Spatio-temporal multi-robot routing

Chopra, Smriti; Egerstedt, Magnus

doi:10.1016/j.automatica.2015.07.010

Cited by 17 publications

(11 citation statements)

References 21 publications

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“…The authors exploit gossip based local optimizations to both assign tasks located in a plane and compute optimized routes for robots. Finally, Chopra and Egerstedt investigated in [21,22] heterogeneity in multi-robot systems as the ability of robots with heterogeneous skill sets to serve spatially and temporally distributed tasks.…”

Section: Literature Reviewmentioning

confidence: 99%

Gossip based asynchronous and randomized distributed task assignment with guaranteed performance on heterogeneous networks

Franceschelli

Giua

Seatzu

2017

Nonlinear Analysis: Hybrid Systems

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The main contribution of this paper is a novel distributed algorithm based on asynchronous and randomized local interactions, i.e., gossip based, for task assignment on heterogeneous networks. We consider a set of tasks with heterogeneous cost to be assigned to a set of nodes with heterogeneous execution speed and interconnected by a network with unknown topology represented by an undirected graph. Our objective is to minimize the execution time of the set of tasks by the networked system. We propose a local interaction rule which allows the nodes of a network to cooperatively assign tasks among themselves with a guaranteed performance with respect to the optimal assignment exploiting a gossip based randomized interaction scheme. We first characterize the convergence properties of the proposed approach, then we propose an edge selection process and a distributed embedded stop criterion to terminate communications, not only task exchanges, while keeping the performance guarantee. Numerical simulations are finally presented to corroborate the theoretical results.

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Section: Literature Reviewmentioning

confidence: 99%

Gossip based asynchronous and randomized distributed task assignment with guaranteed performance on heterogeneous networks

Franceschelli

Giua

Seatzu

2017

Nonlinear Analysis: Hybrid Systems

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“…However, most of the discussed algorithms have been developed for the static multi-vehicle task assignment problem in which the information on all the target locations to be visited is initially known and no new target locations dynamically appear. Some algorithms have been designed for the dynamic task assignment for multiple vehicles in which the position information of some targets or vehicles is not initially known to every vehicle (Fua and Ge, 2005;Smith and Bullo, 2009;Zhu et al, 2013;Yu et al, 2015;Chopra and Egerstedt, 2015). In Fua and Ge (2005), a cooperative backoff adaptive scheme was designed for the task allocation of robots under limited communication range and potential malfunctions, where completing one task might require the cooperation of several robots.…”

Section: Introductionmentioning

confidence: 99%

Event- and time-triggered dynamic task assignments for multiple vehicles

2020

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We study the dynamic task assignment problem in which multiple dispersed vehicles are employed to visit a set of targets. Some targets' locations are initially known and the others are dynamically randomly generated during a finite time horizon. The objective is to visit all the target locations while trying to minimize the vehicles' total travel time. Based on existing algorithms used to deal with static multi-vehicle task assignment, two types of dynamic task assignments, namely event-triggered and time-triggered, are studied to investigate what the appropriate time instants should be to change in real time the assignment of the target locations in response to the newly generated target locations. Furthermore, for both the eventand time-triggered assignments, we propose several algorithms to investigate how to distribute the newly generated target locations to the vehicles. Extensive numerical simulations are carried out which show better performance of the event-triggered task assignment al-

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“…In [3] and [10], a collision-free trajectory optimization problem for autonomous vehicles is formulated as a MILP solved by a branch-and-bound algorithm with branching heuristics. In [11], a multi-robot routing problem under connectivity constraints is shown to be formulated as an integer program with binary variables, and then its LP relaxation is solved. Recently, in [12] a heuristic based on the Alternating Direction Method of Multipliers is used to approximately solve mixed-integer linear and quadratic programs.…”

Section: Introductionmentioning

confidence: 99%

Distributed Mixed-Integer Linear Programming via Cut Generation and Constraint Exchange

Testa

Rucco

Notarstefano

2020

IEEE Trans. Automat. Contr.

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Many problems of interest for cyber-physical network systems can be formulated as Mixed-Integer Linear Programs in which the constraints are distributed among the agents. In this paper we propose a distributed algorithmic framework to solve this class of optimization problems in a peer-to-peer network with no coordinator and with limited computation and communication capabilities. At each communication round, agents locally solve a small linear program, generate suitable cutting planes and communicate a fixed number of active constraints. Within the distributed framework, we first propose an algorithm that, under the assumption of integer-valued optimal cost, guarantees finite-time convergence to an optimal solution. Second, we propose an algorithm for general problems that provides a suboptimal solution up to a given tolerance in a finite number of communication rounds. Both algorithms work under asynchronous, directed, unreliable networks. Finally, through numerical computations, we analyze the algorithm scalability in terms of the network size. Moreover, for a multi-agent multi-task assignment problem, we show, consistently with the theory, its robustness to packet loss. 1 A. Testa and A. Rucco are with the An early short version of this work appeared as [1]: the current article includes a much improved comprehensive treatment, an extended algorithm for general mixed-integer linear programs, new numerical computations, and an application to the multi-agent multi-task assignment problem.

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Spatio-temporal multi-robot routing

Cited by 17 publications

References 21 publications

Gossip based asynchronous and randomized distributed task assignment with guaranteed performance on heterogeneous networks

Gossip based asynchronous and randomized distributed task assignment with guaranteed performance on heterogeneous networks

Event- and time-triggered dynamic task assignments for multiple vehicles

Distributed Mixed-Integer Linear Programming via Cut Generation and Constraint Exchange

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