Learning-Inspired Immune Algorithm for Multiobjective-Optimized Multirobot Maritime Patrolling

Abstract

Multirobot patrolling systems with various sensing and communications devices are deployed to guarantee maritime safety. Patrolling path planning for multiple robots can be modeled as a multiobjective optimization problem. The positions of patrolling nodes impact the length of patrolling paths and execution efficiency of robots. To compute them, a huge solution space is encountered. Besides, multiple patrolling nodes on the same line lead to the same patrolling scheme. Thus, how to promote solution (population) diversity becomes a new challenge. To tackle it, this work proposes a learning-inspired immune algorithm. It uses the historical information in the previous generations during iterations to realize a learning process. Unlike saving all the individuals themselves and training a model for them, the useful historical information is extracted by using upper confidence bound-based and actor–critic-inspired methods. Both time consumption and storage space can be dramatically saved. The experimental results indicate that the proposed algorithm can generate multiple patrolling schemes for the decision makers and outperforms the state-of-the-art.