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首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >A Deformable Configuration Planning Framework for a Parallel Wheel-Legged Robot Equipped with Lidar
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A Deformable Configuration Planning Framework for a Parallel Wheel-Legged Robot Equipped with Lidar

机译：一种可变形的配置规划框架用于平行轮腿机器人配备延立雷达

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摘要

The wheel-legged hybrid robot (WLHR) is capable of adapting height and wheelbase configuration to traverse obstacles or rolling in confined space. Compared with legged and wheeled machines, it can be applied for more challenging mobile robotic exercises using the enhanced environment adapting performance. To make full use of the deformability and traversability of WHLR with parallel Stewart mechanism, this paper presents an optimization-driven planning framework for WHLR with parallel Stewart mechanism by abstracting the robot as a deformable bounding box. It will improve the obstacle negotiation ability of the high degree-of-freedoms robot, resulting in a shorter path through adjusting wheelbase of support polygon or trunk height instead of using a fixed configuration for wheeled robots. In the planning framework, we firstly proposed a pre-calculated signed distance field (SDF) mapping method based on point cloud data collected from a lidar sensor and a KD -tree-based point cloud fusion approach. Then, a covariant gradient optimization method is presented, which generates smooth, deformable-configuration, as well as collision-free trajectories in confined narrow spaces. Finally, with the user-defined driving velocity and position as motion inputs, obstacle-avoidancing actions including expanding or shrinking foothold polygon and lifting trunk were effectively testified in realistic conditions, demonstrating the practicability of our methodology. We analyzed the success rate of proposed framework in four different terrain scenarios through deforming configuration rather than bypassing obstacles.
机译：车轮腿混合机器人（WLHR）能够适应高度和轴距配置，以穿越障碍物或在狭窄的空间中滚动。与腿部和轮式机相比，它可以应用于使用增强的环境调整性能的更具挑战性的移动机器人练习。为了充分利用WHLR与平行踩踏机制的可变形性和可移动性，本文通过将机器人作为可变形边界盒向并联机器人提供了一种与平行速度机制的优化驱动的规划框架。它将改善高度自由度机器人的障碍谈判能力，从而通过调节支撑多边形或后备箱高度的轴距而导致较短的路径，而不是使用用于轮式机器人的固定配置。在规划框架中，我们首先提出了一种基于从LIDAR传感器收集的点云数据和基于KD -Tree的点云融合方法的点云数据的预先计算的签名距离字段（SDF）映射方法。然后，提出了一种协调性梯度优化方法，其产生平滑，可变形的配置，以及在密闭的窄空间中的无碰撞轨迹。最后，利用用户定义的驱动速度和位置作为运动输入，在现实条件下有效地证明了包括扩展或缩小多边形和提升躯干的障碍物避免动作，展示了我们方法的实用性。我们通过变形配置而不是绕过障碍，分析了四种不同地形情景中提出的框架的成功率。

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