Shape-Adaptive Planning and Control for a Deformable Quadrotor
🤖 AI Summary
To address the limited maneuverability and poor task adaptability of conventional quadrotors in confined, complex environments, this paper proposes a real-time motion planning and robust control framework explicitly embedding morphing dynamics. We first formulate the morphing dynamics of a reconfigurable quadrotor directly within the motion planner, enabling shape-aware kinodynamic A* search and spatiotemporal joint optimization—supporting multimodal deformation tasks such as narrow-gap traversal and object grasping. Furthermore, we design a shape-adaptive trajectory generation module coupled with disturbance-compensation-enhanced control. Experimental results demonstrate a 37.3% reduction in trajectory tracking error and successful high-precision autonomous operation under dynamic morphology switching. The framework significantly improves task generalization capability and robustness in constrained spaces.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
Autonomous motion planning for deformable quadrotors in confined spaces
Shape-adaptive trajectory generation incorporating deformation dynamics
Enhanced control strategy reducing trajectory tracking errors
Innovation
Methods, ideas, or system contributions that make the work stand out.
Shape-adaptive trajectory planner with deformation dynamics
Spatio-temporal optimization for smooth deformable trajectories
Enhanced control strategy reducing tracking errors
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