论文标题
基于贪婪聚类的算法，用于改善多点机器人操纵测序
Greedy Clustering-Based Algorithm for Improving Multi-point Robotic Manipulation Sequencing
论文作者
论文摘要
优化机器人（也称为多点制造）的一系列任务的问题是一个充分研究的问题。这些解决方案中有许多使用旅行推销员问题（TSP）的变体，并寻求找到最小距离或时间解决方案。最佳解决方案方法难以实时运行，并为更大的问题进行规模运行。在执行任务很快的在线计划应用程序中，优化订购的计算时间可以主导总执行时间。本应用程序中的最佳解决方案定义为计划的计算时间以及执行时间。因此，这里提出的算法通过找到局部最佳序列来平衡解决方案的质量与总执行时间。该算法由航点生成，空间聚类和航点优化组成。在模拟和真实的UR5机器人中，观察到时间减少的显着改善并验证了基本案例算法。
The problem of optimizing a sequence of tasks for a robot, also known as multi-point manufacturing, is a well-studied problem. Many of these solutions use a variant of the Traveling Salesman Problem (TSP) and seek to find the minimum distance or time solution. Optimal solution methods struggle to run in real-time and scale for larger problems. In online planning applications where the tasks being executed are fast, the computational time to optimize the ordering can dominate the total execution time. The optimal solution in this application is defined as the computational time for planning plus the execution time. Therefore, the algorithm presented here balances the quality of the solution with the total execution time by finding a locally optimal sequence. The algorithm is comprised of waypoint generation, spatial clustering, and waypoint optimization. Significant improvements in time reduction were seen and validated against a base case algorithm in simulation and on a real UR5 robot.
