Robotic Brain Storm Optimization: A Multi-target Collaborative Searching Paradigm for Swarm Robotics

TL;DR

This paper introduces Robotic BSO, a novel multi-target collaborative search method for swarm robotics based on Brain Storm Optimization, demonstrating improved multi-target exploration capabilities in simulated environments.

Contribution

It proposes a new BSO-based framework for swarm robotics that enhances multi-target search performance through clustering and guided search strategies.

Findings

Effective multi-target search demonstrated in simulations

Robotic BSO outperforms traditional methods in multi-modal environments

Potential for real-world swarm robotics applications

Abstract

Swarm intelligence optimization algorithms can be adopted in swarm robotics for target searching tasks in a 2-D or 3-D space by treating the target signal strength as fitness values. Many current works in the literature have achieved good performance in single-target search problems. However, when there are multiple targets in an environment to be searched, many swarm intelligence-based methods may converge to specific locations prematurely, making it impossible to explore the environment further. The Brain Storm Optimization (BSO) algorithm imitates a group of humans in solving problems collectively. A series of guided searches can finally obtain a relatively optimal solution for particular optimization problems. Furthermore, with a suitable clustering operation, it has better multi-modal optimization performance, i.e., it can find multiple optima in the objective space. By matching the members in a robotic swarm to the individuals in the algorithm under both environments and robots constraints, this paper proposes a BSO-based collaborative searching framework for swarm robotics called Robotic BSO. The simulation results show that the proposed method can simulate the BSO's guided search characteristics and has an excellent prospect for multi-target searching problems for swarm robotics.