Hybrid Insect-Inspired Multi-Robot Coverage in Complex Environments

Coordination is one of the most challenging research issues in distributed multi-robot systems (MRS), aiming to improve performance, energy consumption, robustness and reliability of a robotic system in accomplishing complex tasks. Social insect-inspired coordination techniques achieve these goals by applying simple but effective heuristics from which elegant solutions emerge. In our previous research, we demonstrated the effectiveness of a hybrid ant-and-bee inspired approach, HybaCo, designed to provide coordinated multi-robot solutions to area coverage problems in simple environments. In this paper, we extend this work and illustrate the effectiveness of our hybrid ant-and-bee inspired approach (HybaCo) in complex environments with static obstacles. We evaluate both the ant-inspired (StiCo) and bee-inspired (BeePCo) approaches separately, and then compare them according to a number of performance criteria using a high-level simulator. Experimental results indicate that HybaCo improves the area coverage uniformly in complex environments as well as simple environments.

[1]  Marco Dorigo,et al.  Optimization, Learning and Natural Algorithms , 1992 .

[2]  Marco Dorigo,et al.  AntNet: Distributed Stigmergetic Control for Communications Networks , 1998, J. Artif. Intell. Res..

[3]  Gerhard Weiss,et al.  A Multi-robot Coverage Approach Based on Stigmergic Communication , 2012, MATES.

[4]  Jun Hu,et al.  AdMoVeo: A Robotic Platform for Teaching Creative Programming to Designers , 2009, Edutainment.

[5]  Gerhard Weiss,et al.  StiCo in action , 2013, AAMAS.

[6]  Leandro Soares Indrusiak,et al.  Using mobile robotic agents to increase service availability and extend network lifetime on WSRNs , 2014, 2014 12th IEEE International Conference on Industrial Informatics (INDIN).

[7]  Elizabeth Sklar,et al.  Social Insect-Inspired Multi-Robot Coverage , 2015, AAMAS.

[8]  Amanda J. C. Sharkey,et al.  Swarm robotics , 2014, Scholarpedia.

[9]  Ann Nowé,et al.  Bee Behaviour in Multi-agent Systems , 2007, Adaptive Agents and Multi-Agents Systems.

[10]  Gerhard Weiss,et al.  A macroscopic model for multi-robot stigmergic coverage , 2013, AAMAS.

[11]  Karl Tuyls,et al.  Multi-Robot Coverage: A Bee Pheromone Signalling Approach , 2014, ALIA.

[12]  Leandro Soares Indrusiak,et al.  Bioinspired Load Balancing in Large-Scale WSNs Using Pheromone Signalling , 2013, Int. J. Distributed Sens. Networks.

[13]  S Erol Swarm Robotics: From Sources of Inspiration to Domains of Application , 2005 .

[14]  Leandro Soares Indrusiak,et al.  Runtime optimisation in WSNs for load balancing using pheromone signalling , 2012, 2012 IEEE 3rd International Conference on Networked Embedded Systems for Every Application (NESEA).

[15]  Thomas Stützle,et al.  Ant colony optimization: artificial ants as a computational intelligence technique , 2006 .

[16]  Gerhard Weiss,et al.  Stigmergic coverage algorithm for multi-robot systems (demonstration) , 2012, AAMAS.