Multi-Agent Swarm Based Localization of Hazardous Events

Abstract

Swarm robotic concept has become a topic of extensive research in hazardous environments where fault tolerant, robust and energyefficient approach is needed. In this paper we present behavioural based algorithm for outdoor environment exploration. This work is inspired by ant colony behaviour models. Our goal is to control agent's motion using simple rules and not to rely on wireless communication. Agents perform random walk combined with simple heuristic pheromone based rules which prevent frequent visits of previously visited locations. To determine heuristic parameters which maximize area coverage, we optimize parameters by simulating swarm behaviour in Matlab. Furthermore, we take into account the agent limitations, such as battery lifetime, and we model a probability success of a mission with respect to that parameter.

Swarm Robots Search for Multiple Targets

“…The circles represent robots and the stars indicate the two targets in the environment. Target locations are determined by function f 1 , see (11). The whole robot swarm is split into two sub-swarms which are marked with different colors to distinguish them.…”

“…Among those many bio-inspired methods, mimicking the behaviors of swarms of eusocial animals for robots to perform target search has received extreme attentions. For examples, Doctor et al discussed using a bio-inspired algorithm of particle swarm optimization (PSO) for multi-robot search tasks with focus on optimizing PSO parameters [8], Tan et al proposed an ant colony algorithm for mobile robots robots realtime optimal path planning [9], Kisdi and Tatnall simulated the case of robots search for caves on mars using honey bees search strategy [10], Varga et al presented a method based on ant colony and honey bees behaviors for target localization and decision making [11], respectively. In the work of [12] the glowworm swarm optimization (GSO) is used for real robots multiple radiation sources search which is very interesting.…”

Swarm-based path-following for cooperative unmanned surface vehicles

“…In some of the previous articles, the authors presented another study (for details, refer to Karlsson et al 24 ) about a distributed control algorithm for mobile aquatic sensor networks. Researches on marine robotic swarming, presented in Fritsch et al 25 and Varga et al, 26 prove the great importance of the development of swarm control algorithms, in these cases applied to the oil pollution and to the critical target localization and enemy engagement problems, respectively. Finally, issues about the security challenges for swarm robotics are described in Higgins et al 27 and Sharma and Bagla.…”

“…Nevertheless, in order to investigate how this discrepancy affects the guidance of the swarm centroid during the transient phase of the swarm aggregation, equation (26) is recalled and rewritten taking into account the discrepancy term, thus obtaining Bibuli et al 199 …”

Collective search and decision-making for target localization

“…Generally, the swarm concept is very efficient in environmental monitoring since decentralized control requires minimal inter-agent communication. In [4], we proposed bio-inspired algorithms for target localization and swarm distribution (decision-making) over localized targets. Both algorithms were inspired by the behaviour of social insects, especially ants and honey bees.…”