Exploring unknown environments with multi-modal locomotion swarm

Swarm robotics is focused on creating intelligent systems from large number of simple robots. The majority of nowadays robots are bound to operations within mono-modal locomotion (i.e. land, air or water). However, some animals have the capacity to alter their locomotion modalities to suit various terrains, operating at high levels of competence in a range of substrates. One of the most significant challenges in bio-inspired robotics is to determine how to use multimodal locomotion to help robots perform a variety of tasks. In this paper, we investigate the use of multi-modal locomotion on a swarm of robots through a multi-target search algorithm inspired from the behavior of flying ants. Features of swarm intelligence such as distributivity, robustness and scalability are ensured by the proposed algorithm. Although the simplicity of movement policies of each agent, complex and efficient exploration is achieved at the team level.

[1]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[2]  Nicolas Jouandeau,et al.  An Energy-Aware Algorithm for Large Scale Foraging Systems , 2015, Scalable Comput. Pract. Exp..

[3]  Jean-Sébastien Sereni,et al.  Collaborative search on the plane without communication , 2012, PODC '12.

[4]  Nicolas Jouandeau,et al.  A Cooperative Switching Algorithm for Multi-Agent Foraging , 2016, Eng. Appl. Artif. Intell..

[5]  David W. Payton,et al.  Pheromone Robotics , 2001, SPIE Optics East.

[6]  Giancarlo Fortino,et al.  An integrated approach for the development and validation of multi-agent systems , 2005, Comput. Syst. Sci. Eng..

[7]  Lenka Pitonakova,et al.  Understanding the Role of Recruitment in Collective Robot Foraging , 2014 .

[8]  Christoph Lenzen.  The Power of Pheromones in Ant Foraging , 2013 .

[9]  F. Zambonelli,et al.  Spreading Pheromones in Everyday Environments through RFID Technology , 2005 .

[10]  W. J. Bell.  Searching Behaviour: The Behavioural Ecology of Finding Resources , 1991 .

[11]  Eric J. Barth,et al.  A dynamic programming approach to robotic swarm navigation using relay markers , 2003, Proceedings of the 2003 American Control Conference, 2003..

[12]  Sven Koenig,et al.  Building Terrain-Covering Ant Robots: A Feasibility Study , 2004, Auton. Robots.

[13]  Moreno Marzolla,et al.  Netlogo , 2019, Economics for a Fairer Society.

[14]  Rolf Klein,et al.  Online algorithms for searching and exploration in the plane , 2010, Comput. Sci. Rev..

[15]  Daniel Campos,et al.  Random Search Strategies , 2014 .

[16]  Giancarlo Fortino,et al.  A Mission-Oriented Coordination Framework for Teams of Mobile Aerial and Terrestrial Smart Objects , 2016, Mob. Networks Appl..

[17]  Haibing Guan,et al.  A swarm intelligence based algorithm for distribute search and collective cleanup , 2010, 2010 IEEE International Conference on Intelligent Computing and Intelligent Systems.

[18]  Giancarlo Fortino,et al.  Using event-driven lightweight DSC-based agents for MAS modelling , 2010, Int. J. Agent Oriented Softw. Eng..

[19]  Tüze Kuyucu,et al.  Evolutionary Optimization of Pheromone-Based Stigmergic Communication , 2012, EvoApplications.

[20]  Gaurav S. Sukhatme,et al.  Blazing a Trail: Insect-inspired Resource Transportation by a Robot Team , 2000, DARS.

[21]  Rodrigo Calvo,et al.  Bio-inspired coordination of multiple robots systems and stigmergy mechanims to cooperative exploration and surveillance tasks , 2011, 2011 IEEE 5th International Conference on Cybernetics and Intelligent Systems (CIS).