Whole-body motion planning and safety-critical control for aerial manipulation
🤖 AI Summary
This work addresses the challenge of whole-body motion planning for aerial manipulators in complex environments, where conventional geometric abstractions are overly conservative and simultaneous satisfaction of dynamic feasibility and collision-free safety remains difficult. We propose a unified planning and control framework integrating superquadric (SQ) geometric modeling with high-order control barrier functions (HOCBFs). Specifically, we design an SQ-plus-proxy surrogate model coupled with a maximum-clearance Voronoi diagram for efficient path planning; pioneer the coupling of SQs with balance manifolds for whole-body collision awareness; and construct a critical-safety controller enforcing thrust limits and multi-obstacle avoidance. Simulation results demonstrate that our method generates faster, smoother, and safer trajectories compared to sampling-based planners. Real-world experiments further validate its robustness and strong simulation-to-hardware consistency.
Application Category
📝 Abstract
Aerial manipulation combines the maneuverability of multirotors with the dexterity of robotic arms to perform complex tasks in cluttered spaces. Yet planning safe, dynamically feasible trajectories remains difficult due to whole-body collision avoidance and the conservativeness of common geometric abstractions such as bounding boxes or ellipsoids. We present a whole-body motion planning and safety-critical control framework for aerial manipulators built on superquadrics (SQs). Using an SQ-plus-proxy representation, we model both the vehicle and obstacles with differentiable, geometry-accurate surfaces. Leveraging this representation, we introduce a maximum-clearance planner that fuses Voronoi diagrams with an equilibrium-manifold formulation to generate smooth, collision-aware trajectories. We further design a safety-critical controller that jointly enforces thrust limits and collision avoidance via high-order control barrier functions. In simulation, our approach outperforms sampling-based planners in cluttered environments, producing faster, safer, and smoother trajectories and exceeding ellipsoid-based baselines in geometric fidelity. Actual experiments on a physical aerial-manipulation platform confirm feasibility and robustness, demonstrating consistent performance across simulation and hardware settings. The video can be found at https://youtu.be/hQYKwrWf1Ak.
Problem
Research questions and friction points this paper is trying to address.
Planning safe dynamically feasible trajectories for aerial manipulators
Overcoming geometric conservatism in whole-body collision avoidance
Ensuring thrust limits and collision avoidance in safety-critical control
Innovation
Methods, ideas, or system contributions that make the work stand out.
Superquadrics model for geometry-accurate obstacle representation
Voronoi-fused equilibrium-manifold maximum-clearance trajectory planner
High-order control barrier functions for safety-critical control
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