cs.ROSep 28, 2026

QuadHand: A Compact Quadrotor Aerial Manipulator with MRC-SDF-Based Whole-Body Motion Planning

Authors: Rui Jin, Ruiyang Liu, Xinhang Xu, Haotian Jin, Yi Wang, Yizhuo Yang, Lihua Xi

Organizations: School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798 · NTU–VinUni Joint Research Laboratory for Embodied AI and Robotics, School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, and VinUniversity, Hanoi, Vietnam

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Abstract

Uncrewed aerial manipulators (UAMs) integrate robotic arms with aerial platforms for three-dimensional physical interaction. However, enlarging the workspace increases arm-induced disturbances, while existing geometric representations face a trade-off between geometric fidelity and computational efficiency in close-proximity interaction. This paper presents QuadHand, a compact quadrotor aerial manipulator with a 3-DoF arm, gripper, and battery-assisted passive CoG compensation module to reduce dominant arm-induced disturbances. We further propose MRC-SDF, a Multi-articulated Robot-Centric Signed Distance Field that preserves fine geometric detail with tractable computation, and a spatiotemporal whole-body trajectory optimization framework that jointly optimizes the quadrotor and manipulator for safe and executable trajectory generation. Simulations and real-world experiments demonstrate safe and executable aerial manipulation in complex environments.

Figures & tables

Fig. 1: The proposed QuadHand system hovering in mid-air.
Fig. 2: An overview of our proposed aerial manipulation system.
Fig. 3: Overview of the UAM platform and the key components in the proposed planning framework. (A) Coordinate definitions. (B) PCGC illustration: as the arm moves, the battery passively rotates, synchronously adjusting the gravity moment arm. (C) End-effector workspace. (D) MRC-SDF illustration, where each link is represented by a precomputed SDF. (E) Penalty term for end-effector insertion: the sampled end-effector poses along the trajectory are encouraged to match a predefined pose.
Fig. 4: Trajectory tracking experiments. (A) Experimental configurations. (B) Long-exposure image of the figure-eight trajectory and the corresponding position tracking curves. (C) Tracking curves with payload under configurations with and without PCGC. (D) Distribution of three-dimensional absolute tracking errors over three trials of the figure-eight trajectory, with three loops per trial. (E) Benchmark comparison setup. (F) Comparison of different geometric representations: SFC, ESDF, and MRC-SDF. (G) Comparison of success rate and average optimization time for successful trials.
Fig. 5: General mobile manipulation tasks: Drone Rescue, Cable Unplugging, and Plier Placement. Each row shows the motion trail, FPV images, external views, and EE position trajectories. Green, red, and blue arrows denote approach, retreat, and object motion, respectively.
Fig. 6: Gap-crossing experiments. (A–D) Gap-crossing grasping: setup, crossing-and-grasping snapshot, FPV image, and position tracking. (E) Long-exposure gap-crossing visualization.

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