Improving Collision Avoidance Behavior of a Target-Searching Algorithm for Kilobots

Alexander Horst (University of Applied Sciences and Arts Northwestern Switzerland)
Egemen Kaba (University of Applied Sciences and Arts Northwestern Switzerland)
Rolf Dornberger (University of Applied Sciences and Arts Northwestern Switzerland)
Thomas Hanne (University of Applied Sciences and Arts Northwestern Switzerland)

Journal of Robotics and Control (JRC)
Vol 2, No 6 (2021): November

Collision avoidance in the area of swarm robotics is very important. The lacking ability of such collision avoidance is mentioned as one important reason for the sparse distribution of the small test robots named Kilobots. In this research paper, two new algorithms providing a collision avoidance strategy are presented and compared with previous research results. The first algorithm uses randomness to decide which one of several approaching Kilobots are stopped for a defined time before starting to move again. The second algorithm tries   to determine the assumed position of approaching Kilobots based on its radio signal strength and then to move away in the opposite direction by rotation. The results, especially of the second algorithm, are promising as the number of collisions can be significantly reduced.