Abstract
<jats:p>The temporal-difference learning-inspired mission planning algorithm developed in previous work resulted in a reduction in makespan, i.e., total mission time, compared to existing mission planning techniques. However, optimization was not performed for the robots other than the one determining the makespan. In this paper, an enhanced mission planning algorithm that optimizes the mission completion time for the remaining robots by adjusting the plan is proposed. Some assignment steps in previous research are modified and the missions are adjusted to further reduce makespan. The optimal mission planning performance of the proposed algorithm is demonstrated by comparing it with the results of various techniques, including Brute-Force, across various cases.</jats:p>