This paper presents a novel swarm robotics application of chemotaxis behaviour observed in microorganisms. This approach was used to cause exploration robots to return to a work area around the swarm's nest within a boundless environment. We investigate the performance of our algorithm through extensive simulation studies and hardware validation. Results show that the chemotaxis approach is effective for keeping the swarm close to both stationary and moving nests. Performance comparison of these results with the unrealistic case where a boundary wall was used to keep the swarm within a target search area showed that our chemotaxis approach produced competitive results.

Swarm foraging is a common test case application for multi-robot systems. In this paper RepAtt algorithm is used for improving coordination of a robot swarm by selectively broadcasting repulsion and attraction signals. This is a chemotaxis-inspired search behaviour where robots use the temporal gradients of these signals to navigate towards more advantageous areas. Hardware experiments were used to model and validate realistic, noisy sound communication and vision system. We then show through extensive simulation studies that RepAtt significantly improves swarm foraging time and robot efficiency under realistic communication and vision models.

Swarm foraging is a common test case application for multi-robot systems. In this paper we present a novel algorithm for improving coordination of a robot swarm by selectively broadcasting repulsion and attraction signals. Robots use a chemotaxis-inspired search behaviour based on the temporal gradients of these signals in order to navigate towards more advantageous areas. Hardware experiments were used to model and validate realistic, noisy sound communication. We then show through extensive simulation studies that our chemotaxis-based coordination algorithm significantly improves swarm foraging time and robot efficiency.

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