Results 1  10
of
23
Dynamic assignment in distributed motion planning with local information
 IEEE TRANSACTIONS ON ROBOTICS
, 2008
"... Distributed motion planning of multiple agents raises fundamental and novel problems in control theory and robotics. In particular, in applications such as coverage by mobile sensor networks or multiple target tracking, a great new challenge is the development of motion planning algorithms that dyna ..."
Abstract

Cited by 39 (4 self)
 Add to MetaCart
(Show Context)
Distributed motion planning of multiple agents raises fundamental and novel problems in control theory and robotics. In particular, in applications such as coverage by mobile sensor networks or multiple target tracking, a great new challenge is the development of motion planning algorithms that dynamically assign targets or destinations to multiple homogeneous agents, not relying on any aprioriassignment of agents to destinations. In this paper, we address this challenge using two novel ideas. First, distributed multidestination potential fields are developed that are able to drive every agent to any available destination. Second, nearest neighbor coordination protocols are developed ensuring that distinct agents are assigned to distinct destinations. Integration of the overall system results in a distributed, multiagent, hybrid system for which we show that the mutual exclusion property of the final assignment is guaranteed for almost all initial conditions. Furthermore, we show that our dynamic assignment algorithm will converge after exploring at most a polynomial number of assignments, dramatically reducing the combinatorial nature of purely discrete assignment problems. Our scalable approach is illustrated with nontrivial computer simulations.
Distributed intelligence: Overview of the field and its application in multirobot systems
 Journal of Physical Agents
, 2008
"... Abstract—This article overviews the concepts of distributed intelligence, outlining the motivations for studying this field of research. First, common systems of distributed intelligence are classified based upon the types of interactions exhibited, since the type of interaction has relevance to the ..."
Abstract

Cited by 37 (1 self)
 Add to MetaCart
(Show Context)
Abstract—This article overviews the concepts of distributed intelligence, outlining the motivations for studying this field of research. First, common systems of distributed intelligence are classified based upon the types of interactions exhibited, since the type of interaction has relevance to the solution paradigm to be used. We outline three common paradigms for distributed intelligence — the bioinspired paradigm, the organizational and social paradigm, and the knowledgebased, ontological paradigm — and give examples of how these paradigms can be used in multirobot systems. We then look at a common problem in multirobot systems — that of task allocation — and show how the solution approach to this problem is very different depending upon the paradigm chosen for abstracting the problem. Our conclusion is that the paradigms are not interchangeable, but rather the selection of the appropriate paradigm is dependent
A Distributed Auction Algorithm for the Assignment Problem
, 2008
"... The assignment problem constitutes one of the fundamental problems in the context of linear programming. Besides its theoretical significance, its frequent appearance in the areas of distributed control and facility allocation, where the problems’ size and the cost for global computation and inform ..."
Abstract

Cited by 27 (0 self)
 Add to MetaCart
The assignment problem constitutes one of the fundamental problems in the context of linear programming. Besides its theoretical significance, its frequent appearance in the areas of distributed control and facility allocation, where the problems’ size and the cost for global computation and information can be highly prohibitive, gives rise to the need for local solutions that dynamically assign distinct agents to distinct tasks, while maximizing the total assignment benefit. In this paper, we consider the linear assignment problem in the context of networked systems, where the main challenge is dealing with the lack of global information due to the limited communication capabilities of the agents. We address this challenge by means of a distributed auction algorithm, where the agents are able to bid for the task to which they wish to be assigned. The desired assignment relies on an appropriate selection of bids that determine the prices of the tasks and render them more or less attractive for the agents to bid for. Up to date pricing information, necessary for accurate bidding, can be obtained in a multihop fashion by means of local communication between adjacent agents. Our algorithm is an extension to the parallel auction algorithm proposed by Bertsekas et al to the case where only local information is available and it is shown to always converge to an assignment that maximizes the total assignment benefit within a linear approximation of the optimal one.
MultiLevel Partitioning and Distribution of the Assignment Problem for LargeScale MultiRobot Task Allocation
"... Abstract — A team of robots can handle failures and dynamic tasks by repeatedly assigning functioning robots to tasks. This paper introduces an algorithm that scales to large numbers of robots and tasks by exploiting both task locality and sparsity. The algorithm mixes both centralized and decentral ..."
Abstract

Cited by 8 (1 self)
 Add to MetaCart
(Show Context)
Abstract — A team of robots can handle failures and dynamic tasks by repeatedly assigning functioning robots to tasks. This paper introduces an algorithm that scales to large numbers of robots and tasks by exploiting both task locality and sparsity. The algorithm mixes both centralized and decentralized approaches at different scales to produce a fast, robust method that is accurate and scalable, and reduces both the global communication and unnecessary repeated computation. We depart from optimization and bipartite matching formulations of the problem, observing instead that an assignment can be computed through coarsening and partitioning operations on the utility matrix. First, a coarse assignment is calculated by evaluating the global utility information and partitioning it into clusters in a problemdomain independent way. Next, the assignment solutions in each partition are refined (either recursively, or via an existing algorithm). This multilevel framework allows the repeated reassignment to execute among interrelated partitions. The results suggest that only a minor sacrifice in solution quality is required for gains in efficiency. The proposed algorithm is validated using extensive simulation experiments and the results show advantages over the traditional optimal assignment algorithms. I.
Distance optimal formation control on graphs with a tight convergence time guarantee
 In IEEE International Conference on Decision and Control
, 2012
"... ar ..."
(Show Context)
Largescale multirobot task allocation via dynamic partitioning and distribution. Autonomous Robots 33(3):291–307
, 2012
"... This paper introduces an approach that scales assignment algorithms to large numbers of robots and tasks. It is especially suitable for dynamic task allocations since both task locality and sparsity can be effectively exploited. We observe that an assignment can be computed through coarsening and ..."
Abstract

Cited by 7 (3 self)
 Add to MetaCart
This paper introduces an approach that scales assignment algorithms to large numbers of robots and tasks. It is especially suitable for dynamic task allocations since both task locality and sparsity can be effectively exploited. We observe that an assignment can be computed through coarsening and partitioning operations on the standard utility matrix via a set of mature partitioning techniques and programs. The algorithm mixes centralized and decentralized approaches dynamically at different scales to produce a fast, robust method that is accurate and scalable, and reduces both the global communication and unnecessary repeated computation. An allocation results by operating on each partition: either the steps are repeated recursively to refine the generalized assignment, or each subproblem may be solved by an existing algorithm. The results suggest that only a minor sacrifice in solution quality is needed for significant gains in efficiency. The algorithm is validated using extensive simulation experiments and the results show advantages over the traditional optimal assignment algorithms.
Goal Assignment and Trajectory Planning for Large Teams of Aerial Robots
"... Abstract—This paper presents a computationally tractable, resolutioncomplete algorithm for generating dynamically feasible trajectories for N interchangeable (identical) aerial robots navigating through cluttered known environments to M goal states. This is achieved by assigning the robots to goal ..."
Abstract

Cited by 6 (3 self)
 Add to MetaCart
(Show Context)
Abstract—This paper presents a computationally tractable, resolutioncomplete algorithm for generating dynamically feasible trajectories for N interchangeable (identical) aerial robots navigating through cluttered known environments to M goal states. This is achieved by assigning the robots to goal states while concurrently planning the trajectories for all robots. The algorithm minimizes the maximum cost over all robot trajectories. The computational complexity of this algorithm is shown to be cubic in the number of robots, substantially better than the expected exponential complexity associated with planning in the joint state space and the assignment of goals to robots. This algorithm can be used to plan motions and goals for tens of aerial robots, each in a 12dimensional state space. Finally, experimental trials are conducted with a team of six quadrotor robots navigating in a constrained threedimensional environment. I.
Multiagent Path Planning and Network Flow
"... Abstract This paper connects multiagent path planning on graphs (roadmaps) to network flow problems, showing that the former can be reduced to the later, therefore enabling the application of combinatorial network flow algorithms, as well as general linear program techniques, to multiagent path pl ..."
Abstract

Cited by 5 (4 self)
 Add to MetaCart
(Show Context)
Abstract This paper connects multiagent path planning on graphs (roadmaps) to network flow problems, showing that the former can be reduced to the later, therefore enabling the application of combinatorial network flow algorithms, as well as general linear program techniques, to multiagent path planning problems on graphs. Exploiting this connection, we show that when the goals are permutation invariant, the problem always has a feasible solution path set with a longest finish time of no more than n+V −1 steps, in which n is the number of agents and V is the number of vertices of the underlying graph. We then give a complete algorithm that finds such a solution in O(nV E) time, with E being the number of edges of the graph. Taking a further step, we study time and distance optimality of the feasible solutions, show that they have a pairwise Pareto optimal structure, and again provide efficient algorithms for optimizing each of these practical objectives. 1
K.: Efficient multirobot motion planning for unlabeled discs in simple polygons
 CoRR
, 2013
"... Abstract. We consider the following motionplanning problem: we are given m unit discs in a simple polygon with n vertices, each at their own start position, and we want to move the discs to a given set of m target positions. Contrary to the standard (labeled) version of the problem, each disc is ..."
Abstract

Cited by 3 (2 self)
 Add to MetaCart
(Show Context)
Abstract. We consider the following motionplanning problem: we are given m unit discs in a simple polygon with n vertices, each at their own start position, and we want to move the discs to a given set of m target positions. Contrary to the standard (labeled) version of the problem, each disc is allowed to be moved to any target position, as long as in the end every target position is occupied. We show that this unlabeled version of the problem can be solved in
Shortest Path Set Induced Vertex Ordering and its Application to Distributed Distance Optimal Formation Path Planning and Control on Graphs
"... Abstract — For the task of moving a group of indistinguishable agents on a connected graph with unit edge lengths into an arbitrary goal formation, it was shown that distance optimal paths can be computed to complete with a tight convergence time guarantee [30], using a fully centralized algorithm. ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
(Show Context)
Abstract — For the task of moving a group of indistinguishable agents on a connected graph with unit edge lengths into an arbitrary goal formation, it was shown that distance optimal paths can be computed to complete with a tight convergence time guarantee [30], using a fully centralized algorithm. In this study, we establish the existence of a more fundamental ordering of the vertices on the underlying graph network, induced by a fixed goal formation. The ordering leads to a simple distributed scheduling algorithm that assures the same convergence time. The vertex ordering also readily extends to more general graphs those with arbitrary integer capacities and edge lengths for which we again provide guarantees on the convergence time until the desired formation is achieved. Simulations, accessible via a web browser,1 confirm our theoretical developments. I.