一种用于拾取机械臂路径规划的神经符号配置距离函数
A neural signed configuration distance function for path planning of picking manipulators
AI总结：
针对拾取机械臂路径规划问题，提出一种神经符号配置距离函数（nSCDF）作为隐式障碍物表示，通过构建配置空间中的无碰撞球体，将多查询路径规划器中的点替换为球体，从而快速生成无碰撞走廊并利用凸规划优化路径，实验表明该方法在显著减少时间的同时生成接近渐近最优的路径。
摘要：
拾取机械臂是特定任务机器人，与通用机械臂相比自由度较少，在工业中广泛使用。拾取机器人的效率高度依赖于路径规划解决方案，该方案通常基于采样的多查询方法。规划器能够稳健地解决问题，但其对碰撞检测的大量使用限制了在线使用的规划能力。我们通过提出一种新颖的隐式障碍物表示用于路径规划，即神经符号配置距离函数（nSCDF），从而能够在配置空间中形成无碰撞球体。我们使用球体表示重新表述了一种先进的多查询路径规划器，即在图中使用球体而不是点。我们的规划器返回一个无碰撞走廊，这使我们能够使用凸规划生成优化路径。从数值实验中，我们观察到我们的规划器在显著更短的时间内生成接近渐近最优路径规划器的路径。
英文摘要：
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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