用神经网络建模障碍物距离，提升抓取机械臂路径规划速度与效率。
A neural signed configuration distance function for path planning of picking manipulators
- 提出神经符号配置距离函数，隐式表示障碍物空间。
- 规划结果生成无碰撞通道，可快速优化路径。
- 相比最优算法更快，适合在线实时应用。
抓取机械臂是专用机器人，自由度少于通用机械臂，在工业中广泛应用。其作业效率高度依赖路径规划，通常采用基于采样的多查询方法。该方法虽鲁棒，但频繁的碰撞检测限制了在线使用能力。本文提出一种新的隐式障碍物表示方法——神经符号配置距离函数（nSCDF），可在配置空间中构建无碰撞球体。我们将这一球体表示用于重构先进多查询路径规划器：图中不再使用点，而是使用球体。新规划器输出无碰撞通道，可借助凸规划生成优化路径。数值实验表明，所提方法在显著更短时间内生成的路径接近渐近最优规划器的结果。
原文摘要 · Abstract (English)
Picking manipulators are task specific robots, with fewer degrees of freedom compared to general-purpose manipulators, and are heavily used in industry. The efficiency of the picking robots is highly dependent on the path planning solution, which is commonly based on sampling-based multi-query methods. The planner is robustly able to solve the problem, but its heavy use of collision-detection limits the planning capabilities for online use. We approach this problem by presenting a novel implicit obstacle representation for path planning, a neural signed configuration distance function (nSCDF), which allows us to form collision-free balls in the configuration space. We use the ball representation to re-formulate a state of the art multi-query path planner, i.e., instead of points, we use balls in the graph. Our planner returns a collision-free corridor, which allows us to use convex programming to produce optimized paths. From our numerical experiments, we observe that our planner produces paths that are close to those from an asymptotically optimal path planner, in significantly less time.
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