Efficient velocity planning improves robotic arm motion on moving spacecraft
Efficient Bezier Velocity Optimization for Free-Floating Space Manipulators
Robotics
Summary
Space robots attached to spacecraft that float freely in space have to plan their movements carefully because moving their arms also moves the spacecraft body. The authors developed a new way called FAVOR that plans smooth arm motions by optimizing velocity curves, making sure the robot avoids collisions and reaches targets on time. This method calculates gradients efficiently and runs much faster than older approaches, achieving higher success in tests with different spacecraft models. Their approach helps control these complex robots better while using less computing power.
What this means in practice
- •For space robotic engineers: Plan collision-free, smooth arm motions on spacecraft with unactuated bases more quickly and reliably than traditional methods.
- •For robotics software developers: Integrate efficient velocity curve optimization with analytic gradients to improve real-time motion planning for complex articulated robots.
Authors
Duo Zhang, Zhizhuo Zhang, Xiaoli Bai, Jingjin Yu
Abstract
We present FAVOR (Free-floating Arm Velocity Optimization with Recursive Sensitivities), a planner for collision-free reaching, tracking, and prescribed-time pre-grasp interception on an unactuated spacecraft. It optimizes Bezier joint-velocity curves with linear velocity, acceleration, and continuity constraints. Decision dimension is independent of rollout resolution. Analytical recursive sensitivities provide task and clearance gradients through the coupled base-arm motion. Parallel evaluation, caching, and incremental collision discovery reduce computation. With a seven-DoF arm and five simulated spacecraft models, FAVOR achieves 99.8% point-to-point success with 1.779 s mean computation, versus 62.9% and 50.270 s for an IK-initialized position-spline baseline. Means include failures and timeouts. FAVOR completes 30 of 36 tracking cases, versus 15 for single-step QP, and all 36 interception instances, versus 22 for the spline baseline. A controlled ablation shows that finite differences increase mean planning time 4.7-fold.