A future lunar habitat, as part of the Artemis program, will require a significant amount of logistics infrastructure. Cargo that is transported to the Moon will need to be moved from a landing site to other key locations that may be up to 5 km away. Teach and repeat navigation is well suited to this task as utility rovers will need to repeat these cargo routes many times. One of the most significant challenges involves the modules that will be assembled together to form the habitat. Canada is studying potential Lunar Utility Vehicle (LUV) designs to carry these large payloads between the landing site and the location of the habitat. As the details of the cargo continue to evolve, using two, smaller LUVs to carry cargo together would provide high capacity and mission flexibility. In this paper, we develop and implement a distributed model-predictive controller that allows vehicles to carry cargo that is shared between them. The algorithm is compared to baselines in small-scale before being implemented onboard two 800 kg path-to-flight rovers and field tested carrying a 475 kg cargo between them. A custom cargo coupling decouples the kinematics of each vehicle while fully supporting the cargo's mass. In our field test, the rovers maintain a relative separation error of 9.2 cm and maximum error of 33.4 cm. This multi-vehicle control architecture retains the high-quality path tracking of lidar teach and repeat for each rover. We demonstrate that kinematic freedom of the vehicles allows a single controller to provide mission improvements for other operations as well.

翻译：作为“阿尔忒弥斯”计划的一部分，未来月球栖息地需要大量后勤基础设施。运送到月球的货物必须从着陆点转移到可能距离5公里的其他关键位置。由于作业漫游车需要多次重复执行这些货物运输路线，“示教与重复”导航方法非常适用于这一任务。其中最显著的挑战之一涉及将组装成栖息地的模块。加拿大正在研究潜在月球实用车辆（LUV）设计，以在着陆点与栖息地位置之间运输这些大型载荷。随着货物细节的不断演变，使用两辆较小的LUV协同运输货物将提供高容量和任务灵活性。本文开发并实现了一种分布式模型预测控制器，使车辆能够协同运输共享货物。该算法首先在小规模场景下与基线方法进行对比，随后在两辆重达800公斤的飞行验证漫游车上实施，并在现场测试中共同运输475公斤货物。定制的货物耦合装置在完全支撑货物重量的同时，解耦了每辆车的运动学特性。现场测试中，漫游车保持相对分离误差为9.2厘米，最大误差为33.4厘米。该多车控制架构保留了每辆漫游车基于激光雷达的“示教与重复”路径跟踪的高质量性能。我们证明，车辆的运动学自由度使得单一控制器也能为其他操作提供任务改进。