The International Lunar Research Station, to be established around 2030, will equip lunar rovers with robotic arms as constructors. Construction requires lunar soil and lunar rovers, for which rovers must go toward different waypoints without encountering obstacles in a limited time due to the short day, especially near the south pole. Traditional planning methods, such as uploading instructions from the ground, can hardly handle many rovers moving on the moon simultaneously with high efficiency. Therefore, we propose a new collaborative path-planning method based on deep reinforcement learning, where the heuristics are demonstrated by both the target and the obstacles in the artificial potential field. Environments have been randomly generated where small and large obstacles and different waypoints are created to collect resources, train the deep reinforcement learning agent to propose actions, and lead the rovers to move without obstacles, finish rovers’ tasks, and reach different targets. The artificial potential field created by obstacles and other rovers in every step affects the action choice of the rover. Information from the artificial potential field would be transformed into rewards in deep reinforcement learning that helps keep distance and safety. Experiments demonstrate that our method can guide rovers moving more safely without turning into nearby large obstacles or collision with other rovers as well as consuming less energy compared with the multi-agent A-Star path-planning algorithm with improved obstacle avoidance method.

Pre-designed spacecraft plans suffer from failure due to the uncertain space environment. In this case, instead of spending a long time waiting for ground control to upload a feasible plan in order to achieve the mission goals, the spacecraft could repair the failed plan while executing another part of the plan. This paper proposes a method called Isolation and Repair Plan Failures (IRPF) for a spaceship with durable, concurrent, and resource-dependent actions. To enable the spacecraft to perform some actions when a plan fails, IRPF separates all defective actions from executable actions in the pre-designed plan according to causal analysis between the failure state and the established plan. Then, to address the competition between operation and repair during the partial execution of the plan, IRPF sets up several regulatory factors associated with the search process for a solution, and then repairs the broken plan within the limits of these factors. Experiments were carried out in simulations of a satellite and a multi-rover system. The results demonstrate that, compared with replanning and other plan-repair methods, IRPF creates an execution plan more quickly and searches for a recovery plan with fewer explored state nodes in a shorter period of time.