Self-organized swarm robot for multi-target trapping based on self-regulated density interaction - Zhou, Yuchen; Tao, Yuan; Lei, Xiaokang; Peng, Xingguang (Information sciences, 2024)
In swarm robotics, multi-target trapping usually relies on global information or explicit communication, posing a challenge for robots to autonomously self-organize and trap multiple targets with only local perceptual data. We present a self-regulated density-based approach for self-organized multi-target trapping. This method employs density-based negative feedback control and density-distance weighted target selection. Our approach leverages distributed perception, where robots perceive nearby peers within their sensory range, eliminating the need for explicit information exchange and enabling autonomous trapping. During task execution, negative feedback control adjusts swarm density, ensuring it reaches an optimal level. Building on this, individual robots use density fields and relative target positions to select suitable targets, achieving self-regulated dispersed selection and multi-target trapping. We validate our approach through numerical simulations and real-world robot experiments. Results demonstrate stable self-organization, efficient self-regulated dispersed target selection, and successful target trapping, even with a limited number of trapping robots in low-redundancy scenarios. •Self-organizing robot swarms calculate density using adapted Smoothed Particle Hydrodynamics (SPH) smoothing functions.•Density-based negative feedback control for robot density, rather than distance-based attractive-repulsive interactions.•Robots autonomously select targets through self-organized, shape-free strategies.