Exterior International Space Station (ISS) visual inspection currently requires astronaut extravehicular activity (EVA), a safety risk. Free-flying space robots can perform visual inspection but risk station collision and high astronaut overhead for teleoperation. Existing inspection planners do not effectively co-optimize inspection coverage and energy consumption with consideration of both orbital dynamics and human supervisor situation awareness. This paper presents an inspection trajectory generation pipeline that integrates orbital dynamics with robot coverage path planning methods to assure collision avoidance and investigate situation awareness. Inspection trajectories meet thrust and space robot dynamics constraints while achieving 98% coverage with 17 grams of fuel on a space station model scaled to the ISS. Pareto front analysis balances fuel consumption with coverage directly. Presented solutions show that paths vary as a function of coverage versus energy prioritization. Methods in this paper contribute towards reducing risk posed to astronaut safety during space station operation and maintenance by providing trajectory generation algorithms towards external semi-autonomous in-orbit inspection of complex space structures.

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Paper submitted to IEEE Robotics and Automation Letters (RA-L) Febuary 3, 2025.

Future spacecraft in zero-gravity proximity operations are expected to host autonomous planning and control capabilities. Available path planning approaches have high computational complexity or are not specifically designed for use in zero-gravity environments, particularly given multi-step inspection activities. This paper extends the Rapidly-exploring Random Tree (RRT) algorithm to a Rapidly-exploring Random Tree for Zero-gravity (RRTZ) formulation designed to efficiently generate low-energy paths in zero-gravity. RRTZ exploits drag-free locomotion for constant velocity path segments along with the tree expansion structure employed in RRT to generate paths with minimal change in local path heading. RRTZ, RRT* (an asymptotically optimal extension of RRT), and a modified version of RRT* are applied to several three-dimensional zero-gravity environments with obstacles of varying geometries. RRTZ produces paths of lower average cost, reduced average planning time, and greater robustness (100% task completion) compared to RRT* (71% task completion) and a modified version of RRT* (93% task completion). RRTZ offers a novel RRT-derived formulation for solution discovery in a variety of emerging space robotic planning tasks.

Paper presented at the IEEE Aerospace Conference March 5, 2024

Paper published online May 13, 2024.