Energy-efficient strategies for multi-agent continuous cooperative patrolling problems

Whereas research of the multi-agent patrolling problem has been widely conducted from different aspects, the issue of energy minimization has not been sufficiently studied. When considering real-world applications with a trade-off between energy efficiency and level of perfection, it is usually more desirable to minimize the energy cost and carry out the tasks to the required level of quality instead of fulfilling tasks perfectly by ignoring energy efficiency. This paper proposes a series of coordinated behavioral strategies and an autonomous learning method of target decision strategies to reduce of energy consumption on the premise of satisfying quality requirements in continuous patrolling problems by multiple cooperative agents. We extended our previous method of target decision strategy learning by incorporating a number of behavioral strategies, with which agents individually estimate whether the requirement is reached and then modify their action plans to reduce energy consumption. It is experimentally shown that agents with the proposed methods learn to decide the appropriate strategies based on energy cost and performance efficiency and are able to reduce energy consumption while cooperatively meeting the given requirements of quality.