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Fully Decentralized Task Swaps with Optimized Local Searching
"... Abstract—Communication constraints dictated by hardware often require a multirobot system to make decisions and take actions locally. Unfortunately, local knowledge may impose limits that run against global optimality in a decentralized optimization problem. This paper redesigns the taskswap mecha ..."
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Abstract—Communication constraints dictated by hardware often require a multirobot system to make decisions and take actions locally. Unfortunately, local knowledge may impose limits that run against global optimality in a decentralized optimization problem. This paper redesigns the taskswap mechanism recently introduced in an anytime assignment algorithm to tackle the problem of decentralized task allocation for large scale multirobot systems. We propose a fully decentralized approach that allows local search processes to execute concurrently while minimizing interactions amongst the processes, needing neither global broadcast nor a multihop communication protocol. The formulation is analyzed in a novel way using tools from group theory and the optimization duality theory to show that the convergence of local searching processes is related to a shortest path routing problem on a graph subject to the network topology. Simulation results show that this fully decentralized method converges quickly while sacrificing little optimality. I.
VisibilityBased Persistent Monitoring with Robot Teams
"... Abstract — We study the problem of planning paths for a team of robots motivated by coverage, persistent monitoring and surveillance applications. The input is a set of target points in a polygonal environment that must be monitored using robots with omnidirectional cameras. The goal is to compute ..."
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Abstract — We study the problem of planning paths for a team of robots motivated by coverage, persistent monitoring and surveillance applications. The input is a set of target points in a polygonal environment that must be monitored using robots with omnidirectional cameras. The goal is to compute paths for all robots such that every target is visible from at least one path. The cost of a path is given by the weighted combination of the length of the path (travel time) and the number of viewpoints along the path (measurement time). The overall cost is given by the maximum cost over all robot paths and the objective is to minimize the maximum cost. In its general form, this problem is NPhard. In this paper, we present an optimal algorithm and a constant factor approximation for two special versions of the problem. In both cases, the paths are restricted to lie on a predefined curve in the polygon. We show that if the curve satisfies a special property, termed chainvisibility, then there exists an optimal algorithm for monitoring a given set of target locations. Furthermore, if we restrict the input polygon to the class of street polygons, then we present a constantfactor approximation which is applicable even if the set of target locations is the entire polygon. In addition to theoretical proofs, we also present results from simulation studies. I.
1Automated SelfAssembly of Large Maritime Structures by a Team of Robotic Boats
"... Abstract—We present the methodology, algorithms, system design, and experiments addressing the selfassembly of large teams of autonomous robotic boats into floating platforms. Identical selfpropelled robotic boats autonomously dock together and form connected structures with controllable variable ..."
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Abstract—We present the methodology, algorithms, system design, and experiments addressing the selfassembly of large teams of autonomous robotic boats into floating platforms. Identical selfpropelled robotic boats autonomously dock together and form connected structures with controllable variable stiffness. These structures can selfreconfigure into arbitrary shapes limited only by the number of rectangular elements assembled in bricklike patterns. An O(m2) complexity algorithm automatically generates assembly plans which maximize opportunities for parallelism while constructing operatorspecified target configurations with m components. The system further features an O(n3) complexity algorithm for the concurrent assignment and planning of trajectories from n free robots to the growing structure. Such peertopeer assembly among modular robots compares favorably to a single active element assembling passive
Target Assignment in Robotic Networks: Distance Optimality Guarantees and Hierarchical Strategies
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1Motion Planning for Unlabeled Discs with Optimality Guarantees
"... Abstract—We study the problem of path planning for unlabeled (indistinguishable) unitdisc robots in a planar environment cluttered with polygonal obstacles. We introduce an algorithm which minimizes the total path length, i.e., the sum of lengths of the individual paths. Our algorithm is guarantee ..."
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Abstract—We study the problem of path planning for unlabeled (indistinguishable) unitdisc robots in a planar environment cluttered with polygonal obstacles. We introduce an algorithm which minimizes the total path length, i.e., the sum of lengths of the individual paths. Our algorithm is guaranteed to find a solution if one exists, or report that none exists otherwise. It runs in time Õ m4 +m2n2, where m is the number of robots and n is the total complexity of the workspace. Moreover, the total length of the returned solution is at most OPT+4m, where OPT is the optimal solution cost. To the best of our knowledge this is the first algorithm for the problem that has such guarantees. The algorithm has been implemented in an exact manner and we present experimental results that attest to its efficiency. I.
1Target Assignment in Robotic Networks: Distance Optimality Guarantees and Hierarchical Strategies
"... Abstract—We study the problem of assigning a group of mobile robots to an equal number of distinct static targets, seeking to minimize the total distance traveled by all robots until each target is occupied by a robot. In the first half of our paper, the robots assume limited communication and targe ..."
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Abstract—We study the problem of assigning a group of mobile robots to an equal number of distinct static targets, seeking to minimize the total distance traveled by all robots until each target is occupied by a robot. In the first half of our paper, the robots assume limited communication and targetsensing range; otherwise, the robots have no prior knowledge of target locations. Under these assumptions, we present a necessary and sufficient condition under which true distance optimality can be achieved. Moreover, we provide an explicit, nonasymptotic formula for computing the number of robots needed for achieving distance optimality in terms of the robots ’ communication and targetsensing ranges with arbitrary guaranteed probabilities. We also show that the same bound is asymptotically tight. Because a large number of robots is required for guaranteeing distance optimality with high probability, in the second half of our study, we present suboptimal strategies when the number of robots cannot be freely chosen. Assuming that each robot is aware of all target locations, we first work under a hierarchical communication model such that at each hierarchy level, the workspace is partitioned into disjoint regions; robots can communicate with one another if and only if they belong to the same region. This communication model leads naturally to hierarchical strategies, which, under mild assumptions, yield constant approximations of true distanceoptimal solutions. We then revisit the rangebased communication model and show that combining hierarchical strategies with simple rendezvousbased strategies results in decentralized strategies which again achieve constant approximation ratios on distance optimality. Results from simulation show that the approximation ratio is as low as 1.4. I.