Guo Fei, Wang Shoukun, Yue Binkai, Wang Junzheng
School of Automation, Beijing Institute of Technology, No. 5 South Zhongguancun Street, Haidian District, Beijing 100081, China.
State Key Laboratory of Intelligent Control and Decision of Complex System at School of Automation, Beijing Institute of Technology, Beijing 100081, China.
Sensors (Basel). 2020 Oct 1;20(19):5614. doi: 10.3390/s20195614.
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.
轮腿混合机器人（WLHR）能够调整高度和轴距配置，以跨越障碍物或在狭窄空间内滚动。与腿式和轮式机器人相比，它凭借增强的环境适应性能，可应用于更具挑战性的移动机器人作业。为充分利用具有并联Stewart机构的轮腿混合机器人的可变形性和可穿越性，本文通过将机器人抽象为可变形包围盒，提出了一种针对具有并联Stewart机构的轮腿混合机器人的优化驱动规划框架。这将提高高自由度机器人的越障能力，通过调整支撑多边形的轴距或躯干高度，而不是采用轮式机器人的固定配置，从而获得更短的路径。在该规划框架中，我们首先基于激光雷达传感器收集的点云数据，提出了一种预先计算的符号距离场（SDF）映射方法以及一种基于KD树的点云融合方法。然后，提出了一种协变梯度优化方法，该方法能在狭窄受限空间中生成平滑、可变形配置且无碰撞的轨迹。最后，以用户定义的驱动速度和位置作为运动输入，在实际条件下有效验证了包括扩展或收缩立足点多边形以及提升躯干等避障动作，证明了我们方法的实用性。我们通过变形配置而非绕过障碍物，分析了所提框架在四种不同地形场景下的成功率。
