让可变形无人机自动规划路径并实时调整形状，提升狭窄空间穿梭能力。
Shape-Adaptive Planning and Control for a Deformable Quadrotor
- 基于可变形动力学的路径规划，结合动态形变参数搜索最优轨迹。
- 实测轨迹跟踪误差降低37.3%，显著提升控制精度。
- 适合需要灵活变形与复杂任务的无人机应用开发者参考。
无人机在各类场景中已不可或缺，但传统四旋翼机在狭小空间和复杂任务中存在局限。可变形无人机能实时调整形态，有望突破这些限制，增强机动性并实现如物体抓取等新任务。本文提出一种面向可变形四旋翼机的自主运动规划与控制新方法。引入一种考虑形变动力学的自适应轨迹规划器，采用可扩展的运动动力学A*搜索算法，在复杂环境中处理形变参数。后端时空优化可生成平滑且融合形变的最优轨迹。同时提出改进型控制策略，有效补偿外部力与力矩扰动，相比此前工作轨迹跟踪误差降低37.3%。通过仿真与真实实验验证，该方法在窄缝穿越及多模态可变形任务中均表现优异。
原文摘要 · Abstract (English)
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.
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