Shape-Adaptive Planning and Control for a Deformable Quadrotor

TL;DR

This paper introduces a novel shape-adaptive planning and control framework for deformable quadrotors, enabling improved maneuverability and task execution in complex environments through integrated deformation-aware trajectory planning and disturbance-compensating control.

Contribution

It presents a new shape-adaptive trajectory planner with kinodynamic A* search and an enhanced control strategy for deformable drones, advancing autonomous navigation in confined and complex spaces.

Findings

37.3% reduction in trajectory tracking error

Effective in narrow-gap traversal

Validated through simulations and real-world experiments

Abstract

Drones have become essential in various applications, but conventional quadrotors face limitations in confined spaces and complex tasks. Deformable drones, which can adapt their shape in real-time, offer a promising solution to overcome these challenges, while also enhancing maneuverability and enabling novel tasks like object grasping. This paper presents a novel approach to autonomous motion planning and control for deformable quadrotors. We introduce a shape-adaptive trajectory planner that incorporates deformation dynamics into path generation, using a scalable kinodynamic A* search to handle deformation parameters in complex environments. The backend spatio-temporal optimization is capable of generating optimally smooth trajectories that incorporate shape deformation. Additionally, we propose an enhanced control strategy that compensates for external forces and torque disturbances, achieving a 37.3\% reduction in trajectory tracking error compared to our previous work. Our approach is validated through simulations and real-world experiments, demonstrating its effectiveness in narrow-gap traversal and multi-modal deformable tasks.