论文标题
为广播机器人团队计划广阔的生物识别和表型数据收集
Planning for Aerial Robot Teams for Wide-Area Biometric and Phenotypic Data Collection
论文作者
论文摘要
这项工作为在部署的生物识别数据收集的多个UAV平台中的联合任务分配和路径计划问题提供了有效且可实施的解决方案。与任务相关的传感要求引起了传统车辆路由问题的不可思议，并具有覆盖范围/传感限制。就像几个多机器人路径计划问题中一样，我们的问题减少到$ m $ tsp的问题。为了驯服与问题相关的计算挑战，我们提出了一个分层解决方案，该解决方案将车辆路由问题与目标分配问题脱离。作为分配问题的切实解决方案，我们使用基于聚类的技术，该技术将时间不确定性纳入机器人的基数和位置。最后，我们在我们的多季度平台上实施了所提出的技术。
This work presents an efficient and implementable solution to the problem of joint task allocation and path planning in a multi-UAV platform deployed for biometric data collection in-the-wild. The sensing requirement associated with the task gives rise to an uncanny variant of the traditional vehicle routing problem with coverage/sensing constraints. As is the case in several multi-robot path-planning problems, our problem reduces to an $m$TSP problem. In order to tame the computational challenges associated with the problem, we propose a hierarchical solution that decouples the vehicle routing problem from the target allocation problem. As a tangible solution to the allocation problem, we use a clustering-based technique that incorporates temporal uncertainty in the cardinality and position of the robots. Finally, we implement the proposed techniques on our multi-quadcopter platforms.
