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Game-theoretic cooperative coverage using autonomous vehicles

机译：使用自动驾驶汽车进行博弈论的合作报道

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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：本文提出了一种使用博弈论的方法，使用一组自动驾驶汽车对先验未知环境进行协作覆盖。这些自主车辆需要作为自主实体在没有人工监督的情况下协同扫描搜索区域。但是，由于缺乏对确切障碍物位置的先验知识，自动驾驶汽车的轨迹无法离线计算，并且需要在现场发现环境时进行调整。在这方面，合作覆盖方法基于由本地导航和全局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而以降低的计算复杂度实现了实时的局部最优决策，并且ii）全局导航为寻找未开发区域的区域提供了更广阔的视野。该算法可防止自动驾驶汽车陷入局部极小值，这在基于势场的算法中经常遇到。自动驾驶汽车团队中的相邻代理商会交换最新的环境信息以进行协作。只要有足够的运行时间，自动驾驶汽车团队就能在自己的区域内实现完全覆盖。但是，为了进一步提高清洁效率并减少操作时间，提早完成的车辆应参与协助需要帮助的其他人。从这个意义上讲，合作游戏被设计为在参与的代理之间进行游戏，以实现最佳任务重新分配。本文考虑了协同溢油清理的应用。但是，这些概念可以应用于一般类别的覆盖问题。该算法的有效性在高保真播放器/舞台模拟器上的障碍物丰富的环境中使用配备激光的自动驾驶汽车进行了验证。

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