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Current State of the Art in Distributed Autonomous Mobile Robotics
Abstract
As research progresses in distributed robotic systems, more and more aspects of multi-robot systems are being explored. This article surveys the current state of the art in distributed mobile robot systems. Our focus is principally on research that has been demonstrated in physical robot implementations. We have identified eight primary research topics within multi-robot systems -- biological inspirations, communication, architectures, localization/mapping/exploration, object transport and manipulation, motion coordination, reconfigurable robots, and learning - and discuss the current state of research in these areas. As we describe each research area, we identify some key open issues in multi-robot team research. We conclude by identifying several additional open research issues in distributed mobile robotic systems.
... A common application for biologically inspired control is in cooperative multi-robot systems, where rules from biological systems are applied to artificial systems to produce organized interactions with intelligent behavior [12]. Robotics researchers examine the social characteristics of insects and animals and apply their discoveries to the design of multi-robot systems [13]. Barnes et al. [14] used potential fields for group control of AGV. ...
... Swarms [11][12][13][14] Behavior-based [15][16][17][18][19][20] Indoor exploration [21][22][23][24][25][26] Swarm optimization [27][28][29]31] Biological control ...
... Finally, from the data observed in Fig. 6, the following model for success rate (p s ) as a function of radio range (r n ) is proposed. p s (r n , n v ) = 1 − e −β(nv)rn (12) where the coefficient β is a function of the number of vehicles n v β(n v ) = α 1 − 1 e −(nv−n0) (13) and values of α = 0.033 and n 0 = 6.057 were determined using least-squares fitting. Equation (12) can be solved for r n to determine the minimum required radio range (r n,req for a given number of vehicles and a desired success rate (or allowable failure rate)). ...
This paper presents a study of how communication ranges influence the performance of a new decentralized control method for swarms of autonomously navigating ground vehicles that uses a blended leader-follower / artificial potential field approach. While teams of autonomous ground vehicles (AGV) that can navigate autonomously through off-road terrain have a variety of potential uses, it may be difficult to control the team in low-infrastructure environments that lack long-range radio communications capabilities. In this work, we propose a novel decentralized swarm control algorithm that combines the potential-field planning method with the leader-follower control algorithm and biologically-inspired inter-robot interactions to effectively control the navigation of a team of AGV (swarm) through rough terrain using only a single lead vehicle. We use simulated experimentation to demonstrate the robustness of this approach using only point-to-point wireless communication with realistic communication ranges. Furthermore, we analyze the range requirements of the communication network as the number in the swarm increases. We find that wireless communication range must increase as the number of agents in the swarm increases in order to effectively control the swarm. Our analysis showed that mission success decreased by 40% when the communication range was reduced from 100 meters to 200 meters, with the exact reduction also depending on the number of vehicles.
... As quite often happens in nature, in some cases it is beneficial to adopt a strategy which involves several robots working cooperatively to carry out specific tasks. Such an approach offers several advantages which can be summarised as follows (Cao, Fukunaga, & Kahng, 1997;Parker, 2000): ...
... Intuitively, the use of multiple robots to carry out search tasks is expected to yield better results than using a single robot, since the probability of the odour source being found more quickly increases (Cao, Fukunaga, & Kahng, 1997;Parker, 2000). ...
This thesis presents an investigation of odour source localisation using multiple cooperating robots. A cooperative strategy has been implemented for the control of multi-robot teams in order to demonstrate their advantages together with the improvements in carrying out cooperative search tasks.
The odour source localisation experiments that are presented in this thesis are carried out in two types of chemical fields and the results are obtained both via simulation studies as well as via experiments using real mobile robots. The fundamental search strategies are biologically inspired and single robot searches are used to demonstrate for the first time their relative merits under different experimental conditions in order to assess the search performance of these strategies.
The performance of the single robot searches is compared to that of a multi-robot system in non-cooperative multi-robot experiments. It is shown that the search efficiency and robustness are significantly improved even though frequent spatial interactions between the robots hinder the search. The effects of such interactions for the multi-robot system are quantified. Finally, a new cooperation strategy is proposed in order to further improve the search performance. The cooperative multi-robot searching experiments demonstrate that the robots are carrying out the odour source localisation task more efficiently as a result of sharing local information and knowledge concerning the chemical field to achieve a good global cooperation strategy.
... One of the possible ways to overcome this problem is to use multiple robots to map the same environment. The multi-robot approach has some well known advantages in itself [10], [1], most notably robustness. In the rescue framework, multirobot systems are even more appealing because of the possibility to perform a faster exploration of the inspected area, thus increasing the chances to quickly locate victims and hazards. ...
We illustrate our progresses in developing multi-robot systems to be used for mapping in rescue scenarios. The problem we are currently investigating is to combine poor quality multiple maps produced by different robots into a single map to be used by human operators. In particular we motivate our approach and we illustrate the different techniques we implemented and that are at the moment being compared.
... As an immature research area, a part of the work only solves the problem in an ideal environment. The existing plan often strongly depends on absolute positioning [10] or ideal communication conditions [13]. Burgart presents a technique to explore an unknown environment with a team of robots and make an encouraging result, whereas it relies on absolute positioning [14]. ...
With advances in autonomy technology, the use of multi-robot systems is becoming increasingly viable and efficient. In particular, heterogeneous systems are effective in complicated missions that require various capabilities. When the missions are prolonged, certain robots such as quadcopter-typed unmanned aerial vehicles (UAVs) run out of energy and require replenishment. As some robots such as unmanned surface vessels (USVs) have large payload capacities for carrying supplies, the UAVs can receive the necessary resources from the USVs amid the missions. In this paper, we propose a mission planning framework that aims to minimize the total mission duration with consideration of the energy replenishment of robots and the heterogeneity of robots and tasks. The proposed framework consists of four components: task allocation, rendezvous point selection, task planning, and plan combination. For replenishment, the location of the rendezvous must be chosen, and we propose using a data-driven approach to predict the best rendezvous point. Then, the prediction result is used for computing candidate plans of each robot in a distributed fashion, and the best candidates are combined to compute the final plan. To validate the efficacy of the proposed method, simulated experiments of various problem configurations are performed and analyzed.
Modelling the learning dynamic of multi-agent systems has long been a crucial issue for understanding the emergence of collective behavior. In public goods games, agents interact in multiple larger groups. While previous studies have primarily focused on infinite populations that only allow pairwise interactions, we aim to investigate the learning dynamics of agents in a public goods game with higher-order interactions. With a novel use of hypergraphs for encoding higher-order interactions, we develop a formal model (a Fokker-Planck equation) to describe the temporal evolution of the distribution function of Qvalues. Noting that early research focused on replicator models to predict system dynamics failed to accurately capture the impact of hyperdegree in hypergraphs, our model effectively maps its influence. Through experiments, we demonstrate that our theoretical findings are consistent with the agent-based simulation results. We demonstrated that as the number of groups an agent is involved in reaches a certain scale, the learning dynamics of the system evolve to resemble those of a well-mixed population. Furthermore, we demonstrate that our model offers insights into algorithmic parameters, such as the Boltzmann temperature, facilitating parameter tuning
Cooperative transportation of objects by a group of small mobile robots is expected to work in disaster sites. In this study, we aim to transport fragile objects including humans which may move during the transport, with as little burden as possible. We propose the adoption of a flexible tri-axis tactile sensor with thickness at the top of the robot on which the object is mounted for safe support and state monitoring. We improved the leader-follower based control by adding force feedback into the leader’s control law to prevent excessive force on the object, which is the disadvantage of the typical leader-follower method. We verified the robots can transport a rigid body gently with the proposed control law by converging their speeds to the same value in a dynamical simulation and actual experiments. Additionally, we found that multijoint objects also can be transported with the proposed method, whereas the stable support of the object by each robot is reserved for future works.
This paper describes the design and development of an Autonomous Robot Assistant with the Robot Operating System (ROS). The solution incorporates 2D LiDAR with the ROS 2D navigation stack, leading to a low cost, moderate onboard computer. The safety of both property and people is the top priority. Which is being investigated as a viable solution to in-house transportation industry to make it fully automation. The proposed system has the capability to navigate inside the environment by avoiding dynamic obstacles. To examine system stability multiple experiments were conducted. The findings of our experiment reveal that the robot can avoid obstacles in its path or stop in the event by avoiding collision with obstacles.KeywordsAutonomousMobile robotRobot operating system (ROS)NavigationLidarObstacle avoidance
A complete understanding of communication, language, intentionality andrelated mental phenomena will require a theory integrating mechanistic explanationswith ethological phenomena. For the foreseeable future, the complexitiesof natural life in its natural environment will preclude such an understanding.An approach more conducive to carefully controlled experiments and tothe discovery of deep laws of great generality is to study synthetic life forms ina synthetic world to which they have...
August 8-12, 1994, Brighton, England
From Animals to Animats 3 brings together research intended to advance the front tier of an exciting new approach to understanding intelligence. The contributors represent a broad range of interests from artificial intelligence and robotics to ethology and the neurosciences. Unifying these approaches is the notion of "animat"—an artificial animal, either simulated by a computer or embodied in a robot, which must survive and adapt in progressively more challenging environments. The 58 contributions focus particularly on well-defined models, computer simulations, and built robots in order to help characterize and compare various principles and architectures capable of inducing adaptive behavior in real or artificial animals.
Topics Include
Individual and collective behavior • Neural correlates of behavior • Perception and motor control • Motivation and emotion • Action selection and behavioral sequences • Ontogeny, learning, and evolution • Internal world models and cognitive processes • Applied adaptive behavior • Autonomous robots • Heirarchical and parallel organizations • Emergent structures and behaviors • Problem solving and planning • Goal-directed behavior • Neural networks and evolutionary computation • Characterization of environments
Bradford Books imprint
A new concept of robotic systems, "Dynamically Reconfigurable Robotic System(DRRS)" is shown in this paper. Each cell of the robotic module in DRRS can detach itself and combine them autonomously depending on a task, such as manipulators or mobile robots, so that the system can reorganize the optimal total shape, unlike robots developed so far which cannot reorganize automatically by changing the linkage of arms, replacing some links with others or reforming shapes in order to adapt itself to the change of working environments and demands. The newly proposed 'robotic system in this paper can be reconfigurable dynamically to a given task, so that the level of the flexibility and adaptability is much higher than that of the conventionals. DRRS has many unique adavantages, such as optimal shaping under circumstances, fault tolerance, self repairing and others. Some demonstrations can be shown experimentally and a decision method for such cell structured manipulator configurations is also proposed.
ALLIANCE is a software architecture that facilitates the fault tolerant cooperative control of teams of heterogeneous mobile robots performing missions composed of loosely coupled subtasks that may have ordering dependencies. ALLIANCE allows teams of robots, each of which possesses a variety of high-level functions that it can perform during a mission, to individually select appropriate actions throughout the mission based on the requirements of the mission, the activities of other robots, the current environmental conditions, and the robot's own internal states. ALLIANCE is a fully distributed, behaviour-based architecture that incorporates the use of mathematically-modeled motivations (such as impatience and acquiescence) within each robot to achieve adaptive action selection. Since cooperative robotic teams usually work in dynamic and unpredictable environments, this software architecture allows the robot team members to respond robustly, reliably, flexibly, and coherently to unexpected environmental changes and modifications in the robot team that may occur due to mechanical failure, the learning of new skills, or the addition or removal of robots from the team by human intervention. The feasibility of this architecture is demonstrated in an implementation on a team of mobile robots performing a laboratory version of hazardous waste cleanup
There has been increased research interest in systems composed of multiple autonomous mobile robots exhibiting cooperative behavior. Groups of mobile robots are constructed, with an aim to studying such issues as group architecture, resource conflict, origin of cooperation, learning, and geometric problems. As yet, few applications of cooperative robotics have been reported, and supporting theory is still in its formative stages. In this paper, we give a critical survey of existing works and discuss open problems in this field, emphasizing the various theoretical issues that arise in the study of cooperative robotics. We describe the intellectual heritages that have guided early research, as well as possible additions to the set of existing motivations.
