Adaptive Coordinated Motion Control for Swarm Robotics Based on Brain Storm Optimization

TL;DR

This paper introduces an adaptive method using Brain Storm Optimization to tune PID controllers for swarm robotics, enhancing coordinated movement, flexibility, and scalability in dynamic environments.

Contribution

It presents a novel application of modified Brain Storm Optimization for adaptive PID parameter tuning in swarm robot formation control.

Findings

The method achieves near-optimal parameters during robot movements.

It demonstrates high flexibility and scalability in various scenarios.

Simulation results confirm improved coordination and adaptability.

Abstract

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.