Whole-body motion planning and safety-critical control for aerial manipulation

TL;DR

This paper introduces a novel whole-body motion planning and safety-critical control framework for aerial manipulators using superquadrics for accurate, collision-aware trajectory generation and safety enforcement.

Contribution

It presents a superquadric-based modeling approach combined with a maximum-clearance planner and a high-order control barrier function for improved safety and efficiency.

Findings

Outperforms sampling-based planners in cluttered environments

Produces faster, safer, and smoother trajectories

Demonstrates robustness on physical hardware

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.