Multi-Robot Source Location of Scalar Fields by a Novel Swarm Search Mechanism with Collision/Obstacle Avoidance

This paper focuses on source location for scalar fields through multiple mobile robots with sensors. Combined with field strength measurements and swarm evolution mechanisms, the robots are guided to move toward the source of the field. Upon introducing an adaptive weight strategy, a swarm timely update mechanism and a leading-following behavior into quantum particle swarm optimization (QPSO) algorithm, quantum-leading-following-based optimization (QLFBO) algorithm is proposed to direct the movement of robots for a more efficient search. Meanwhile, a collision/obstacle avoidance strategy is designed for the robot to avert accidents. Considering the minimum problem as an optimization objective, the global convergence is proved for QLFBO algorithm. Besides, we study the computational complexity on QLFBO algorithm and the collision/obstacle avoidance strategy. Thus the performance analysis on the global convergence and computational complexity provides theoretical guarantee and feasibility support for multi-robot source location. Finally, simulation tests show the practicality and effectiveness of the developed scheme.