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

Abhishek Cauligi, Keenan Albee

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.