基于随机k-Opt的协调运动规划
Coordinated Motion Planning Through Randomized k-Opt
摘要：
本文探讨了团队gitastrophe在CG：SHOP 2021挑战赛中采取的方法。挑战是要找到一个序列的同时移动的两个给定的配置，最大限度地减少总距离或makespan（总时间）之间的方形机器人。我们的获胜方法有两个主要组成部分：一个初始化阶段，找到一个好的初始解决方案，和一个$k$ -opt本地搜索阶段，优化这个解决方案。这导致了第一位完成的距离类别和第三位完成的最大跨度类别。
英文摘要：
This paper examines the approach taken by team gitastrophe in the CG:SHOP 2021 challenge. The challenge was to find a sequence of simultaneous moves of square robots between two given configurations that minimized either total distance travelled or makespan (total time). Our winning approach has two main components: an initialization phase that finds a good initial solution, and a $k$-opt local search phase which optimizes this solution. This led to a first place finish in the distance category and a third place finish in the makespan category.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
