CRESCENT: Collision-Free Highly-Constrained Trajectory Optimization for Driving on the Moon

Rovers have been a mainstay of planetary exploration missions, significantly expanding ourknowledge in planetary science. However, past rover missions have involved significant human supervisionto oversee rover operations, a state-of-practice that scales poorly for the next generation of missions. Inthis work, we present the development of CRESCENT, a motion planning algorithm developed for theupcoming multi-agent CADRE Lunar rover mission. CRESCENT was designed to safely drive a miniaturerover platform in a highly cluttered unmapped Lunar environment, executing complex motion directivesfrom CADRE’s team-level autonomy while meeting far stricter dynamical and temporal constraints thanexisting on-board planetary rover planning algorithms are capable of satisfying. Our hierarchical approachformulates an efficient numerical trajectory optimization-based motion planning algorithm that makesuse of nonlinear optimization to solve the planning problem in real-time. We demonstrate the efficiencyof our proposed approach through extensive simulations and hardware testing in a representative Lunarenvironment. Following CADRE’s upcoming deployment on the Lunar surface, CRESCENT will be thefirst nonlinear optimization-based trajectory optimization approach used on another celestial body.