In-Orbit Space Structure Inspection Trajectory Generation
Brandon Apodaca, Thor Helgeson, Ella Atkins, Leia Stirling
AI summary
Problem
Existing in-orbit inspection planners fail to integrate orbital dynamics with coverage optimization, neglect fuel efficiency, and lack constraints to support human supervisor situation awareness, forcing reliance on risky astronaut EVAs.
Approach
The method decouples viewpoint generation and ordering using a novel frictionless TSP that accounts for prior orbital states, then feeds these into an optimal control framework to generate dynamically feasible, collision-free trajectories.
Key results
- Achieves 98% coverage with 17 grams of fuel on an ISS-scaled model
- Introduces a frictionless TSP that conditions traversal costs on prior orbital states
- Generates a Pareto front balancing inspection coverage against fuel consumption
- Integrates situation awareness modulation constraints into trajectory planning
Why it matters
Enables safer, semi-autonomous space station maintenance by reducing astronaut EVA risks and cognitive workload through optimized, awareness-aware inspection paths.
Abstract
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 1 that integrates orbital dynamics with robot coverage path planning methods to ensure 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.