{"title":"机器人团队在复杂环境中放置传感器以覆盖区域","authors":"Xu Li, Greg Fletcher, A. Nayak, I. Stojmenovic","doi":"10.1145/2632149","DOIUrl":null,"url":null,"abstract":"Existing solutions to carrier-based sensor placement by a single robot in a bounded unknown Region of Interest (ROI) do not guarantee full area coverage or termination. We propose a novel localized algorithm, named Back-Tracking Deployment (BTD). To construct a full coverage solution over the ROI, mobile robots (carriers) carry static sensors as payloads and drop them at the visited empty vertices of a virtual square, triangular, or hexagonal grid. A single robot will move in a predefined order of directional preference until a dead end is reached. Then it back-tracks to the nearest sensor adjacent to an empty vertex (an “entrance” to an unexplored/uncovered area) and resumes regular forward movement and sensor dropping from there. To save movement steps, the back-tracking is carried out along a locally identified shortcut. We extend the algorithm to support multiple robots that move independently and asynchronously. Once a robot reaches a dead end, it will back-track, giving preference to its own path. Otherwise, it will take over the back-track path of another robot by consulting with neighboring sensors. We prove that BTD terminates within finite time and produces full coverage when no (sensor or robot) failures occur. We also describe an approach to tolerate failures and an approach to balance workload among robots. We then evaluate BTD in comparison with the only competing algorithms SLD [Chang et al. 2009a] and LRV [Batalin and Sukhatme 2004] through simulation. In a specific failure-free scenario, SLD covers only 40--50% of the ROI, whereas BTD covers it in full. BTD involves significantly (80%) less robot moves and messages than LRV.","PeriodicalId":263540,"journal":{"name":"ACM Trans. Sens. 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引用次数: 23
摘要
现有的由单个机器人在有限的未知感兴趣区域(ROI)中放置基于载波的传感器的解决方案不能保证整个区域的覆盖或终止。我们提出了一种新的本地化算法,称为回溯部署(BTD)。为了在ROI上构建全覆盖解决方案,移动机器人(载体)携带静态传感器作为有效载荷,并将它们放置在虚拟正方形、三角形或六边形网格的访问空顶点上。单个机器人将按照预先定义的方向优先顺序移动,直到到达一个死胡同。然后它返回到靠近空顶点(未探索/未覆盖区域的“入口”)的最近的传感器,并从那里恢复正常的向前移动和传感器掉落。为了节省移动步骤,回溯沿着本地识别的快捷方式进行。我们将该算法扩展到支持多个独立异步移动的机器人。一旦机器人进入死胡同,它就会往回走,优先选择自己的路径。否则,它将通过咨询邻近的传感器来接管另一个机器人的反向路径。我们证明了在没有(传感器或机器人)故障发生的情况下,BTD在有限时间内终止并产生全覆盖。我们还描述了一种容忍故障的方法和平衡机器人之间工作负载的方法。然后,我们通过模拟将BTD与唯一的竞争算法SLD [Chang et al. 2009a]和LRV [Batalin and Sukhatme 2004]进行比较。在特定的无故障场景中,SLD仅覆盖ROI的40- 50%,而BTD则覆盖全部ROI。与LRV相比,BTD涉及的机器人移动和信息显著减少(80%)。
Placing Sensors for Area Coverage in a Complex Environment by a Team of Robots
Existing solutions to carrier-based sensor placement by a single robot in a bounded unknown Region of Interest (ROI) do not guarantee full area coverage or termination. We propose a novel localized algorithm, named Back-Tracking Deployment (BTD). To construct a full coverage solution over the ROI, mobile robots (carriers) carry static sensors as payloads and drop them at the visited empty vertices of a virtual square, triangular, or hexagonal grid. A single robot will move in a predefined order of directional preference until a dead end is reached. Then it back-tracks to the nearest sensor adjacent to an empty vertex (an “entrance” to an unexplored/uncovered area) and resumes regular forward movement and sensor dropping from there. To save movement steps, the back-tracking is carried out along a locally identified shortcut. We extend the algorithm to support multiple robots that move independently and asynchronously. Once a robot reaches a dead end, it will back-track, giving preference to its own path. Otherwise, it will take over the back-track path of another robot by consulting with neighboring sensors. We prove that BTD terminates within finite time and produces full coverage when no (sensor or robot) failures occur. We also describe an approach to tolerate failures and an approach to balance workload among robots. We then evaluate BTD in comparison with the only competing algorithms SLD [Chang et al. 2009a] and LRV [Batalin and Sukhatme 2004] through simulation. In a specific failure-free scenario, SLD covers only 40--50% of the ROI, whereas BTD covers it in full. BTD involves significantly (80%) less robot moves and messages than LRV.