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.

翻译：拣取机械臂是特定任务型机器人，与通用机械臂相比自由度较少，在工业领域应用广泛。此类机器人的作业效率高度依赖于路径规划方案，通常采用基于采样的多查询方法。虽然规划器能够稳健地求解路径规划问题，但其对碰撞检测的大量依赖限制了在线规划能力。针对这一问题，我们提出了一种新颖的隐式障碍物表征方法——神经符号构型距离函数（nSCDF），该函数能够在构型空间中构建无碰撞球体。利用球体表征方法，我们重新构建了当前先进的多查询路径规划器：在图中使用球体替代传统点集。该规划器可输出无碰撞走廊，进而利用凸优化方法生成优化路径。数值实验表明，该规划器能在显著缩短计算时间的同时，生成接近渐进最优路径规划器质量的路径。