Solving Methods for Multi-Robot Missions Planning with Energy Capacity Consideration

We consider a problem minimizing the total duration of accomplishing missions performed by heterogeneous vehicles. The problem respects constraints related to vehicles’ capabilities and energy capacities. The goal is to determine the best routes of each vehicle deployed by choosing which waypoints to pass and which observations to perform. Each vehicle has a particular distance matrix and a limited energy. In order to provide high quality solutions within reasonable computational time, two decomposition-based approximate methods were implemented: (i) the Multiphase heuristic, and (ii) the Two-Phase iterative heuristic. The performance of the methods is evaluated against the Branch-and-Cut algorithm using generated instances.

[1]  Chandra Chekuri,et al.  A recursive greedy algorithm for walks in directed graphs , 2005, 46th Annual IEEE Symposium on Foundations of Computer Science (FOCS'05).

[2]  Alexandre Dolgui,et al.  An efficient two-phase iterative heuristic for Collection-Disassembly problem , 2017, Comput. Ind. Eng..

[3]  J. Mitchell.  Branch-and-Cut Algorithms for Combinatorial Optimization Problems , 1988 .

[4]  Gaurav S. Sukhatme,et al.  Branch and bound for informative path planning , 2012, 2012 IEEE International Conference on Robotics and Automation.

[5]  Takao Enkawa,et al.  Competition-based neural network for the multiple travelling salesmen problem with minmax objective , 1999, Comput. Oper. Res..

[6]  B. Charrow.  Information-Theoretic Active Perception for Multi-Robot Teams , 2015 .

[7]  Timothy Patten,et al.  Decentralised Monte Carlo Tree Search for Active Perception , 2016, WAFR.

[8]  Ahmed Hussein,et al.  Market-based approach to Multi-robot Task Allocation , 2013, 2013 International Conference on Individual and Collective Behaviors in Robotics (ICBR).

[9]  Robert Fitch,et al.  Path Planning With Spatiotemporal Optimal Stopping for Stochastic Mission Monitoring , 2017, IEEE Transactions on Robotics.

[10]  P. B. Sujit,et al.  On cooperation between a fuel constrained UAV and a refueling UGV for large scale mapping applications , 2015, 2015 International Conference on Unmanned Aircraft Systems (ICUAS).

[11]  Pratap Tokekar,et al.  Algorithms for Routing of Unmanned Aerial Vehicles with Mobile Recharging Stations , 2017, 2018 IEEE International Conference on Robotics and Automation (ICRA).

[12]  Stephen Warshall,et al.  A Theorem on Boolean Matrices , 1962, JACM.

[13]  Steven Lake Waslander,et al.  A graph-based approach to multi-robot rendezvous for recharging in persistent tasks , 2013, 2013 IEEE International Conference on Robotics and Automation.

[14]  Stéphane Dauzère-Pérès,et al.  A Two-Phase Iterative Heuristic Approach for the Production Routing Problem , 2015, Transp. Sci..

[15]  Haldun Süral,et al.  A multi-phase heuristic for the production routing problem , 2017, Comput. Oper. Res..

[16]  Jan Faigl,et al.  Approximate Solution of the Multiple Watchman Routes Problem With Restricted Visibility Range , 2010, IEEE Transactions on Neural Networks.

[17]  Jan Faigl,et al.  Online planning for multi-robot active perception with self-organising maps , 2018, Auton. Robots.

[18]  John E. Beasley,et al.  Route first--Cluster second methods for vehicle routing , 1983 .

[19]  Robert Fitch,et al.  Probabilistic Maximum Set Cover with Path Constraints for Informative Path Planning , 2016 .

[20]  Andreas Krause,et al.  Efficient Informative Sensing using Multiple Robots , 2014, J. Artif. Intell. Res..

[21]  Jan Faigl,et al.  An Application of Self-Organizing Map for Multirobot Multigoal Path Planning with Minmax Objective , 2016, Comput. Intell. Neurosci..

[22]  Ahmed M. Elmogy,et al.  Multi-robot Task Allocation: A Review of the State-of-the-Art , 2015, Advances in Social Media Analysis.

[23]  Keld Helsgaun,et al.  An effective implementation of the Lin-Kernighan traveling salesman heuristic , 2000, Eur. J. Oper. Res..