论文标题
KC-TSS：一种执行弹性目标搜索的异质机器人团队的算法
KC-TSS: An Algorithm for Heterogeneous Robot Teams Performing Resilient Target Search
论文作者
论文摘要
本文提出了KC-TSS：基于K-Clustered-traveling推销员的搜索，一种失败的弹性路径计划算法，用于在人类环境中执行目标搜索的异质机器人团队。我们将样本路径生成问题分为异质聚类和多个旅行推销员问题。这使我们可以为每个代理提供高质量的候选路径（即最小的回溯，重叠）到信息理论效用函数。首先，我们从地图知识和目标预测模型中生成Waypoint候选者。所有这些候选人都是根据代理的数量及其覆盖空间或覆盖能力的能力聚集的。每个代理商在分配的集群上解决了旅行推销员问题（TSP）实例，然后将候选人送入实用程序功能进行路径选择。我们在室内搜索中进行了大量的凉亭模拟，并在室内搜索中进行了真正的机器人的初步部署，并使用静态目标进行了模拟救援方案。我们将提出的方法与最先进的算法进行比较，并表明我们的方法能够在传教士时间胜过它。我们的方法通过在线重新计算全球团队计划，在单身或多队友失败的情况下提供了韧性。
This paper proposes KC-TSS: K-Clustered-Traveling Salesman Based Search, a failure resilient path planning algorithm for heterogeneous robot teams performing target search in human environments. We separate the sample path generation problem into Heterogeneous Clustering and multiple Traveling Salesman Problems. This allows us to provide high-quality candidate paths (i.e. minimal backtracking, overlap) to an Information-Theoretic utility function for each agent. First, we generate waypoint candidates from map knowledge and a target prediction model. All of these candidates are clustered according to the number of agents and their ability to cover space, or coverage competency. Each agent solves a Traveling Salesman Problem (TSP) instance over their assigned cluster and then candidates are fed to a utility function for path selection. We perform extensive Gazebo simulations and preliminary deployment of real robots in indoor search and simulated rescue scenarios with static targets. We compare our proposed method against a state-of-the-art algorithm and show that ours is able to outperform it in mission time. Our method provides resilience in the event of single or multi teammate failure by recomputing global team plans online.
