Shape-Adaptive Planning and Control for a Deformable Quadrotor

TL;DR

This work addresses the limitations of fixed-size quadrotors in confined spaces by introducing a shape-adaptive planning-and-control framework for deformable UAVs. It integrates a scalable kinodynamic A* front-end that accounts for deformation radius $r$ with a nonlinearly constrained back-end trajectory optimization in both Cartesian and deformation spaces, yielding smooth, collision-free paths. An enhanced adaptive controller combines NMPC with real-time external force estimation and INDI torque compensation to robustly track morphing trajectories, achieving a significant reduction in tracking error. Validation through simulations and real-world experiments demonstrates improved narrow-gap traversal and successful whole-body grasping-transport, highlighting the practical potential of deformable drones for complex autonomous tasks.

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.

Figures (8)

• Figure 1: (a) Shape-adaptive motion planning for deformable traversal and whole-body grasping-transport. (b) Highlight of autonomous deformation enabling traversal through confined areas. (c) Highlight of autonomous grasping-transport through a narrow cross-shaped gap.
• Figure 2: (a-b) Mechanical schematics of the morphing quadrotor named RR: the robot utilizes a single motor and tendon-driven system for dynamic switching between maximum and minimum sizes. (c) RR's hardware platform. (d) RR is capable of whole-body object grasping.
• Figure 3: Comparative schematic diagram of front-end kinodynamic A* pathfinding with max size, min size, and scalable size.
• Figure 4: The schematic diagram of the proposed enhanced adaptive controller.
• Figure 5: A comparison of trajectories between the proposed method and traditional fixed-size motion planning approaches wang2022geometrically.