Real-time and efficient path planning is critical for all robotic systems. In particular, it is of greater importance for industrial robots since the overall planning and execution time directly impact the cycle time and automation economics in production lines. While the problem may not be complex in static environments, classical approaches are inefficient in high-dimensional environments in terms of planning time and optimality. Collision checking poses another challenge in obtaining a real-time solution for path planning in complex environments. To address these issues, we propose an end-to-end learning-based framework viz., Path Planning and Collision checking Network (PPCNet). The PPCNet generates the path by computing waypoints sequentially using two networks: the first network generates a waypoint, and the second one determines whether the waypoint is on a collision-free segment of the path. The end-to-end training process is based on imitation learning that uses data aggregation from the experience of an expert planner to train the two networks, simultaneously. We utilize two approaches for training a network that efficiently approximates the exact geometrical collision checking function. Finally, the PPCNet is evaluated in two different simulation environments and a practical implementation on a robotic arm for a bin-picking application. Compared to the state-of-the-art path planning methods, our results show significant improvement in performance by greatly reducing the planning time with comparable success rates and path lengths.

翻译：实时高效的路径规划对所有机器人系统至关重要，尤其对工业机器人而言更为关键，因为整体规划与执行时间直接影响产线的循环周期和自动化经济性。虽然静态环境下的路径规划问题并不复杂，但经典方法在高维环境中存在规划时间与最优性方面的低效问题。碰撞检测在复杂环境下的实时路径规划中构成另一项挑战。针对这些问题，我们提出一种基于端到端学习的框架——路径规划与碰撞检测网络（PPCNet）。该网络通过两个子网络依次计算路径点：第一网络生成候选路径点，第二网络判定该路径点是否位于无碰撞路径段。端到端训练过程基于模仿学习，通过聚合专家规划器经验数据同步训练两个网络。我们采用两种方法训练网络，使其高效逼近精确的几何碰撞检测函数。最后，在两种仿真环境和机械臂料箱抓取实际场景中评估PPCNet。与现有最优路径规划方法相比，本方法在显著降低规划时间的同时保持相近的成功率与路径长度，性能提升显著。