Adaptive Coordinated Motion Control for Swarm Robotics Based on Brain Storm Optimization
This work addresses the need for low-cost, adaptive control in swarm robotics, but it is incremental as it builds on existing PID and optimization methods.
The paper tackled the problem of coordinated motion control in swarm robotics by optimizing PID controller parameters using a modified Brain Storm Optimization algorithm, achieving adaptive parameter tuning during robot movements with validated flexibility and scalability.
Coordinated motion control in swarm robotics aims to ensure the coherence of members in space, i.e., the robots in a swarm perform coordinated movements to maintain spatial structures. This problem can be modeled as a tracking control problem, in which individuals in the swarm follow a target position with the consideration of specific relative distance or orientations. To keep the communication cost low, the PID controller can be utilized to achieve the leader-follower tracking control task without the information of leader velocities. However, the controller's parameters need to be optimized to adapt to situations changing, such as the different swarm population, the changing of the target to be followed, and the anti-collision demands, etc. In this letter, we apply a modified Brain Storm Optimization (BSO) algorithm to an incremental PID tracking controller to get the relatively optimal parameters adaptively for leader-follower formation control for swarm robotics. Simulation results show that the proposed method could reach the optimal parameters during robot movements. The flexibility and scalability are also validated, which ensures that the proposed method can adapt to different situations and be a good candidate for coordinated motion control for swarm robotics in more realistic scenarios.