Manipulator Motion Planning Dataset from the Mt. Etna Moon Analogue Environment

Manipulator motion planning is an important technology for planetary exploration, as it enables crucial manipulation tasks in challenging environments. Testing motion planning in real situations, as opposed to relying solely on simulations, is crucial to account for unforeseen challenges, validate algorithm effectiveness in handling physical dynamics, and evaluate practical constraints and limitations for reliable and effective deployment. Nevertheless, simulations of motion planning are valuable as they provide a controlled and cost-effective environment to develop algorithms before deploying them in real-world situations. A dataset from real situations is particularly valuable for simulation as it provides accurate and diverse real-world data, enabling more realistic modeling and testing of motion planning algorithms. We supply a dataset to develop, optimize and validate manipulator motion planning algorithms. The data has been recorded in the field, during our moon-analogue demonstration mission ARCHES on Mt. Etna, Sicily. Each entry contains all information necessary to reproduce the particular motion planning query: Kinematic structure of the robot and environment, start configuration of the joints, and the goal specification of the operation. Overall we publish the log data of over 200 planning queries from different operations: fetching payload from the lander, deploying instruments, and extracting samples in the field.机械臂运动规划（Manipulator Motion Planning）是行星探测领域的核心技术之一，可在极端复杂环境下完成关键操控任务。相较于单纯依赖仿真测试，在真实场景中开展运动规划测试至关重要：其可应对未知挑战，验证算法处理物理动力学特性的有效性，并评估实际约束与局限，从而保障部署的可靠性与高效性。不过，运动规划仿真仍具备重要价值，其可提供可控且高性价比的测试环境，便于在实际部署前开展算法研发工作。而真实场景采集的数据集对仿真研发而言尤为关键，其可提供精准且多元的真实世界数据，助力实现更贴合实际的运动规划算法建模与测试。本数据集旨在为机械臂运动规划算法的研发、优化与验证提供支撑。本次采集的野外数据取自我们在西西里岛埃特纳火山开展的月球模拟演示任务ARCHES。每条数据条目均包含复现特定运动规划查询所需的全部信息：机器人与作业环境的运动学结构、关节初始位姿，以及作业的目标任务规范。本次公开的日志数据共涵盖200余条来自不同作业场景的规划查询记录，作业类型包括从着陆器取回有效载荷、部署科学仪器，以及野外样本采集。