论文标题
$ε^*$+：一种在线覆盖路径计划算法，用于能源受限的自动驾驶汽车
$ε^*$+: An Online Coverage Path Planning Algorithm for Energy-constrained Autonomous Vehicles
论文作者
论文摘要
本文介绍了一种新型算法，称为$ε^*$+，用于使用能源受限的自动驾驶汽车对未知环境的在线覆盖路径规划。由于电池尺寸有限，因此能量约束的车辆的操作时间持续时间有限。因此，在执行覆盖轨迹的同时，车辆必须在电池用完之前返回充电站进行充电。在这方面，$ε^*$+算法使车辆能够根据在整个覆盖过程中受到监视的剩余能量撤回充电站。接下来是一个前进轨迹，该轨迹将车辆带到附近未探索的路点，以重新启动覆盖过程，而不是将其带回撤退轨迹的先前左侧。从而减少了整体覆盖时间。提出的$ε^*$+算法是$ε^*$算法的扩展，该算法利用探索性图灵机（ETM）作为主管，用来来回轨迹浏览车辆以进行完整覆盖。 $ε^*$+算法的性能在复杂方案上使用播放器/舞台进行了验证，这是一个高保真的机器人模拟器。
This paper presents a novel algorithm, called $ε^*$+, for online coverage path planning of unknown environments using energy-constrained autonomous vehicles. Due to limited battery size, the energy-constrained vehicles have limited duration of operation time. Therefore, while executing a coverage trajectory, the vehicle has to return to the charging station for a recharge before the battery runs out. In this regard, the $ε^*$+ algorithm enables the vehicle to retreat back to the charging station based on the remaining energy which is monitored throughout the coverage process. This is followed by an advance trajectory that takes the vehicle to a near by unexplored waypoint to restart the coverage process, instead of taking it back to the previous left over point of the retreat trajectory; thus reducing the overall coverage time. The proposed $ε^*$+ algorithm is an extension of the $ε^*$ algorithm, which utilizes an Exploratory Turing Machine (ETM) as a supervisor to navigate the vehicle with back and forth trajectory for complete coverage. The performance of the $ε^*$+ algorithm is validated on complex scenarios using Player/Stage which is a high-fidelity robotic simulator.
