Flocking Controls for Swarms of Mobile Robots Inspired by Fish Schools

摘要

In this paper, we presented a decentralized algorithm of adaptive flocking and tracking, enabling a swarm of autonomous mobile robots to navigate toward achieving a mission while adapting to an unknown environment. Through local interactions by observing the position of the neighboring robots, the swarm could maintain a uniform distance between individual robots, and adapt its direction of heading and geometric shape. We verified the effectiveness of the proposed strategy using our in-house simulator. The simulation results clearly demonstrated that the proposed flocking and tracking are a simple and efficient approach to autonomous navigation for robot swarms in a cluttered environment by repeating the process of splitting and merging of groups passing through multiple narrow passageways. In practice, this approach is expected to be used in applications such as odor localization, search-and-rescue, and ad hoc mobile networking. Finally, we emphasize several points that highlight unique features of our approach. First, an equilateral triangle lattice is built with a partially connected mesh topology. Among all the possible types of regular polygons, the equilateral triangle lattices can reduce the computational burden and become less influenced by other robots, due to the limited number of neighbors, and be highly scalable. Secondly, the proposed local interaction is computationally efficient, since each robot utilizes only position information of other robots.