A flocking robots round-up multi-target and obstacle avoidance control algorithm

Cooperative rounding up multiple targets through self-organizing flocking behavior represents a fundamental challenge in multi-robot systems. Existing studies have often failed to achieve a uniform distribution of robots across multiple targets and, in most cases, only succeeded in tracking rather than capturing the targets. To address these limitations, this paper proposes a round-up control algorithm that consists of two stages: (1) Tracking and enclosing: Each target is assigned a dynamic influence area that adjusts according to the number of surrounding robots, enabling robots to redistribute themselves and tracking all targets with a balanced number. (2) Intercepting and capturing: Once the number of robots within a target’s influence area reaches a predefined threshold, the robots gradually converge their velocities. Together with a repulsive potential function designed between robots and targets, this mechanism effectively restricts target movement and enables capture. Furthermore, a new potential function with increased well depth is introduced to accelerate inter-robot interactions and reduce the time required to complete the rounding-up process, while maintaining effective obstacle avoidance. The stability of the proposed control strategy is rigorously analyzed using Lyapunov’s theorem and LaSalle’s invariance principle. Simulation results validate the effectiveness of the algorithm, demonstrating uniform target allocation, successful capture, and significant improvements in efficiency compared with existing methods.