
... Most studies on connectivity aware multi-robot systems focus on cooperative exploration and target tracking [15] [14][11][6] [5]. Vazquez and Malcolm [15], developed a behavior based scheme for multi-robot exploration under connectivity constraints. They do not consider the effect of obstacles in the environment on communication range. ...
... With anymore increase in number of agents there is a decline in performance. These results are consistent with the existing literature ( [15] and the references therein). ...
Multi-robot systems find application in real-time operations like remote surveillance, and search and rescue. Some of these missions involve critical tasks that require human in-put for decision making. To facilitate human-in-the-loop (HIL) task servicing, the task executing robot (Service Agent) is required to remain connected to a remotely located human operator.
The development of connectivity constrained coordination algorithms is complex due to limited communication range and presence of obstacles in the search region. In this paper, we present a distributed multi-robot algorithm for task servicing with HIL constraint. The algorithm facilitates in-direct collaboration amongst the robotic agents and uses a combination of graph theoretic and gradient descent based approaches for path planning. It allows dynamic task reassignments and role exchanges amongst the agents based on increased situational awareness. To substantiate the claims, we present results from extensive simulations.
... Subsequent work developed methods for maintaining communication in distributed multi-robot networks [21], merging occupancy grids from teams of robots [22], and obeying constraints of wireless networks during exploration [23]. Research on optimizing exploration algorithms for robot teams also continued at this time [24,25]. ...
... (21) and (22), the relative tradeoff between fuel use and negative obstacle detection for a given terrain can be calculated for the swarm. The detection rate per unit fuel (δ d /f v ) can be found by dividing Eq. (22) by Eq. (21) δ d /f v = 678.35l −2.55 (23) This model is specific to this particular AGV team and detection tasks. ...
Autonomous ground vehicles (AGV) operating collaboratively have several advantages over vehicles operating alone. An AGV team may be more resilient and efficient than a single AGV. Other advantages of AGV teams include increased coverage and multiple viewing angles of terrain features as well as resistance to failure from any single AGV. Additionally, AGV teams can explore large terrains more quickly and thoroughly than a single system. In this work, the feasibility of using a team of high-mobility AGV to explore a navigation corridor, map the terrain, and autonomously flag obstacles for future navigation is evaluated. Focusing on negative obstacles, the value of using multiple vehicles to map a navigation corridor is quantified. This study is the first to evaluate large teams of AGV collaborating in realistic off-road, 3D environments. The feasibility of the large-scale AGV team is demonstrated while avoiding the high cost of purchasing and testing large numbers of vehicles by using the MSU Autonomous Vehicle Simulator (MAVS), a high-fidelity, physics-based simulation tool. The cost and benefits of increasing the AGV team size are evaluated. The simulation results show how factors like fuel use, map coverage, and obstacle detection are influenced by increasing numbers of AGV in the team. The simulation architecture is presented and experiments quantifying the performance of the simulator are shown. Finally, a model for evaluating the tradeoff between mission effectiveness and fuel use is developed and presented to demonstrate the utility of this approach.
... In [21] a behaviour-based approach is presented. The architecture is designed to guide the exploration constraining the fleet to keep within the communication range, establishing a mobile network. ...
... On this domain, the function x u+x presents an absolute maximum equal to 1 in the point (x, u) = (1, 0), and absolute minima equal to 0 along the line segment defined by (x, u) = (0, u). Assessing the α HO expression derived in (15) with (0, u) leads to (21) and (22), respectively: ...
Featured Application: In application fields where strong communication requirements do not condition the mission, the present approach represents a proper option for coping with real communication constraints, being more fault tolerant and still having good performance simultaneously.
Abstract: The exploration problem is a fundamental subject in autonomous mobile robotics that deals with achieving the complete coverage of a previously unknown environment. There are several scenarios where completing exploration of a zone is a main part of the mission. Due to the efficiency and robustness brought by multi-robot systems, exploration is usually done cooperatively. Wireless communication plays an important role in collaborative multi-robot strategies. Unfortunately, the assumption of stable communication and end-to-end connectivity may be easily compromised in real scenarios. In this paper, a novel auto-adaptive multi-objective strategy is followed to support the selection of tasks regarding both exploration performance and connectivity level. Compared with others, the proposed approach shows effectiveness and flexibility to tackle the multi-robot exploration problem, being capable of decreasing the last of disconnection periods without noticeable degradation of the completion exploration time.
... Enfin, les contraintes inter-agents permettent de s'assurer que chaque détecteur n'est affecté qu'à un mobile au maximum. Le problème d'exploration multi-robots L'exploration d'un environnement inconnu tout en vérifiant des contraintes de communication est considérée comme un problème difficile [Rooker et Birk, 2007], [Vazquez et Malcolm, 2004]. Dans le problème d'exploration multi-robots [Doniec et al., 2009], les robots disposent de capacités de communication (wifi par exemple) et de capacités de détection (par exemple camera ou laser). ...
... Parmi les applications des systèmes multi-robots, l'exploration d'un environnement inconnu sous des contraintes de communication est considéré comme un problème difficile ( [Rooker et Birk, 2007, Vazquez et Malcolm, 2004). Les robots doivent maintenir, durant toute l'exploration, une connectivité avec le reste de la flotte de robots afin de pouvoir constamment envoyer des messages. ...
The CSP formalism (Constraint Satisfaction Problem) can represent many problems in a simple and efficient way. However, some of these problems cannot be solved in a classical and centralized way. The causes can be multiple: prohibitive repatriation time, unsecured data and so on. Distributed CSP (DisCSP), domain intersecting MAS and CSP, are used to model and to solve these problems. The intra-agent and inter-agent reasonning are so based on a set of relation between different variables. The agents interact in order to build a global solution from local solutions. We propose, in this work, an algorithm for solving DisCSP named Distributed Backtracking with Sessions (DBS) which allows to solve DisCSP where each agent owns a complex local problem. DBS has the particularity to not use nogoods like the majority of algorithms for solvingDisCSP but to use instead of sessions. These sessions are numbers which allow to assign a context to each agent and each message exchanged during the resolution of the problem. DBS is a complete algorithm which allows the use of filters on messages exchanged without affecting the proof of completeness. Our proposal is evaluated, for mono-variable and multi-variables per agents problems, on different classical benchmarks (distributed graph coloring problems and random DisCSP) and on an unknown environment exploration problem.
... Although robots take actions independently, the controller monitors them closely and dictates plan progress. Vazquez and Malcolm [100] also use a distributed approach to a closely related problem in which communication attenuates with range but not through obstacles. Each robot maintains a complete map of the team's network and attempts to explore unknown areas provided it wont disconnect the team. ...
... Malcolm's approach is distributed [100], it requires every teammate to have this complete information. Given that this approach cannot scale and also uses short-term planning, it is likely that centralized approaches (and even distributed approaches with long-term planning such as Hoplites) will easily outperform it. ...
... In previous research we developed coordination algorithms for exploration of unknown environments for groups of mobile robots [5]. The localization of the robot was assumed to be known within a bounded error. ...
... This has achieved remarkable results in localizing sonar-based robots. However there are delays in incorporating features into a map, which our previous research [5] suggests is undesirable in multirobot scenarios. ...
We present a novel approach that incorporates informa-tion from sonar and infrared sensors mounted on a ro-tating platform to obtain feature-based stochastic maps of the environment. The purpose is to reliably deter-mine the position of a robot and the features in its environment using low cost sensors. Line and corner features are extracted from the sonar sensors by means of triangulation from multiple vantage points, while line features are extracted from the infrared sensors in sepa-rate processes. RANSAC-based approaches are used to extract the features from sonar data and from infrared data. An extended kalman filter is used to update the position of the robot and the features. The addition of infrared data to sonar data makes maps more accurate and compact.
... Cohesion of groups of moving agents has been investigated in many contexts. In addition to many flocking and formation algorithms, local cohesion has been provided using switching laws that alternate between mission objectives and connection reinforcement (e.g., [18]- [20]); using potential fields to combine mission objectives and cohesion (e.g., [21], [22]); and using algebraic approximations of arbitrary geometric constraints (e.g., [23]). Global cohesion has been added in [20], [21] by communicating the entire network connectivity to each agent and then selecting a set of cuts to be made; [21] also discusses market-based consensus algorithms for selecting cuts. ...
... In addition to many flocking and formation algorithms, local cohesion has been provided using switching laws that alternate between mission objectives and connection reinforcement (e.g., [18]- [20]); using potential fields to combine mission objectives and cohesion (e.g., [21], [22]); and using algebraic approximations of arbitrary geometric constraints (e.g., [23]). Global cohesion has been added in [20], [21] by communicating the entire network connectivity to each agent and then selecting a set of cuts to be made; [21] also discusses market-based consensus algorithms for selecting cuts. Our approach to global cohesion is based on comparable identities instead of market-based decisions, and only requires each agent to recall a set of messages received in a finite time window: no agent needs to accumulate knowledge of the connectivity graph. ...
We consider the coalescence or gathering problem: a set of k mobile agents (robots) with distinct identifiers have to locate one another and gather at a common location. Agents may face several challenges: swarms of agent may be too large to simultaneously observe one another, agent sensors may be subject to noise, and some agents may be malicious. For small swarms, we design an algorithm coalescing all trust-worthy agents despite sensor noise and an arbitrary number of malicious agents. This algorithm requires that all coalescing agents be able to simultaneously observe a single agent, limiting its practical scalability. For arbitrarily large swarms we design an algorithm achieving coalescence as long as the number of malicious agents near any given trustworthy agent is bounded. In both cases, our algorithms work by reduction to black-box solutions to well-studied problems, allowing them to be used in any environment and for any type of agents for which deterministic rendezvous, agent state estimation, and pair-wise cohesion algorithms are available.
... Specifically a first-order prediction model is used to predict one step ahead network topology and to identify the region where there is higher probability of maintaining network connectivity. A behavior-based architecture is proposed in [20] to encourage a team of robots to maintain a local communication network while exploring an area; during exploration, any two robots that form a bridge connection, i.e., a link whose removal disconnects the network, are forced to perform connectivity-behavior task when their distance are greater than a given threshold. An algorithm that, starting from an arbitrary initial connected condition, modifies the robots' location to achieve a fault-tolerant bi-connected configuration is proposed in [5]. ...
... Moreover, in order to avoid collisions, all other bodies in the environment (i.e., base station, other antennas, agent and obstacles) must be farther than d min,i from each antenna. Notice that this definition is similar to that of comfort zone in [20], therefore, this term will be used in the following to denote the area shown inFigure 3 Then, at time t the i-th vehicle is able to directly communicate with all vehicles which are inside the region I i (t). The set of all direct communication paths constitute a graph that can be synthetically represented via the associated incidence matrix G whose entries satisfy ...
This paper investigates the implementation of a wireless mobile ad-hoc network to guarantee that an autonomous agent, i.e., an autonomously driven mobile vehicle or a human, remains connected to a fixed base station while performing its own mission. To the purpose, the use of a platoon of mobile robots is proposed to carry a number of repeater antennas; these are suitably moved to dynamically ensure a multi-hop communication link to the moving agent, hence extending and adapting the area covered by the sole base station. Self configuration of the robots' platoon is achieved by a singularity-robust task-priority inverse kinematics algorithm via the definition of suitable task functions. The obtained simulation results show the effectiveness of the proposed approach.
... The behavior of a Mobile Robot system [4] is defined as a complex system (swarm of Mobile Robots [5]) that solves a common problem, where each solves his individual task, defined by a strategy [6], while communicating with other robots in order to obtain the necessary information [7,8,9] with aim to achieve the target objectives, with resources and in the shortest possible time [10]. ...
... Vazquez and Malcolm [22] pressed that an exploration algorithm is based on certain objectives: to avoid obstacles, to maintain communication between robots and to explore around the frontier. By using this approach, the connectivity of the network is taken into account. ...
... Unfortunately, building optimal or near-optimal coordination strategies is not straightforward. This is why there is substantial effort put by the research community to address instances of this problem such as the multi-robot exploration and mapping [3][4][5][6][7][8][9][10][11][12][13]. ...
While the robotics community agrees that the benchmarking is of high importance to objectively compare different solutions, there are only few and limited tools to support it. To address this issue in the context of multi-robot systems, we have defined a benchmarking process based on experimental designs, which aimed at improving the reproducibility of experiments by making explicit all elements of a benchmark such as parameters, measurements and metrics. We have also developed a ROS (Robot Operating System)-based testbed with the goal of making it easy for users to validate, benchmark, and compare different algorithms including coordination strategies. Our testbed uses the MORSE (Modular OpenRobots Simulation Engine) simulator for realistic simulation and a computer cluster for decentralized computation. In this paper, we present our testbed in details with the architecture and infrastructure, the issues encountered in implementing the infrastructure, and the automation of the deployment. We also report a series of experiments on multi-robot exploration, in order to demonstrate the capabilities of our testbed.
... However, the resultant algorithms are often either so theoretic or really applicable just on a few set of controlled environments. On the contrary, the team is forced to be close enough in order to be fully communicated all time (for instance, not spreading further than the limits of communication ranges) [24], [14], [27]. In real scenarios many things may put the ideal working assumption at risk. ...
In the present document, the authors introduce the Cooperative Exploration problem as well as the most
relevant approaches in order to show the most common drawbacks and opportunities to improve the state of art solutions. Subsequently, a preliminary version of a multi-robot exploration proposal is described. The first results obtained in simulated scenarios support the underlying ideas are feasible and promising. They show that is possible to cope with real communication constraints (always present in practice), being more fault tolerant and still having good performance regarding the total exploration time. Next steps to fully implement a more
reliable and robust system are discussed.
... Vazquez y Malcolm en [19] proponen un algoritmo de exploración descentralizado basado en la selección de diferentes comportamientos: evitando obstáculos, manteniendo la conectividad de la red, explorando hacia una frontera. Esta selección toma en cuenta la condición actual de la red, la cual es conocida por cada robot gracias a que periódicamente se intercambian mensajes. ...
This paper proposes a completely decentralized algorithm for multi-robot coordination to explore and map terrain, under constraints on communication range. This algorithm is based on a market bidding process of unrevealed frontiers, where each robot calculates the bids independently. When it reaches its target position, the robot makes a decision by itself, which involves every one of the team members and their ranges of communication, under a decentralized scheme and without the necessity of a central module. Since the approach maintains a more stable communication network among the robots and, consequently, more similar individual maps, the result is an efficient and fault tolerant approach. The bidding function includes the distance of the considered robot towards the frontier, the distances to the others robots and their respective objectives and include the constraint on communication range. This function provides the robots with a complex behavior, so the robot tries to explore distant frontiers, while trying to keep itself within communication range of the other robots.
... On the other hand decentralized algorithms are faster, but lack completeness and optimality. In [20] a decentralized method is proposed which con-siders range constraints. In this study, robots are able to decide whether to keep persistency of the communication with other robots or to avoid obstacles. ...
The present work proposes a multi-robot exploration method for con-junct environments, based on one of the state-of-the-art algorithms. In many exploration missions, after the subject is found, it is beneficial if the discoverer robot returns back to the base station, in order to report, delivery or recharge. In addition, the exploration might need a long time to be finished or has to be done over and over. Returning back to the base station enables robots to get recharged, fixed, or even substituted with other robots. Furthermore, the equilibrium in task allocation to robots is this work's other concern. The presented algorithm also reduces the maximum energy consumption of robots, as a good side effect. The efficiency of the proposed algorithm is demonstrated by providing simulation results for a variety of obstacle densities and different number of robots.
... An interesting control technique used to coordinate mobile nodes is the Behavioral Control Paradigm (Balch and Arkin, 1997). As an example, in (Vazquez and Malcolm, 2004) a behavioral-based control is implemented in order to force robots to perform an exploration task while preserving the group communication. An alternative behavioral control approach has been introduced in (Antonelli et al., 2007), where the robots are controlled to perform a typical MANET task by exploiting the so called Null-Spaced Behavioral control, based on the idea that the main desired behavior to be accomplished by the robots can be decomposed in smaller behavioral subtasks whose priority is defined by each robot depending on many parameters, such as environmental informations, battery charge or detected robots faults. ...
In this paper a novel graph-based approach to the self coordination of robotic Mobile Ad-hoc NETwors (MANETs) to be used in disaster scenarios is presented. In particular, in the proposed approach a leader-follower architecture is exploited in order to control few nodes while ensuring that connectivity is preserved between a base node and an explorer (a robotic explorer or a human rescuer) that is moving in unknown environments. The presented algorithm is supported by means of simulations.
... With anymore increase in number of agents there is a decline in performance. These results are consistent with the existing literature ( [3] and the references therein). Figures 6.1b, 6.1c, 6.2b and 6.2c consider only those tasks which were successfully serviced to compute the average time taken to service a task. ...
... Vazquez és Malcolm [4] egy decentralizált eljárást javasolt, ahol a robotok egyénileg végzik az algoritmusaikat, felelve a terület felfedezéséért a kommunikáció fenntartása és az ütközésfigyelés mellett. ...
In our paper an area exploration method is presented based on the static linear communication network above. The robots are communicated via bluetooth and are organized a communication chain. One of the end of robot chain is fixed during the exploration and the other constraint is that not allowed for the neighbor robots moving away from the range of other. With a simple proof, we have shown that the proposed (fixed chain-like team) exploration method is optimal in the obstacle-free case under the constraint of the connectivity with the base station . It was found that the proposed method performs better than the chosen reference methods in the case of zero or low obstacle density . The method was tested by computer simulations and real Dual NXTs for various obstacle configurations and densities.
... Another future work is to perform a similar study and determine the optimal robotic fleet size with alternative cooperation strategies such as [6,3,8,5,9,2,1]. This result, will enable us to actually compare the strategies. ...
Exploring a terrain using a fleet of mobile robots is a key challenge that can be trans-posed to many real cases scenarios (e.g. mine clearance, search and rescue). Many re-searches have proposed some algorithms making the robots cooperate and optimize the overall exploration time. Given these algorithms, the problem is now to carefully determine the number of robots to use in a fleet. In this paper, we propose to address this question for the well-known frontier-based exploration algorithm proposed by Yamauchi. We report on the results of 6000 discrete simulations where we varied the size of the robot fleet, robots initial positions, as well as the number of obstacles in terrains of the same size.
... A possible reactive approach is to include link quality in the motion [10] or goal generation functions [11]. For example, in exploration, goals may be decided as the result of cost functions that depend on signal quality [12]. This is difficult to carry over to more flexible service missions, since tasks are conditioned by external requirements (in general visiting arbitrary locations), and may be totally unrelated to those generated by the team in order to preserve communication. ...
As field robot teams mature and their missions
become more ambitious, new real world challenges become
more prominent. Problems that were previously not an issue in
small experimental setups, or that were typically left out in sim-
ulation, are attracting increasing interest in order to expand the
applicability of such robotic teams. Communication constraints
in large or cluttered scenarios, where a full infrastructure
network cannot be taken for granted, is such a forefront issue
in current multi-robot field robotic missions like exploration
and rescue. This paper highlights recent results achieved by
our research group from a point of view of task allocation
for multi-robot routing. Our methodologies have in common a
strong bias towards practical application, aiming at easing the
transition between simulation and experimental setups, while
making guarantees on the completeness or competitiveness of
the achieved solutions. The experiments presented in this work
validate this practical approach, and also serve to highlight the
kind of problems that may arise in this experimental area.
... Ulam and Arkin [2] developed a behaviour based scheme for recovering from communication failures during exploration. Vazquez and Malcolm [3] proposed a behaviour based scheme for multi-robot exploration. Hollinger and Singh [4] have come up with a periodic connectivity scheme for cooperative target tracking. ...
In order to complete tasks quickly, multiple robots can be deployed. Some of the tasks are critical and could be dangerous to humans. Hence the tasks must be continuously supervised during execution. For supervision, the human operator and the service robot must, at all times, have a communication link. This paper presents a decentralized task servicing algorithm with base connectivity constraint for multiple homogenous robots to perform the tasks of servicing the assigned task(s). The algorithm is scalable and considers real-world constraints on movement as well as collision avoidance. An example scenario is presented to the performance of the algorithm.
... In reference [13] a group of mobile robots is required to maintain a wireless ad-hoc network by resorting to a distributed algorithm; each robot computes a first-order prediction of the network topology and, according to a proper cost function, estimates the position where the probability to maintain network connectivity is the largest. A behaviourbased approach is used in [15] to encourage a team of robots in maintaining a connected communication network during an exploration mission; the robots that form a bridge connection, i.e., a link whose removal disconnects the network, are forced to implement a connectivity-behaviour task. Reconfiguration aimed at implementing a fault-tolerant biconnected configuration is proposed in [7]. ...
The problem of maintaining a wireless communication link between a fixed base
station and an autonomous agent by means of a team of mobile robots is
addressed in this work. Such problem can be of interest for search and rescue
missions in post disaster scenario where the autonomous agent can be used for
remote monitoring and first hand knowledge of the aftermath, while the mobile
robots can be used to provide the agent the possibility to dynamically send its
collected information to an external base station. To study the problem, a
distributed multi-robot system with wifi communication capabilities has been
developed and used to implement a Mobile Ad-hoc NETwork (MANET) to guarantee
the required multi-hop communication. None of the robots of the team possess
the knowledge of agent's movement, neither they hold a pre-assigned position in
the ad-hoc network but they adapt with respect to the dynamic environmental
situations. This adaptation only requires the robots to have the knowledge of
their position and the possibility to exchange such information with their
one-hop neighbours. Robots' motion is achieved by implementing a behavioural
control, namely the Null-Space based Behavioural control, embedding the
collective mission to achieve the required self-configuration. Validation of
the approach is performed by means of demanding experimental tests involving
five ground mobile robots capable of self localization and dynamic obstacle
avoidance.
... It overcomes the problem of information inconsistency caused by communication delay. In [9] a behaviour-based architecture is designed to guide the robots in a decentralized fashion in order to explore the environment and maintain local short-range communication in a mobile ad-hoc network. In [10] an approach to minimize the overall exploration time and making it possible to localize fire sources in an efficient way was proposed. ...
Exploration of an unknown area is an important topic in multi robot system. Due to its various applications it gains the concern of various researchers. In this paper an approach that is based on circle partitioning method for workload sharing is proposed. Unknown area is partitioned into sub regions equal to the number of available robots, each robot is assigned to different sub region and then the team of mobile robots starts exploration. The aim is to decrease the exploration time. A popular concept for the exploration problem i.e. based on the notion of frontiers has been used. The proposed algorithm has been tested in a set of environments with different level of complexity depending on number of obstacles. This can be used in real world for search and rescue operations where time is a major constraint.
... In addition, the network and robots must have capabilities to exchange the location of the point and to re-construct a new topology that can reconnected to the other partitioned network. [17] discusses an efficient algorithm to explore frontier areas by robots forming an adhoc network. In this proposal, robots are trying to avoid their network range conflict and spread to the frontier. ...
When a disaster happens in an area where people live in and there are victims at there, a rescue team is organized and sent to save the victims. Traditionally, the rescue parties run a risk of their own lives to save them. We believe that recent progress of robotics technologies and networking technologies can help the situation. We propose the idea of the autonomous network construction system and the remote investigation system of the disaster area by robots. In this paper, we propose a new research area for the dynamically extended and autonomously maintained network by robots. We define the disaster situation assumed in this area and state the requirements to realize the solution for the new kind of network.
... Other approaches rely on assumptions about the signal decay function [27] or the line-of-sight view [16,14,2]. However, it is known [7] that such models can badly misrepresent the real behavior of the signal. ...
The Multirobot Task Allocation (MRTA) paradigm is widely used in multirobot cooperation schemes, e.g. for observation, surveillance and tracking missions. Market-based approaches have yielded effective distributed solutions for such missions, showing the ability to manage heterogeneous, dynamic and robust robot teams. Two major challenges remain however poorly tackled: the management of inter-robot and inter-task communication constraints, and the use of a rich task formalism to model complex missions. This paper presents our investigations to treat these two aspects. The inter-robot and inter-task communication constraints are explicitly handled in the task allocation process, through simple geometric models and thanks to temporal scheduling skills. Rich tasks are allocated using a treebased task formalism that allows to treat complex missions with task ordering. Current work has shown it to be able to handle more complex tasks and to give better solution than MRTA systems working on simple task structures. In our work we will try to go further in this investigation.
... On the other hand, when the robots do not know their relative locations, then it is not obvious how to coordinate them effectively, since the robots do not share a common frame of reference or common map. Large number of the published works in multirobot exploration field is based on frontier cells e.g.567891011121314151617181920212223. A frontier cell is any free cell (not occluded) that has at least one explored neighboring cells. ...
In this paper a new exploration algorithm using two cooperating robots is introduced. The new technique is a combination of wall-following exploration algorithm and frontier-based exploration algorithm. Furthermore, robots sweep the line-of-sight between them continuously; if they can see each other then the area between them is assigned as free. The aim is to decrease the exploration time and energy consumption. The proposed algorithm is divided into two stages: Firstly, one of the robots follows (detects) the entire of the environment walls. And secondly, they employ frontier-based algorithm to complete exploring the remained unexplored areas. During these two stages, the robots sweep the line-of-sight between them in each step to maximize the exploration efficiency.
... [1] Vazquez, J. and C. Malcolm proposed a behavior-based exploration with multi-robot system. In this technique the exploration is guided in a distributed fashion to keep the local shortrange communication in the mobile network. ...
In this paper, a frontier-based technique is used with two cooperating mobile robots to explore unknown environment. The aim is to decrease the exploration time. The proposed frontier-based exploration algorithm is based on a new bidding function in which we introduced a special parameter to decrease the overlap between the robots in addition to the utility and cost parameters. Tens of thousands experiments have been conducted to see the relative importance of the weight parameters used in this technique. As a result of these experiments, the weight values can be chosen to guarantee short exploration time. The proposed algorithm has been tested with a set of environments with different shapes and different numbers of obstacles. Finally, the results of our algorithm were compared with the results of one of known exploration algorithms available in the literature. The new technique led to promising results.
This paper addresses the problem of exploring an unknown environment using multiple robots and maintaining the communication among the team of robots. The existing exploration methods assume that the communication is maintained among the team of robots throughout the exploration process. However, this is not possible when the area to be explored is very large and when there is limited communication range. The presented exploration methods provide communication maintenance among the team of robots and maintain coordination. Three different approaches are presented for exploration. They are centralized approach, leader-follower approach, and ad hoc approach. The concept of centralized approach is developed where the base station is connected to all the robots in the team. The concept of leader-follower approach is developed, where among the team of robots, a leader will be selected. The concept of ad hoc approach is developed in which even though the robots are not within the communication range of one another, communication will occur through ad hoc network. The performance evaluation is done for the above three approaches based on the path length, exploration time, and total number of cycles required to explore the complete area. Systematic approach is used to examine the effect of influence of the exploration parameters like the number of robots, communication range of a robot, and sensor range of a robot on the performance metrics. It is investigated that when there is increase in the exploration parameters, then the exploration time is reduced.
In this paper, we consider nonsubsampled graph filter banks (NSGFBs) to process data on a graph in a distributed manner. Given an analysis filter bank with small bandwidth, we propose algebraic and optimization methods of constructing synthesis filter banks such that the corresponding NSGFBs provide a perfect signal reconstruction in the noiseless setting. Moreover, we prove that the proposed NSGFBs can control the resonance effect in the presence of bounded noise and they can limit the influence of shot noise primarily to a small neighborhood of its location on the graph. For an NSGFB on a graph of large size, a distributed implementation has a significant advantage, since data processing and communication demands for the agent at each vertex depend mainly on its neighboring topology. In this paper, we propose an iterative distributed algorithm to implement the proposed NSGFBs. Based on NSGFBs, we also develop a distributed denoising technique which is demonstrated to have satisfactory performance on noise suppression.
Multi-agent exploration of unknown environments with limited communication is a rapidly emerging area of research with applications including surveying and robotic rescue. Quantifying different approaches is tricky, with different schemes favouring one parameter of the exploration, such as the total time of exploring 90 % of the environment, at the expense of another parameter, like the rate of information update at a base station. In this paper we present a novel approach to this problem, in which agents choose their actions based on the time preference of the base station for information, which it encodes as the desired minimum ratio of base station utility to total agent utility. We then show that our approach performs competitively with existing exploration algorithms while offering additional flexibility, and holds the promise for much improvement regarding incorporation of various information preferences for the base station.
Autonomous robots working in a shared environment require new coordination capabilities in order to achieve enhanced efficiency: planning to decide what is the best way to tackle a problem and task allocation to decide which robot will perform each workload. The latter, which is the main focus of this thesis, has been addressed by many different means: from implicit coordination without communications (cooperation as an emergent property of predefined algorithms), through biologically inspired systems (swarms), mathematical models (e.g. Markovian ones), to human-mimicking explicit communication (contracts, auctions).
Task allocation solutions often require explicit communication. This communication is commonly carried out by means of Wi-Fi transmitters, since this is a well established technology that does not impair robots’ mobility. However, Wi-Fi
coverage may be limited, for instance due to economic or infrastructure reasons (unprepared, destroyed, or too large areas). This prompts the use of MANETs and explicit management of multi-hop messaging and network route preservation.
While there is a respectable amount of work on task allocation, there is still the need for research towards the integration of problems that are typically treated in an isolated way. Furthermore, network integrity preservation is a growing concern in mobile robotics and as such it is receiving increasing attention. For the outlined
reasons, this thesis provides novel research on the following subjects:
* Problem-independent generic allocation methods for loosely coupled or uncoupled tasks (e.g. with few or no temporal restrictions), suitable for a variety of service missions. In particular, several auction based methods are
explored, and their use in combination with hierarchical task networks or limited communication environments is studied.
* Well-defined optimization objectives and metrics that allow a fair evaluation and comparison of these task allocation methods within the context of service robotics. Starting from the classic traveling salesman model, several metrics and their effects in the optimization are identified. Methods for the adaptability of algorithms to interchangeably use any of these metrics are proposed.
* Algorithms for the integration of these results and its application to constrained networking environments. Two approaches are studied: one considering networking as an additional restriction in the optimization process, and
another treating it as a fundamental aspect which determines the methodology used for assignation.
* Experimental demonstrations of simulation results. These experiments address two related points of increasing relevance in complex systems such as a multi-robot team: on the one hand, experimental validation is a necessary step in a whole research plan; on the other, by publishing source code (SANCTA architecture) of our robots, the task of verifying and replicating research is simplified for other researchers. Furthermore, this enables code reuse by the robotic community with the corresponding implementation time savings.
* Implementation, integration and deployment, as leaders of the work package devoted to task allocation, of the pertinent algorithms within the context of the European project URUS. This project focuses on the introduction of robots and sensors in urban settings, in order to enable tasks like people and goods transportation, tracking of users, triggering of safety alerts and subsequent robotic intervention, for instance.
By tackling all these issues as a whole, this thesis is posed to contribute to the furthering of the field in its goal of building versatile robotic teams that can be quickly and easily tailored for the particular objectives of a mission, performing its tasks with a high degree of autonomy and relying on humans only for supervision and customization of high level directives. It can be said that such teams are one of the “holy grails” of service robotics because of its impact in many contexts.
Robot area exploration is a very important task in robotics because it has many applications in real-life problem. So, this is always a very interesting field for researches. This paper presents a new method for multi-robot area exploration. Here, first the environment is divided into partition. In each partition, the robot is deployed randomly. Each partition is explored separately by robot. For the movement of robots, well-known particle swarm optimization algorithm is used. Here mainly concentrated on the multi-robot-coordinated exploration for unknown search spaces where decisions made by bio-inspired algorithms for movement and thus helping in exploration. For better and fast exploration, robot should be scattered in different directions; for this purpose, new clustering-based distribution method is used. The proposed method is tested on different simulated environments that are considered as indoor and outdoor environments. Different parameters such as move, coverage, energy, and time are calculated. The results show that method works well for both environments.
A multi-robot system can be highly beneficial for exploration, which is a core robotics task. We know that this exploration task is best performed when using a multi-robot system. The optimal solution strategies of such a task are mostly determined by the conditions imposed by the environment and the abilities of the robot team. We present an algorithm for multi-robot exploration of an unidentified environment, placing into account the communication constraints between the robots. A novel communication scheme of an autonomous robot team via Bluetooth radio is investigated. In the presented solution, an autonomous unit is equipped with two independent Bluetooth radios and so a relatively fast communication is possible in the team in a static (i.e. no ad-hoc) networking topology. An autonomous robot is a machine able to extract information from its environment and use knowledge about its world to move safely in a meaningful and purposive manner. The performance of such a network was tested by implementing a linear graph topology by NXT robots. It was established that the reliability and the speed of such a communication scheme are satisfactory and give rise to applications in a robot team manage task. In the second part of the paper an area exploration method is presented based on the static linear communication system above. The method was tested by computer simulations for various obstacle configurations and density. It was establish that the proposed method performs better than the chosen reference methods in the case of zero or low obstacle density and when high (75% or 100%) exploration ratio is necessary. With a simple verification, we have shown that the proposed (fixed chain-like team) exploration method is optimal in the obstacle-free case under the constraint of the connectivity with the base station.
this paper addresses the problem of coordinating multiple robotic pursuers in tracking and catching an adversarial evader in a dynamic environment. We assume that the adversarial evader can be detected independently by one pursuer but two pursuers are needed for a successful capture. We aim to reduce the capture time of the evader. Therefore, we model the motion of the evader by the probabilistic method and incorporate the model into directing the motion of the pursuers. In addition, we keep the pursuer communicating with at least another pursuer so that the evader found can be known immediately by another pursuer and then a quick capture can be produced by these two pursuers. By combining the two issues above, the evader can be detected and captured as quickly as possible. Finally, we present the simulation results to demonstrate the performance of our algorithm in an indoor environment. The results show that our method can greatly reduce the capture time of the evader.
Neighbor-to-Neighbor (Inter-Module) communi-cation between modules using methods such as infrared, radio and bus-type connection are critical component for global behavior coordination. However, this type of communication method does not necessarily provide the shortest possible routes between modules and not making use of Hidden Communication Shortcuts (HCS). A HCS is defined as a communication channel between two modules without involving any inter-module (non-HCS) communication. For example, instead of sending messages through all modules in a snake configuration, the head module can directly talk to the tail module through infra-red if they are physically close. This paper proposes a novel way to discover existing communication shortcuts distributedly and form a near-optimal network using these shortcuts to connect a set of given terminal modules based on inter-module communication. The size of the discovered network is at most two times larger than the optimal (minimal) ones. To make this approach more realistic, we assume that not all communication devices on the modules are turned on simultaneously and in fact no more than r modules are allowed to turn on devices for non inter-module communication at the same time to avoid interferences. Derived from a centralized network deployment approach of mobile robot network -ANCHOR [7], this paper provides (1) a distributed algorithm for HCS discovery among mod-ular robots (2) with adaptation to heterogeneity as long as the modular robots are connected at least with neighbor-to-neighbor communication (3) a distributed approach to form a near-optimal network with at most two times the minimum number of modules (2-OPT) to activate their devices for HCS. We show that the convergence time on a balanced configuration is of a factor t, O(n 2 t), where n and t are the number of modules and terminal modules respectively, but it remains O(n 2) for the total number of activation and deactivation of devices for HCS. In simulation, we also show the improvement of convergence time as r increases.
Multi-robot system provides more advantages over a single robot. In certain situations, robots need to maintain global connectivity while proceeding tasks such as traveling some interested spots in an area. This paper formulates the Multi-Robot Traveling Problem Constrained by Connectivity, and proposes a Connected Nearest Neighbor solution aiming to minimize the total traveling distance of the robots, which performs nearly twice better than previous work. Additionally, it is load balancing, fast in response, and robust to environmental dynamics and robot failures. Further improvements of the solution are also discussed and developed. Simulations are designed to investigate the cost of maintaining connectivity, the influence of different environments, and the comparison among the algorithms.
Area exploration is the key behind many researches in robotics. Numerous exploration problems have been solved based on the concept of frontiers that can be defined as the boundary between the explored and unexplored cell. In this paper we considered the problem of energy efficient exploration with a team of robots. An approach has been proposed that chooses the next frontier based on the direction strategy which simultaneously takes into account the location of other robot as well. Whenever a frontier has to be assigned to a specific robot, the utility of the unexplored area visible from this position is increased so that at a time not more than single robot moves to the same cell. Based on the direction penalty is calculated for each target points. Then the frontier having minimum utility and penalty has been chosen as the next target point. The robot moves to that frontier cell using energy efficient A* algorithm. The energy efficient A* gives optimal results taking into account energy consumed for stops and turns. Java based platform is used to run the simulation. Proposed algorithm has been tested on various test maps. The result shows that our technique accomplishes the mission quickly as compared to single robot energy efficient exploration and effectively distributes the robots over the environment.
In multi-robot systems, it is very important to maintain the network connectivity for cooperative behavior between robots. This paper describes a new method for deployment of wireless relay nodes. When applying rescue robots in building or underground city, connecting communication range is attenuated. In the paper, we divide multi-robot by leader robot, source robot, and relay robot. Using these robots, we propose communication control algorithm for connecting network communication.
There are various types of tasks performed by the network centric collective robot (NCCR) such as synchronous behavior control signaling, map sharing, and basic monitoring the situation. Since these tasks require a massive and various communication loads, the task efficiency of the NCCR may be greatly affected. Also, if the formation control used as the method of robot communication based on distributed control system, the task may limit the effectiveness of the robot operations. In this paper, we propose a method of formation control to improve the efficiency of the robot tasks because robot has a variety of communication-range and handling-load capacity.
Mobile surveillance and sensing systems need a networking infrastructure that enables the mobile systems to transmit information gathered to a base station. We consider the problem of an efficient use of mobile robots to sense not only the region but also deploy relays to build the networking infrastructure. To develop an efficient solution to the above problem, we first present a problem called precedence constrained two traveling salesman (PC2TSP). We propose a near-optimal heuristic to PC2TSP to generate tours by clustering points, generating optimal single-traveler tours, and tour pruning and balance. By modeling in part by PC2TSP, we then solve the problem of minimum time two-robot real-time search with online relay deployment. We call the solution STAtic Relay aided Search (STARS), which identifies visiting positions, assigns the precedence constraint, and finally generates tours by PC2TSP. STARS enables solutions for remote robotic sensing and control. In addition, STARS substantially reduces cost compared to a homogeneous mobile robot system and enables constant monitoring of suspicious areas. STARS and our solution to PC2TSP are extensible to deal with more than two travelers. Extensive simulations show that our solution to PC2TSP achieves near-optimal performance with less than 2 percent average difference from optimal.
This paper presents a multi-robot exploration approach for application in wireless environments. The challenges generally faced by a robot team are to maintain network connectivity among themselves, in order to have an accurate map of the environment at each instant and have an efficient navigation plan for moving toward the unexplored area. To address these issues, we focus on the integration of such connectivity constraints and take navigation plan problems into account. A modified A* based algorithm is proposed for planning the navigation of the robots. A communication protocol based on the concept of leader-follower is developed for maintaining network connectivity. Mobile robots typically use a wireless connection to communicate with the other team members and establishes a Mobile Ad Hoc NETwork among themselves. A communication route is established between each robot pair for exchanging local map data, in order to achieve consistent global map of the environment at each instant. If the routes have multiple hops, this raises the problem of message delaying because time delay accumulates per hop traveled. The purpose of the proposed Leader Follower Interaction Protocol is to reduce the total number of hop counts required for all transmissions between robot pairs. This is different from the centralized approach where the leader is a fixed base station. The role of leader in the proposed approach switches from one robot to others as network’s wireless topology changes as robots move. Simulation results show the effectiveness of communication protocol, as well as the navigation mechanism.
A common assumption made in multi-robot research is the connectedness of the underlying network. Although this seems a valid assumption for static networks, it is not realistic for mobile robotic networks, where communication between robots usually is distance dependent. Motivated by this fact, we explicitly consider the communication limitations. This paper extends the LFIP based exploration framework previously developed by Pal et al. (Cogn. Comput. doi:10.1007/s12559-012-9142-7, 2012), to address the Multi-Agent Territory Exploration (MATE-n
k
) task under severe communication constraints. In MATE-n
k
task agents have to explore their environment to find and visit n checkpoints, which only count as “visited” when k agents are present at the same time. In its simplest form, the architecture consists of two layers: an “Exploration layer” consisting of a selection of future locations for the team for further exploring the environment, and “Exploration and CheckpointVisit layer”, consisting of visiting the detected checkpoints while continuing the exploration task. The connectivity maintenance objective is achieved via two ways: (1) The first layer employs a leader-follower concept, where a communication zone is constructed by the leader using a distance transforms method, and (2) In the second layer we make use of a graph theory for characterizing the communication, which employs the adjacency and Laplacian matrices of the graph and their spectral properties. The proposed approach has been implemented and evaluated in several simulated environments and with varying team sizes and communication ranges. Throughout the paper, our conclusions are corroborated by the results from extensive simulations.
this paper addresses the problem of coordinating multiple robotic pursuers in tracking and catching an adversarial evader in a dynamic environment. We assume that the adversarial evader can be detected independently by one pursuer but two pursuers are needed for a successful capture. We aim to reduce the capture time of the evader. Therefore, we model the motion of the evader by the probabilistic method and incorporate the model into directing the motion of the pursuers. In addition, we keep the pursuer communicating with at least another pursuer so that the evader found can be known immediately by another pursuer and then a quick capture can be produced by these two pursuers. By combining the two issues above, the evader can be detected and captured as quickly as possible. Finally, we present the simulation results to demonstrate the performance of our algorithm in an indoor environment. The results show that our method can greatly reduce the capture time of the evader.
Our research addresses how to integrate exploration and
localization for mobile robots. A robot exploring and mapping an unknown
environment needs to know its own location, but it may need a map in
order to determine that location. In order to solve this problem, we
have developed ARIEL, a mobile robot system that combines frontier based
exploration with continuous localization. ARIEL explores by navigating
to frontiers, regions on the boundary between unexplored space and space
that is known to be open. ARIEL finds these regions in the occupancy
grid map that it builds as it explores the world. ARIEL localizes by
matching its recent perceptions with the information stored in the
occupancy grid. We have implemented ARIEL on a real mobile robot and
tested ARIEL in a real-world office environment. We present quantitative
results that demonstrate that ARIEL can localize accurately while
exploring, and thereby build accurate maps of its environment
Addresses the problem of coordinating multiple mobile robots in a tightly coupled task by means of implicit communication. This approach allows the development of controllers that do not depend on any explicit data flow between the robots, thus relying only on local sensor information. A box-carrying task is used to validate the proposed methodology both in simulation and in real-world experiments. Results show that implicit communication can be used together or replacing explicit communication for the cooperative box carrying task, making the system more robust to faulty communication environments.
Concerns the development of a framework and algorithms for a distributed heterogeneous robot team. Team members exchange sensor information, collaborate to track and identify targets, or even assist each other to scale obstacles. As for sensing, by coordinating its members a team can exploit information derived from multiple disparate viewpoints. A single robot, even though equipped with a large array of different sensing modalities, is limited at any one time to a single viewpoint, but a team of robots can simultaneously collect information from multiple locations. This article describes the design and construction of a team of 7 × 7 × 7-cm robots called "millibots". We show how the team can exploit collaboration to perform missions such as mapping, exploration, surveillance, and eventually support rescue operations.
Exploration is a central issue for autonomous agents which must carry out navigation tasks in environments whose description is not known a priori. In our approach the environment is described, from a symbolic point of view, by means of a graph; clustering techniques allow for further levels of abstraction to be defined, leading to a multi-layered representation. In this work we propose an unsupervised exploration algorithm in which several agents cooperate to acquire knowledge of the environment at the different abstraction levels; a broadcast model is adopted for inter-agent communication. All agents are structurally equal and pursue the same local exploration strategy; nevertheless, the existence of multiple levels of abstraction in the environment representation allows for the agents' behaviours to differentiate. Agents carry out exploration at different abstraction levels, aimed at reproducing an ideal exploration profile; each agent selects dynamically its exploration level, bas...
1 Introduction and Overview.- 2 Configuration Space of a Rigid Object.- 3 Obstacles in Configuration Space.- 4 Roadmap Methods.- 5 Exact Cell Decomposition.- 6 Approximate Cell Decomposition.- 7 Potential Field Methods.- 8 Multiple Moving Objects.- 9 Kinematic Constraints.- 10 Dealing with Uncertainty.- 11 Movable Objects.- Prospects.- Appendix A Basic Mathematics.- Appendix B Computational Complexity.- Appendix C Graph Searching.- Appendix D Sweep-Line Algorithm.- References.
Frontier-based exploration directs mobile robots to regions on the boundary between unexplored space and space that is known to be open. Previously, we have demonstrated that frontier-based exploration can be used to map indoor environments where walls and obstacles may be in arbitrary orientations. In this paper, we show how frontier-based exploration can be extended to multiple robots. In our approach, robots share perceptual information, but maintain separate global maps, and make independent decisions about where to explore. This approach enables robots to make use of information from other robots to explore more effectively, but it also allows the team to be robust to the loss of individual robots. We have implemented our multirobot exploration system on real robots, and we demonstrate that they can explore and map office environments as a team.
This paper introduces Robomote, a robotic solution developed to
explore problems in large-scale distributed robotics and sensor
networks. The design explicitly aims at enabling research in sensor
networking, adhoc networking, massively distributed robotics, and
extended longevity. The platform must meet many demanding criteria not
limited to but including: miniature size, low power, low cost, simple
fabrication, and a sensor/actuator suite that facilitates navigation and
localization. We argue that a robot test bed such as Robomote is
necessary for practical research with large networks of mobile robots.
Further, we present a preliminary analysis of Robomotes' success to this
end
Much of the focus of the research effort in path planning for mobile robots has centred on the problem of finding a path from a start location to a goal location, while minimising one or more parameters such as length of path, energy consumption or journey time. Most of the experimental results reported in the literature have centred on simulation results. Only a small subset of the reported results have been implemented on real autonomous mobile robots. It is the goal of our research program to develop path planning algorithms whose correctness and robustness can be tested and verified by implementation on our experimental, self-contained and autonomous mobile robot - the Yamabico [Yuta et.al. 91]. 1. Introduction Much of the focus of the research effort in path planning for mobile robots has centred on the problem of finding a path from a start location to a goal location, while minimising one or more parameters such as length of path, energy consumption or journey time. Most of th...
This paper describes an algorithm for deploying the members of a mobile robot team into an unknown environment. The algorithm deploys robots one-at-a-time, with each robot making use of information gathered by the previous robots to determine the next deployment location. The deployment pattern is designed to maximize the area covered by the robots' sensors, while simultaneously ensuring that the robots maintain line-of-sight contact with one another This paper describes the basic algorithm and presents results obtained from a series of experiments conducted using both real and simulated robots.
This paper reports on an investigation into how a team of robotic agents can self-organize for the exploration of a building, subject to the constraint of maintaining line-of-sight communications. Three different behavioral strategies (anchored wander, quadrant-biased anchored wander, and informed exploration) have been developed and tested in simulation. The results are demonstrated within the context of the MissionLab multiagent mission specification system on two different scenarios.
A technique for coordinating the paths of multiple robots in the
presence of obstacles is presented. To accomplish this, the robots are
prioritized. A path that avoids only the stationary obstacles is planned
for the highest-priority robot. A trajectory for the next-lowest
priority robot is planned so that it avoids both the stationary
obstacles and the higher-priority robot, which is treated as a moving
obstacle. This process is continued until trajectories for all of the
robots have been planned. The planning is accomplished by first mapping
the real space of the robots into configuration-space-time. Potential
fields are applied around the c-space-time obstacles and are used to
modify the path of the robot. The advantage of using artificial
potential fields is that they offer a relatively fast and efficient way
to solve for safe trajectories around both stationary and moving
obstacles. In the method used to perform path planning, a trial path
through the c-span-time is chosen and then modified under the influence
of the potential fields until an appropriate path is found
Topological maps provide a useful abstraction for robotic navigation and planning. Although stochastic maps can theoretically be learned using the Baum-Welch algorithm, without strong prior constraint on the structure of the model it is slow to converge, requires a great deal of data, and is often stuck in local minima. In this paper, we consider a special case of hidden Markov models for robot-navigation environments, in which states are associated with points in a metric configuration space. We assume that the robot has some odometric ability to measure relative transformations between its configurations. Such odometry is typically not precise enough to suffice for building a global map, but it does give valuable local information about relations between adjacent states. We present an extension of the Baum-Welch algorithm that takes advantage of this local odometric information, yielding faster convergence to better solutions with less data. 1 Introduction Hidde...
AbstractThis paper describes an algorithm for deploying a mobile sensor network. A mobile sensor network is made up of a distributed collection of nodes, each of which has sensing, computation, communication and locomotion capabilities. In this paper, we describe an incremental deployment algorithm in which nodes are deployed one-at-a-time into an unknown environment. Each node makes use of information gathered by previously deployed nodes to determine its optimal deployment location. The algorithm is designed to maximize network coverage whilst ensuring that nodes retain line-of-sight with one another (this latter constraint arises from the need to localize the nodes: in our previous work on mesh-based localization [9], [10] we have shown how nodes can localize themselves in a completely unknown environment by using other nodes as landmarks). In this paper, we describe a series of experiments (conducted in both simulation and reality) aimed at validating the algorithm and illuminating its empirical properties.
Much of the focus of the research effort in path planning for mobile robots has centred on the problem of finding a path from a start location to a goal location, while minimising one or more parameters such as length of path, energy consumption or journey time. Most of the experimental results reported in the literature have centred on simulation results. Only a small subset of the reported results have been implemented on real autonomous mobile robots. It is the goal of our research program to develop path planning algorithms whose correctness and robustness can be tested and verified by implementation on our experimental, self-contained and autonomous mobile robot - the Yamabico [Yuta et.al. 91]. 1. Introduction Much of the focus of the research effort in path planning for mobile robots has centred on the problem of finding a path from a start location to a goal location, while minimising one or more parameters such as length of path, energy consumption or journey time. Most of th...
Leaming Topological Maps with Weak Local Odomeuic InformationMobile Robot Exploratioo and Map-Building with Continuous LocalizationFrontier-based explaration using multiple robafs
- I Shaikay
- Kaelbling Schultz
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Cowdination fa Multi-Robot Exploration and MappingRobomole: a tiny mobile robot platform for large-scale ad-hoc sensor networks
- Reid G Simmons
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Multi-robot exploration with limited-range communication
- S Pimentel
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Webots: a powerful realistic mobile robots simulator
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