In this paper, we present a concurrent and scalable trajectory optimization method to improve the quality of robot-assisted manufacturing. Our method simultaneously optimizes tool orientations, kinematic redundancy, and waypoint timing on input toolpaths with large numbers of waypoints to improve kinematic smoothness while incorporating manufacturing constraints. Differently, existing methods always determine them in a decoupled manner. To deal with the large number of waypoints on a toolpath, we propose a decomposition-based numerical scheme to optimize the trajectory in an out-of-core manner, which can also run in parallel to improve the efficiency. Simulations and physical experiments have been conducted to demonstrate the performance of our method in examples of robot-assisted additive manufacturing.

翻译：本文提出了一种并行且可扩展的轨迹优化方法，旨在提升机器人辅助制造的加工质量。该方法针对包含大量路径点的输入刀具路径，同步优化工具姿态、运动学冗余与路径点时序，在满足制造约束的同时提升运动学平滑性。与现有方法通常采用解耦方式分别确定这些参数不同，我们的方法实现了协同优化。为处理刀具路径上大量路径点带来的计算挑战，我们提出了一种基于分解的数值求解方案，能够以核外计算方式优化轨迹，并支持并行计算以提高效率。通过仿真与物理实验，我们在机器人辅助增材制造的应用案例中验证了所提方法的性能。