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Game-theoretic cooperative coverage using autonomous vehicles

机译：使用自动车辆的游戏理论合作覆盖

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This paper presents a game-theoretic method for cooperative coverage of a priori unknown environments using a team of autonomous vehicles. These autonomous vehicles are required to cooperatively scan the search area without human supervision as autonomous entities. However, due to the lack of a priori knowledge of the exact obstacle locations, the trajectories of autonomous vehicles cannot be computed offline and need to be adapted as the environment is discovered in situ. In this regard, the cooperative coverage method is based upon the concept of multi-resolution navigation that consists of local navigation and global navigation. The main advantages of this algorithm are: i) the local navigation enables real-time locally optimal decisions with a reduced computational complexity by avoiding unnecessary global computations, and ii) the global navigation offers a wider view of the area seeking for unexplored regions. This algorithm prevents the autonomous vehicles from getting trapped into local minima, which is commonly encountered in potential field based algorithms. The neighboring agents among the team of autonomous vehicles exchange the most up-to-date environment information for collaborations. Given sufficient operation time, the team of autonomous vehicles are capable of achieving complete coverage in their own regions. However, in order to further improve cleaning efficiency and reduce operation time, the vehicles that finish early should participate in assisting others that are in need of help. In this sense, a cooperative game is designed to be played among involved agents for optimal task reallocation. This paper considers the cooperative oil spill cleaning application; however the concepts can be applied to general class of coverage problems. The efficacy of the algorithm is validated using autonomous vehicles equipped with lasers in an obstacle-rich environment on the high-fidelity Player/Stage simulator.
机译：本文介绍了使用自动车组的先验未知环境的合作覆盖游戏 - 理论方法。这些自治车辆需要与自治实体的人类监督进行协作扫描搜索区域。然而，由于缺乏精确的障碍物位置的先验知识，无法离线计算自动车辆的轨迹，并且需要随着环境发现的环境而调整。在这方面，协作覆盖方法基于由本地导航和全球导航组成的多分辨率导航的概念。该算法的主要优点是：i）本地导航通过避免不必要的全局计算，并且II）全球导航提供了寻求未探索区域的区域的广泛视图，该算法能够实时与计算复杂性降低。该算法防止自动车辆被困到局部最小值中，这在基于潜在的基于场的算法中通常遇到。自主车组团队中的邻近代理商交换了合作的最新环境信息。鉴于足够的操作时间，自治车组能力能够在自己的地区实现完全覆盖。然而，为了进一步提高清洁效率并减少操作时间，早期完成的车辆应该参与辅助需要帮助的其他人。从这个意义上讲，合作游戏旨在在涉及的代理中播放以获得最佳任务重新分配。本文考虑了合作油泄漏清洁申请;然而，概念可以应用于覆盖问题的一般课程。使用在高保真播放器/舞台模拟器上的避难所的环境中配备激光器的自动车辆进行验证算法的功效。

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