Inspiring and Modeling Multi-Robot Search with Particle Swarm Optimization

Abstract

Abstract-Within the field of multi-robot systems, multi-robot search is one area which is currently receiving a lot of research attention. One major challenge within this area is to design effective algorithms that allow a team of robots to work together to find their targets. Recently, techniques have been adopted for multi-robot search from the Particle Swarm Optimization algorithm, which uses a virtual multi-agent search to find optima in a multi-dimensional function space. We present here a multi-search algorithm inspired by Particle Swarm Optimization. Additionally, we exploit this inspiration by modifying the Particle Swarm Optimization algorithm to mimic the multi-robot search process, thereby allowing us to model at an abstracted level the effects of changing aspects and parameters of the system such as number of robots and communication range.

Synchronous and Asynchronous Communication Modes for Swarm Robotic Search

“…Based on the above mapping relationship between swarm robotic search and PSO, EPSO method can be taken to model the swarm robotic system (Pugh and Martinoli, 2007;Xue and Zeng, 2008b). In order to understand such method well, let us examine the particle swarm optimization algorithm at first.…”

“…Differing from the ideal particles in PSO, robot possesses mass in real world that causes it to have inertia when moves about in the search environment. Therefore, as for same an evolution position of certain particle, it is unlimited to reach at any speed in PSO case, while robot may arrive at the same position in several sampling times due to constraint of kinematics and dynamics because the evolution position is only expected (Pugh and Martinoli, 2007). These factors should be taken consideration when we design the asynchronous interaction strategies.…”

“…We can borrow ideas from swarm intelligence-based optimization algorithms in general, and the particle swarm optimization (PSO) algorithm in particular, since the case of swarm robotic search can be mapped to the case of functions optimization with PSO. Later, this method is named as the extended particle swarm optimization (EPSO) method (Pugh and Martinoli, 2007). The particle swarm optimization algorithm is a global, stochastic search one, being derivative-free and population-based style (Schutte et al, 2004).…”

Key Aspects of PSO-Type Swarm Robotic Search: Signals Fusion and Path Planning

“…Extending the particle swarm optimization (PSO) algorithm to be one of systemic modeling and controlling tools, several research groups investigate target search with swarm robots (simulated or physical) respectively (Doctor et al, 2004;Hereford & Siebold, 2008;Jatmiko et al, 2007;Marques et al, 2006;Pugh & Martinoli, 2007;Xue & Zeng, 2008). The common idea they hold is to map such swarm robotic search to PSO and deal it with by employing the existing bio-inspired approaches to the latter case in a similar way (Xue et al, 2009).…”

“…Bear that in mind, we might as well explore some representative research work. Pugh et al compare the similarities and differences in properties between real robot and ideal particle, then extend PSO directly to model multiple robots for studying at an abstract level the effects of changing parameters of the swarm system (Pugh & Martinoli, 2007). Xue et al simplify characteristics of robot by treating each physical robot as a first order inertial element to study mechanism of limited sensing and local interactions in swarm robotic search (Xue & Zeng, 2008).…”