Bio-inspired Strategies for the Coordination of a Swarm of Robots in an Unknown Area
This paper addresses the problem of searching mines in an unknown area and disarming them in a cooperative manner. We describe two bio-inspired mechanisms that allow the robots to initiate the coordination with other robots when a mine is discovered. We m
- PDF / 2,031,692 Bytes
- 17 Pages / 439.37 x 666.142 pts Page_size
- 55 Downloads / 190 Views
Department of Computer Engineering Modeling, Electronics and System Science, University of Calabria, Rende (CS), Italy {n.palmieri,derango,marano}@dimes.unical.it 2 School of Science and Technology, Middlesex University, The Burroughs, London, UK {n.palmieri,x.yang}@mdx.ac.uk
Abstract. This paper addresses the problem of searching mines in an unknown area and disarming them in a cooperative manner. We describe two bio-inspired mechanisms that allow the robots to initiate the coordination with other robots when a mine is discovered. We model this problem as a multi-objective exploration and disarming problem. Specifically we propose a modified version of the Ant Colony Optimization (ATS-RR) and the Firefly Algorithm (FTS-RR). The proposed approaches have been implemented and evaluated in several simulated environments varying the parameter of the problems in term of team sizes, the number of mines disseminated in the area, the dimension of area. Our approaches have been implemented in simulation environments and have been compared with Particle Swarm Optimization (PSO). The results demonstrate the efficiency of the FTS-RR over others. Keywords: Swarm intelligence Bio-inspired algorithm Multi-robot system Coordination
1 Introduction In the past new years, the attention of researchers fovuses on the idea of creating groups of mobile robots that are able to collaborate in order to accomplish one or more predefined tasks such as aerial surveillance and reconnaissance, unmanned search and rescue, exploration and so on. Multi-robot systems can provide improved performance, fault-tolerance and robustness in those tasks through parallelism and redundancy. The key issue concerning collective robotics is how to specify the rules of behavior and interaction at the level of individual robots such that coordination can be achieved automatically at the global level. This is called the coordination problem [1, 2]. A central aspect of the coordination of the swarm is to ensure that the robots are distributed in efficient manner into the area in order to ensure rapid and efficient completion of the tasks. Swarm robotics is a new approach to the coordination of large numbers of relatively simple robots. The approach takes its inspiration from the system-level functioning of © Springer International Publishing AG 2017 J.J. Merelo et al. (eds.), Computational Intelligence, Studies in Computational Intelligence 669, DOI 10.1007/978-3-319-48506-5_6
Bio-inspired Strategies for the Coordination of a Swarm
97
social insects which demonstrate three desired characteristics for multi-robot systems: robustness, flexibility and scalability [3]. Algorithms in swarm robotics mostly rely on cooperation and simple interactions between robots, rather than on complex individual behaviours that require powerful sensory capabilities. Several researchers have developed simple information sharing techniques for multi-robot systems using simple, nature inspired models such as stigmergy to enable coordination among the robots in dynamic environments [
Data Loading...
