A Network Robot System for Multiple Odor Source Localization using Glowworm Swarm Optimization Algorithm

In this paper we address the problem of multiple odor source localization using mobile robot swarms modelled as network robot systems. These networked robots are used to locate multiple radiation/odor sources like oil spills, leaks in pressurized systems, hazardous plumes/aerosols resulting from nuclear/chemical spills, deepsea hydrothermal vent plumes, fire-origins in forest fires, and hazardous chemical discharge in water bodies. The robots are assumed to be constrained by limited communication and sensor range and field of view. We present a swarm robotic communication and decision network that enables the robots to obtain information about the environment from their designated neighbors and compute movement decisions autonomously. We use a modified version of the glowworm swarm optimization (GSO) algorithm, which is specially designed for such applications. This algorithm uses an adaptive decision range that enables the agent swarm to partition into disjoint subgroups, simultaneously taxis towards, and rendezvous at multiple source locations of interest. Certain algorithmic aspects need modifications while implementing in a robotic network mainly because of the point-agent model of the basic GSO algorithm and the physical dimensions and dynamics of a real robot. We briefly describe the basic GSO algorithm and the modifications incorporated into the algorithm in order to make it suitable for a robotic implementation. We conduct embodied robot simulations by using a multi-robot-simulator called Player/Stage that provides realistic sensor and actuator models, in order to demonstrate the efficacy of the networked robot system in simultaneously detecting multiple odor sources. The study, based on embodied simulation experiments, also shows the robustness of the algorithm to implementational constraints.