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.

I. Introduction

In recent, the source location problem is a topic of great significance and has been widely concerned by researchers. This problem originates in many scenarios, such as automatic navigation, fire spot discover, disaster zone rescue, information detection, chemical spill investigation, hazardous gas forecast, and environment monitoring [1]–[9]. Among them, the sources can be generated by the diffusion or propagation of certain substances and fields, including plume odor, oil leakage, electromagnetic radiation, thermal processes, sound signals, and so on. It is worth mentioning that those sources may be in a dangerous environment or pose a risk to humans, thus search tasks for them are often accomplished by robots.