CRESCENT: Collision-Free Highly Constrained Trajectory Optimization for Driving on the Moon
Abhishek Cauligi, Keenan Albee, Roland Brockers, Jean-Pierre de la Croix
AI summary
Problem
Future multi-agent Lunar missions require fully onboard autonomy to avoid heavy ground-in-the-loop supervision, but existing planners cannot satisfy tight formation corridors, complex vehicle dynamics, and real-time compute limits in cluttered, unmapped environments.
Approach
The authors developed a hierarchical motion planning framework that formulates navigation as a nonlinear least-squares trajectory optimization problem solved in real time on resource-constrained flight hardware, utilizing signed distance functions and factor graphs to enforce safety and corridor constraints.
Key results
- Hierarchical global and local planners resolve map uncertainty and enforce robust obstacle avoidance
- Real-time nonlinear optimization successfully runs on resource-constrained Snapdragon processors
- First implementation of onboard trajectory optimization and signed distance functions for a celestial body mission
- Validated through extensive simulations and field testing in representative Lunar analog environments
Why it matters
Advances the technology readiness of real-time onboard trajectory optimization, enabling scalable autonomy for future multi-agent planetary exploration missions.
Abstract
Rovers have been a mainstay of planetary exploration missions, significantly expanding our knowledge in planetary science. However, past rover missions have involved significant human supervision to oversee rover operations, a state-of-practice that scales poorly for the next generation of missions. In this work, we present the development of Constrained Roving Exploration via Safe Collision-free and Environment-aware Trajectory optimization (CRESCENT), a motion planning algorithm developed for the upcoming multiagent Cooperative Autonomous Distributed Robotic Exploration (CADRE) Lunar rover mission. CRESCENT was designed to safely drive a miniature rover platform in a highly cluttered unmapped Lunar environment, executing complex motion directives from CADRE’s team-level autonomy while meeting far stricter dynamical and temporal constraints than existing onboard planetary rover planning algorithms are capable of satisfying. Our hierarchical approach formulates an efficient numerical trajectory optimization-based motion planning algorithm that makes use of nonlinear optimization to solve the planning problem in real time. We demonstrate the efficiency of our proposed approach through extensive simulations and hardware testing in a representative Lunar environment. Following CADRE’s upcoming deployment on the Lunar surface, CRESCENT will be the first nonlinear optimization-based trajectory optimization approach used on another celestial body.