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.

Funded by the Helmholtz Association sustainability challenge iFOODis KA-HSC-06_iFOODis and the strategic future topic ARCHES ZT-0033