Problem
Research questions and friction points this paper is trying to address.
Free-floating space manipulators
Velocity optimization
Collision-free planning
Motion planning
Pre-grasp interception
Efficient Bezier Velocity Optimization for Free-Floating Space Manipulators
🤖 AI Summary
This study addresses the challenges of obstacle-avoidance grasping and prescribed-time interception for free-floating space manipulators operating on uncontrolled spacecraft. To this end, we propose the FAAVOR planner, which achieves efficient motion control by optimizing Bézier joint velocity curves with a decision dimensionality independent of discretization resolution. The method computes gradients via analytical recursive sensitivity and substantially accelerates optimization through parallel computing, incremental collision detection, and caching techniques. Experimental results demonstrate that the proposed planner attains a 99.8% success rate in point-to-point trajectory planning with an average computation time of only 1.779 seconds. By significantly outperforming baseline methods, FAAVOR effectively reduces computational costs while enhancing task completion rates in complex scenarios.
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Bezier velocity optimization
recursive sensitivities
free-floating space manipulator
motion planning
collision avoidance
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