Task-Adaptive Design of Modular Aerial Manipulators Under Airflow Exposure Constraints
For roboticists designing aerial manipulators, this work addresses the critical limitation of rotor-induced airflow on task performance, offering a principled method to adapt configurations for diverse tasks.
The paper introduces an optimization-based design framework for modular aerial manipulators that jointly optimizes end-effector placement and platform configuration under airflow exposure constraints, enabling task-adaptive reconfiguration. Scalability experiments and ablation studies validate the framework's effectiveness.
Aerial manipulation with multirotor platforms enables physical interaction in complex environments, but rotor-induced airflow remains a critical limitation for tasks involving airflow-sensitive targets or surroundings. This paper presents an optimization-based design framework for modular aerial manipulators that jointly considers task wrench feasibility, end-effector placement, and airflow exposure constraints. We first introduce a novel categorization of target-side airflow tolerance and formulate the corresponding exposure requirements as geometric constraints. To efficiently model rotor-induced airflow, we introduce a compact cone-sphere envelope that approximates the spreading structure of a quadrotor's airflow while preserving computational tractability for optimization. Building on this formulation, we propose a reconfiguration optimization that adapts a modular aerial manipulator to diverse task wrench requirements while enforcing both target-side airflow exposure and intra-platform airflow interference constraints. Unlike prior designs that assume a fixed end-effector location, the proposed framework optimizes the end-effector placement together with the platform configuration. Scalability experiments and ablation studies validate the effectiveness of the proposed framework.