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Traffic Management Strategies for Multi-Robotic Rigid Payload Transport Systems
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Abstract
In this work, we address traffic management of multiple payload transport systems comprising of non-holonomic robots. We consider loosely coupled rigid robot formations carrying a payload from one place to another. Each payload transport system (PTS) moves in various kinds of environments with obstacles. We ensure each PTS completes its given task by avoiding collisions with other payload systems and obstacles as well. Each PTS has one leader and multiple followers and the followers maintain a desired distance and angle with respect to the leader using a decentralized leader-follower control architecture while moving in the traffic. We showcase, through simulations the time taken by each PTS to traverse its respective trajectory with and without other PTS and obstacles. We show that our strategies help manage the traffic for a large number of PTS moving from one place to another.
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In this work, we present an algorithm for robot replacement to increase the operational time of a multi-robot payload transport system. Our system comprises a group of non-holonomic wheeled mobile robots traversing on a known trajectory. We design a multi-robot system with loosely coupled robots that ensures the system lasts much longer than the battery life of an individual robot. A system level optimization is presented, to decide on the operational state (charging or discharging) of each robot in the system. The charging state implies that the robot is not in a formation and is kept on charge whereas the discharging state implies that the robot is a part of the formation. Robot battery recharge hubs are present along the trajectory. Robots in the formation can be replaced at these hub locations with charged robots using a replacement mechanism. We showcase the efficacy of the proposed scheduling framework through simulations and experiments with real robots.
This paper presents a method for controlling a swarm of quadrotors to perform agile interleaved maneuvers while holding a fixed relative formation, or transitioning between different formations. The method prevents collisions within the swarm, as well as between the quadrotors and static obstacles in the environment. The method is built upon the existing notion of a virtual structure, which serves as a framework with which to plan and execute complex interleaved trajectories, and also gives a simple, intuitive interface for a single human operator to control an arbitrarily large aerial swarm in real time. The virtual structure concept is integrated with differential flatness-based feedback control to give an end-to-end integrated swarm teleoperation system. Collision avoidance is achieved by using multiple layered potential fields. Our method is demonstrated in hardware experiments with groups of 3–5 quadrotors teleoperated by a single human operator, and simulations of 200 quadrotors teleoperated by a single human operator.
Traditionally, motion planning involved navigating one robot from source to goal for accomplishing a task. Now, tasks mostly require movement of a team of robots to the goal site, requiring a chain of robots to reach the desired goal. While numerous efforts are made in the literature for solving the problems of motion planning of a single robot and collective robot navigation in isolation, this paper fuses the two paradigms to let a chain of robot navigate. Further, this paper uses SLAM to first make a static map using a high-end robot, over which the physical low-sensing robots run. Deliberative Planning uses A* algorithm to plan the path. Reactive planning uses the Potential Field Approach to avoid obstacles and stay as close to the initial path planned as possible. These two algorithms are then merged to provide an algorithm that allows the robot to reach its goal via the shortest path possible while avoiding obstacles. The algorithm is further extended to multiple robots so that one robot is followed by the next robot and so on, thus forming a chain. In order to maintain the robots in a chain form, the Elastic Strip model is used. The algorithm proposed successfully executes the above stated when tested on Amigobot robots in an office environment using a map made by the Pioneer LX robot. The proposed algorithm works well for moving a group of robots in a chain in a mapped environment.
An algorithm for robot formation path planning is presented in this paper. Given a map of the working environment, the algorithm finds a path for a formation taking into account possible split of the formation and its consecutive merge. The key part of the solution works on a graph and sequentially employs an extended version of Dijkstra's graph-based algorithm for multiple robots. It is thus deterministic, complete, computationally inexpensive, and finds a solution for a fixed source node to another node in the graph. Moreover, the presented solution is general enough to be incorporated into high-level tasks like cooperative surveillance and it can benefit from state-of-the-art formation motion planning approaches, which can be used for evaluation of edges of an input graph. The performed experimental results demonstrate the behavior of the method in complex environments for formations consisting of tens of robots.
We present a constrained optimization method for multi-robot formation control in dynamic environments, where the robots adjust the parameters of the formation, such as size and three-dimensional orientation, to avoid collisions with static and moving obstacles, and to make progress towards their goal. We describe two variants of the algorithm, one for local motion planning and one for global path planning. The local planner first computes a large obstacle-free convex region in a neighborhood of the robots, embedded in position-time space. Then, the parameters of the formation are optimized therein by solving a constrained optimization, via sequential convex programming. The robots navigate towards the optimized formation with individual controllers that account for their dynamics. The idea is extended to global path planning by sampling convex regions in free position space and connecting them if a transition in formation is possible - computed via the constrained optimization. The path of lowest cost to the goal is then found via graph search. The method applies to ground and aerial vehicles navigating in two- and three-dimensional environments among static and dynamic obstacles, allows for reconfiguration, and is efficient and scalable with the number of robots. In particular, we consider two applications, a team of aerial vehicles navigating in formation, and a small team of mobile manipulators that collaboratively carry an object. The approach is verified in experiments with a team of three mobile manipulators and in simulations with a team of up to sixteen Micro Air Vehicles (quadrotors).
In this paper, we present a novel algorithm for exploring an unknown environment using a team of mobile robots. The suggested algorithm is a grid-based search method that utilizes a triangular pattern which covers an area so that exploring the whole area is guaranteed. The proposed algorithm consists of two stages. In the first stage, all the members of the team make a common triangular grid of which they are located on the vertices. In the second stage, they start exploring the area by moving between vertices of the grid. Furthermore, it is assumed that the communication range of the robots is limited, and the algorithm is based on the information of the nearest neighbours of the robots. Moreover, we apply a new mapping method employed by robots during the search operation. A mathematically rigorous proof of convergence with probability 1 of the algorithm is given. Moreover, our algorithm is implemented and simulated using a simulator of the real robots and environment and also tested via experiments with Adept Pioneer 3DX wheeled mobile robots.
This work presents a path planning algorithm for 3D robot formations based on the standard Fast Marching Square (FM2) path planning method. This method is enlarged in order to apply it to robot formations motion planning. The algorithm is based on a leader-followers scheme, which means that the reference pose for the follower robots is defined by geometric equations that place the goal pose of each follower as a function of the leader’s pose. Besides, the Frenet-Serret frame is used to control the orientation of the formation. The algorithm presented allows the formation to adapt its shape so that the obstacles are avoided. Additionally, an approach to model mobile obstacles in a 3D environment is described. This model modifies the information used by the FM2 algorithm in favor of the robots to be able to avoid obstacles. The shape deformation scheme allows to easily change the behaviour of the formation. Finally, simulations are performed in different scenarios and a quantitative
analysis of the results has been carried out. The tests show that the proposed shape deformation method, in combination with the FM2 path planner, is robust enough to manage autonomous movements through an indoor 3D environment.
This paper describes a robust algorithm for mobile robot formations based on the Voronoi Fast Marching path planning method. This is based on the propagation of a wave throughout the model of the environment, the wave expanding faster as the wave’s distance from obstacles increases. This method provides smooth and safe trajectories and its computational efficiency allows us to maintain a good response time.
The proposed method is based on a local‐minima‐free planner; it is complete and has an O(n) complexity order where n is the number of cells of the map. Simulation results show that the proposed algorithm generates good trajectories.
In this paper, we present a formal approach to reciprocal n-body collision avoidance, where multiple mobile robots need to avoid collisions with each other while moving in a common workspace. In our formulation,
each robot acts fully independently, and does not communicate with other robots. Based on the definition of velocity obstacles
[5], we derive sufficient conditions for collision-free motion by reducing the problem to solving a low-dimensional linear
program. We test our approach on several dense and complex simulation scenarios involving thousands of robots and compute
collision-free actions for all of them in only a few milliseconds. To the best of our knowledge, this method is the first
that can guarantee local collision-free motion for a large number of robots in a cluttered workspace.
Carrying heavy payloads is a challenging task for the modular robot, because its composing modules are relatively tiny and less strong compared with conventional robots. To accomplish this task, we attached passive rollers to the modular robot, and designed a wheeled locomotion gait called tricycleBot. The gait is inspired by paddling motion, and is implemented on the modular robot called SuperBot. Features of this gait are systematically studied and verified through extensive experiments. It is shown that tricycleBot can carry payloads at least 530% of its own weight. It can also be steered remotely to move forward/backward, turn left/right. Capability of tricycleBot demonstrates that the versatility of modular robot can be further expanded to solve very specialized and challenging tasks by using heterogeneous devices.
We have proposed a precise formal definition of a formation and we have presented a Player driver for making formations according to our definition. Our definition of a formation is based on a set of rough mereological predicates which altogether define a geometry of the space. The definition of a formation is independent of a metric on the space and it is invariant under affine transformations. We have examined three methods of formation restoring, based on a reactive (behavioral) model as well as on decoupled way of planning. We have performed simulations in Player/Stage system of planning paths for formations with formation change. The results show the validity of the approach. Further research will be directed at improving the effectiveness of execution by studying divisions into sub-formations and merging sub-formations into formations as well as extending the results to dynamic environments.
This paper presents a reasoning system that enables a group of heterogeneous robots to form coalitions to accomplish a multirobot task using tightly coupled sensor sharing. Our approach, which we call ASyMTRe, maps environmental sensors and perceptual and motor control schemas to the required flow of information through the multirobot system, automatically reconfiguring the connections of schemas within and across robots to synthesize valid and efficient multirobot behaviors for accomplishing a multirobot task. We present the centralized anytime ASyMTRe configuration algorithm, proving that the algorithm is correct, and formally addressing issues of completeness and optimality. We then present a distributed version of ASyMTRe, called ASyMTRe-D, which uses communication to enable distributed coalition formation. We validate the centralized approach by applying the ASyMTRe methodology to two application scenarios: multirobot transportation and multirobot box pushing. We then validate the ASyMTRe-D implementation in the multirobot transportation task, illustrating its fault-tolerance capabilities. The advantages of this new approach are that it: 1) enables robots to synthesize new task solutions using fundamentally different combinations of sensors and effectors for different coalition compositions and 2) provides a general mechanism for sharing sensory information across networked robots
This paper presents a novel multi-robot manipulation algorithm which allows a large number of small robots to move a comparatively large object along a desired trajectory to a goal location. The algorithm does not require an explicit communication network among the robots. Instead, the robots coordinate their actions through sensing the motion of the object itself. It is proven that this implicit information is sufficient to synchronize the forces applied by the robots. A leader robot then steers the forces of the synchronized group to manipulate the object through the desired trajectory to the goal. The paper presents algorithms that are proven to control both translational and rotational motion of the object. Simulations demonstrate the approach for two scenarios with 20 robots transporting a rectangular plank and 1000 robots transporting a piano.
Exploration is a process where the objective is to cover an area that is used for subsequent navigation. It is an important criteria for problem-solving in many unknown search space and is an important aspect of automation and information gathering. Here we have proposed multi-robot area exploration method for unknown search areas. In the proposed work, exploration is mainly guided by combined effect of probabilistic and deterministic movement. It uses Clustering Based Distribution Factor (CBDF) for deterministic movement and nature inspired algorithms (NIA) like Particle swarm optimization (PSO), Bacteria foraging optimization (BFO), and Bat algorithm (BA) for random guidance for exploration. The environment partitioning avoids repeated area coverage, and robots may be allocated to any partition to explore the map in a random manner. Robots move in the direction provided by CBDF and explore the area using nature inspired algorithm. The proposed approaches have been implemented and evaluated in several simulated environments and with varying team sizes and detection ranges. Simulation results show that, on increasing the number of robots and detection range, performance also increases and that best results are achieved for PSO.
In this paper an optimalmethod for distributed collision avoidance among multiple non-holonomic robots is presented in theory and experiments. Non-holonomic optimal reciprocal collision avoidance (NH-ORCA) builds on the concepts introduced in [2], but further guarantees smooth and collision-free motions under non-holonomic constraints. Optimal control inputs and constraints in velocity space are formally derived for the non-holonomic robots. The theoretical results are validated in several collision avoidance experiments with up to fourteen e-puck robots set on collision course. Even in scenarios with very crowded situations, NH-ORCA showed to be collision-free for all times.
This paper presents the optimal path of nonholonomic multi robots with coherent formation in a leader-follower structure in the presence of obstacles using Asexual Reproduction Optimization (ARO). The robots path planning based on potential field method are accomplished and a novel formation controller for mobile robots based on potential field method is proposed. The efficiency of the proposed method is verified through simulation and experimental studies by applying them to control the formation of four e-Pucks robots (low-cost mobile robot platform). Also the proposed method is compared with Simulated Annealing, Improved Harmony Search and Cuckoo Optimization Algorithm methods and the experimental results, higher performance and fast convergence time to the best solution of the ARO demonstrated that this optimization method is appropriate for real time control application.
This paper presents a method for robot motion planning in dynamic environments. It consists of selecting avoidance maneuvers to avoid static and moving obstacles in the velocity space, based on the cur rent positions and velocities of the robot and obstacles. It is a first- order method, since it does not integrate velocities to yield positions as functions of time.
The avoidance maneuvers are generated by selecting robot ve locities outside of the velocity obstacles, which represent the set of robot velocities that would result in a collision with a given obstacle that moves at a given velocity, at some future time. To ensure that the avoidance maneuver is dynamically feasible, the set of avoidance velocities is intersected with the set of admissible velocities, defined by the robot's acceleration constraints. Computing new avoidance maneuvers at regular time intervals accounts for general obstacle trajectories.
The trajectory from start to goal is computed by searching a tree of feasible avoidance maneuvers, computed at discrete time intervals. An exhaustive search of the tree yields near-optimal trajectories that either minimize distance or motion time. A heuristic search of the tree is applicable to on-line planning. The method is demonstrated for point and disk robots among static and moving obstacles, and for an automated vehicle in an intelligent vehicle highway system scenario.
During the last decade, sampling-based path planning algorithms, such as Probabilistic RoadMaps (PRM) and Rapidly-exploring Random Trees (RRT), have been shown to work well in practice and possess theoretical guarantees such as probabilistic completeness. However, little effort has been devoted to the formal analysis of the quality of the solution returned by such algorithms, e.g., as a function of the number of samples. The purpose of this paper is to fill this gap, by rigorously analyzing the asymptotic behavior of the cost of the solution returned by stochastic sampling-based algorithms as the number of samples increases. A number of negative results are provided, characterizing existing algorithms, e.g., showing that, under mild technical conditions, the cost of the solution returned by broadly used sampling-based algorithms converges almost surely to a non-optimal value. The main contribution of the paper is the introduction of new algorithms, namely, PRM* and RRT*, which are provably asymptotically optimal, i.e., such that the cost of the returned solution converges almost surely to the optimum. Moreover, it is shown that the computational complexity of the new algorithms is within a constant factor of that of their probabilistically complete (but not asymptotically optimal) counterparts. The analysis in this paper hinges on novel connections between stochastic sampling-based path planning algorithms and the theory of random geometric graphs.
In this paper, we propose a decentralized control system for transporting a single object by multiple non-holonomic mobile robots. Each agent used in the proposed system has two arms, which can steer around a joint offset from the centre point between two driving wheels. One of these mobile robots acts as a leader, who is assumed to be able to plan and to manipulate the omnidirectional motion of the object by using a resolved velocity control. Other robots, referred to as followers, cooperatively transport the object by keeping a constant relative position with the object using a simple PI control. Different from conventional leader–follower type systems that transport an object by multiple robots in coordination, the present followers can plan an action based on their local coordinate and need no absolute positional information. In addition, as a special case, a system consisting of only two robots is introduced, in which the follower robot not only has an arm length controller to follow the leader but also has a fuzzy controller as an avoidance controller to avoid obstacles or a posture controller to keep a desired posture of the object. Simulation results are given to demonstrate the good performance of the proposed systems.
This paper investigates the leader-follower formation control problem for nonholonomic mobile robots based on a bioinspired neurodynamics based approach. The trajectory tracking control for a single nonholonomic mobile robot is extended to the formation control for multiple nonholonomic mobile robots based on the backstepping technique, in which the follower can track its real-time leader by the proposed kinematic controller. An auxiliary angular velocity control law is proposed to guarantee the global asymptotic stability of the followers and to further guarantee the local asymptotic stability of the entire formation. Also a bioinspired neurodynamics based approach is further developed to solve the impractical velocity jumps problem. The rigorous proofs are given by using Lyapunov theory. Simulations are also given to verify the effectiveness of the theoretical results.
In this paper we present an overview of techniques and approaches used for a load transportation system based on small size unmanned helicopters. The focus is on the control approach and on the movement of the rope connecting helicopters and load. The proposed approach is based on two control loops: an outer loop to control the translation of each helicopter in compound and an inner loop to control the orientation of helicopters. The challenge here is that in both loops the dynamics of the whole system - all helicopters and load - should be accounted for. It is shown, that for designing the outer loop controller a complex model of the helicopters and load can be replaced by a simplified model based on interconnected mass points. For designing the inner loop controller, the complete dynamics of the whole system are considered. The usage of force sensors in the ropes is proposed in order to simplify the inner loop controller and to make it robust against variations of system parameters. The presented inner loop controller is independent of the number of coupled helicopters. The outer loop controller depends on the number of helicopters. The problem of oscillations in the flexible ropes due to external disturbancies (e.g. wind gusts) is discussed and a solution based on load state observer is presented. The performance of the presented system was verified in simulations and in real flight experiments with one and three helicopters transporting the load. The worldwide first demonstration of a slung load transportation using three helicopters was performed in December 2007.
This paper presents a motion-planning approach for coordinating multiple mobile robots in moving along specified paths. The robots are required to fulfill formation requirements while meeting velocity/acceleration constraints and avoiding collisions. Coordination is achieved by planning robot velocities along the paths through a velocity-optimization process. An objective function for minimizing formation errors is established and solved by a linear interactive and general optimizer. Motion planning can be further adjusted online to address emergent demands such as avoiding suddenly appearing obstacles. Simulations and experiments are performed on a group of mobile robots to demonstrate the effectiveness of the proposed coordinated motion planning in multirobot formations.
There has been increased research interest in systems composed of multiple autonomous mobile robots exhibiting collective 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 collective 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. 1 Preliminaries There has been much recent activity toward achieving systems of multiple mobile robots engaged in collective behavior. Such systems are of interest for several reasons: (1) tasks may be inherently too complex for a singl...
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