Lunar Rover Cargo Transport: Mission Concept and Field Test

2026-01-06

IEEE Transactions on Field Robotics

AI Summary

This work addresses the challenge of achieving high-precision, safe cargo transport and docking for lunar surface operations in complex environments. The study proposes and validates an autonomous navigation approach based on LiDAR-based teach-and-repeat methodology. After an operator constructs a network of safe trajectories through semi-autonomous teleoperation, the rover accurately replays these paths to perform loading and unloading tasks. This approach demonstrates, for the first time in a lunar-analog environment, closed-loop, high-precision cargo transportation capable of rapid deployment and reliable operation. During a two-week field trial at the Canadian Space Agency’s analog terrain site, the system successfully executed autonomous pick-up and delivery under harsh conditions, achieving localization accuracy within the required docking tolerances.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Natural Language Processing: Safety and Robustness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

In future operations on the lunar surface, automated vehicles will be required to transport cargo between known locations. Such vehicles must be able to navigate precisely in safe regions to avoid natural hazards, human-constructed infrastructure, and dangerous dark shadows. Rovers must be able to park their cargo autonomously within a small tolerance to achieve a successful pickup and delivery. In this field test, Lidar Teach and Repeat (LT&R) provides an ideal autonomy solution for transporting cargo in this way. A one-ton path-to-flight rover was driven in a semi-autonomous remote-control mode to create a network of safe paths. Once the route was taught, the rover immediately repeated the entire network of paths autonomously while carrying cargo. The closed-loop performance is accurate enough to align the vehicle with the cargo and pick it up. This field report describes a two-week deployment at the Canadian Space Agency’s (CSA) Analog Terrain, culminating in a simulated lunar operation to evaluate the system’s capabilities. Successful cargo collection and delivery were demonstrated in harsh environmental conditions.

Problem

• Lunar rover
• Cargo transport
• Autonomous navigation
• Precision parking
• Hazard avoidance

Innovation

• Lidar Teach and Repeat
• autonomous lunar rover
• cargo transport
• precision navigation
• field test