A Distributed Model Predictive Control-Based Method for Multidifferent-Target Search in Unknown Environments

Abstract

This article proposes a framework for multidifferent-target search in unknown environments based on swarm intelligence. In this framework, the idea of distributed model predictive control is introduced in the target search method. The use of a hierarchical prediction strategy further improves the robot’s path prediction ability in unknown environments. Compared with swarm intelligence methods—adaptive robotic particle swarm optimization (A-RPSO), improved group explosion strategy (IGES), and other existing works, this strategy significantly improves the multidifferent-target search functionality and the task success rate in unknown complex obstacle environments. Moreover, two effective efforts are then introduced to reduce computational complexity and speed up online decision making. One is to select cooperative individuals based on the line of sight, and the other is to reduce both the frequency of decision making and the amount of data transmitted. A comparison between obstacle-free map experiments and obstacle map experiments confirms the effectiveness of the ideas and methods presented in this article.