Persistent Monitoring with Refueling on a Terrain Using a Team of Aerial and Ground Robots
Persistent Monitoring with Refueling on a Terrain Using a Team of Aerial and Ground Robots
复制标题
使用空中和地面机器人团队对地形进行持续监控和加油
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Pratap Tokekar
中科院分区：
文献类型：
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作者：
Parikshit Maini;Kevin Yu;P. Sujit;Pratap Tokekar
有许多应用程序，如监视和绘图，需要持续监测地形。在这项工作中，我们考虑了一个由空中和地面机器人组成的异构团队，它们的任务是沿着给定的路径监测地形。这两种类型的机器人都配备了摄像头，可以监控其视野范围内的地形。我们还考虑了空中机器人偶尔降落在地形上充电的能力。目标是为所有机器人找到一条路径，以减少所需的时间。由于地形和燃料的限制，机器人的能见度受到限制，因此确定机器人的最佳路线是一个具有挑战性的问题。我们使用1.5维表示模型为该问题设计了一个MILP公式。采用分支切断框架来实现MILP，并设计分离算法来计算有效不等式。我们报告了大量模拟和概念验证现场实验的结果，以显示我们的方法的有效性。
There are many applications such as surveillance and mapping that require persistent monitoring of terrains. In this work, we consider a heterogeneous team of aerial and ground robots that are tasked with monitoring a terrain along a given path. Both types of robots are equipped with cameras that can monitor the terrain within their fields-of-view. We also consider the ability of the aerial robots to land occasionally on the terrain to recharge. The objective is to find a path for all the robots to reduce the time required. Determining optimal routes for the robots is a challenging problem because of constrained visibility due to the terrain and fuel limitations of the robots. We devise an MILP formulation for the problem using a 1.5 dimensional representation model. A branch-and-cut framework is used to implement the MILP and involves the design of a separation algorithm to compute valid inequalities. We report results from extensive simulations and proof-of-concept field experiments to show the efficacy of our approach.