提出高效轨迹规划方法,让拖车机器人在复杂环境中安全快速行进。
Tracailer: An Efficient Trajectory Planner for Tractor-Trailer Robots in Unstructured Environments
- 用高阶平滑轨迹表示法建模拖车运动,提升规划效率。
- 相比现有方法,计算速度提升数倍,轨迹曲率更低、时长更短。
- 适合需要精准控制的物流搬运、户外作业等场景使用。
拖车机器人由可驾驶的牵引车和一个或多个非驱动挂车通过铰链连接而成。相较于典型轮式机器人,其运输能力更强,但复杂的运动学、高维状态空间和可变形结构使路径规划更具挑战性。为此,本文提出一种轻量、紧凑且高阶平滑的轨迹表示方法,并构建可高效求解的时空轨迹优化问题。针对可变形结构带来的避障难题,充分利用环境中的无碰撞区域,在连续空间中直接对轨迹施加形变,无需预先通过碰撞自由种子点构造凸近似安全区,避免解空间损失,降低对初始值的依赖。同时,提出多终端快速路径搜索算法生成优化初始值。大量仿真实验表明,本方法相较现有算法实现数倍效率提升,同时保持更低曲率和更短轨迹时长。真实世界实验在室内外场景中完成货物运输、装卸任务,进一步验证了方法的有效性。源码已开源:https://github.com/Tracailer/Tracailer。
原文摘要 · Abstract (English)
The tractor-trailer robot consists of a drivable tractor and one or more non-drivable trailers connected via hitches. Compared to typical car-like robots, the addition of trailers provides greater transportation capability. However, this also complicates motion planning due to the robot's complex kinematics, high-dimensional state space, and deformable structure. To efficiently plan safe, time-optimal trajectories that adhere to the kinematic constraints of the robot and address the challenges posed by its unique features, this paper introduces a lightweight, compact, and high-order smooth trajectory representation for tractor-trailer robots. Based on it, we design an efficiently solvable spatial-temporal trajectory optimization problem. To deal with deformable structures, which leads to difficulties in collision avoidance, we fully leverage the collisionfree regions of the environment, directly applying deformations to trajectories in continuous space. This approach not requires constructing safe regions from the environment using convex approximations through collision-free seed points before each optimization, avoiding the loss of the solution space, thus reducing the dependency of the optimization on initial values. Moreover, a multi-terminal fast path search algorithm is proposed to generate the initial values for optimization. Extensive simulation experiments demonstrate that our approach achieves severalfold improvements in efficiency compared to existing algorithms, while also ensuring lower curvature and trajectory duration. Real-world experiments involving the transportation, loading and unloading of goods in both indoor and outdoor scenarios further validate the effectiveness of our method. The source code is accessible at https://github.com/Tracailer/Tracailer.
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