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首页> 外文期刊>Mechatronics, IEEE/ASME Transactions on >Cooperative Path Planning for Target Tracking in Urban Environments Using Unmanned Air and Ground Vehicles
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Cooperative Path Planning for Target Tracking in Urban Environments Using Unmanned Air and Ground Vehicles

机译：使用无人机和地面车辆在城市环境中进行目标跟踪的合作路径规划

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摘要

As the need for autonomous reconnaissance and surveillance missions in cluttered urban environments has been increasing, this paper describes a cooperative path planning algorithm for tracking a moving target in urban environments using both unmanned air vehicles (UAVs) and unmanned ground vehicles (UGVs). The novelty of the algorithm is that it takes into account vision occlusions due to obstacles in the environment. The algorithm uses a dynamic occupancy grid to model the target state, which is updated by sensor measurements using a Bayesian filter. Based on the current and predicted target behavior, the path planning algorithm for a single vehicle (UAV/UGV) is first designed to maximize the sum of the probability of detection over a finite look-ahead horizon. The algorithm is then extended to multiple vehicle collaboration scenarios, where a decentralized planning algorithm relying on an auction scheme is designed to plan finite look-ahead paths that maximize the sum of the joint probability of detection over all vehicles.
机译：随着在混乱的城市环境中对自动侦察和监视任务的需求不断增加，本文描述了一种协作路径规划算法，用于使用无人机和无人机来跟踪城市环境中的移动目标。该算法的新颖之处在于它考虑了由于环境中的障碍而导致的视觉遮挡。该算法使用动态占用栅格对目标状态建模，该目标状态通过使用贝叶斯滤波器的传感器测量值进行更新。基于当前和预测的目标行为，首先设计用于单个车辆的路径规划算法（UAV / UGV），以在有限的超前视野范围内最大化检测概率的总和。然后将该算法扩展到多个车辆协作方案，在该方案中，设计了一种基于拍卖方案的分散式规划算法，以规划有限的超前路径，从而使所有车辆的联合检测概率之和最大化。

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