An Uncertainty-driven Sampling-based Online Coverage Path Planner for Seabed Mapping using Marine Robots
An Uncertainty-driven Sampling-based Online Coverage Path Planner for Seabed Mapping using Marine Robots
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DOI：
10.1109/auv53081.2022.9965886
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发表时间：
2022-09
期刊：
影响因子：
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通讯作者：
Mingxi Zhou;Jianguang Shi
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文献类型：
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作者：
Mingxi Zhou;Jianguang Shi
海底测绘是海洋机器人的一个常见应用，它通常被视为机器人学中的覆盖路径规划问题。在基于机器人的调查期间，感知传感器（例如，相机、激光雷达和声纳）的变化，尤其是在水下环境中。因此，在线路径规划是必要的，以适应感测的变化，以实现所需的覆盖率。在本文中，我们提出了一个感测的信心模型和不确定性驱动的采样为基础的在线覆盖路径规划（SO-CPP），以协助现场机器人规划海底测绘和其他调查类型的应用。与传统的割草机模式不同，SO-CPP将基于概率地图选择随机点，该概率地图是基于使用传感置信度模型的现场声纳测量结果更新的。然后，SO-CPP通过使用多变量成本函数确定的边缘成本连接相邻节点来构建图。最后，SO-CPP将选择最佳路线，并使用多变量目标函数生成所需的航路点列表。SO-CPP已在具有实际水深图、6自由度AUV动力学模型和射线跟踪声纳模型的模拟环境中进行了评估。我们已经进行了Monte Carlo模拟与各种环境设置，以验证SO-CPP适用于凸工作空间，非凸工作空间，未知占用的工作空间。因此，CPP被发现优于定期割草机模式调查，减少了高达20%的旅行距离。除此之外，我们观察到关于环境中的障碍物的先验知识对整体旅行距离的影响很小。在本文中，限制和现实世界的实现也讨论了沿着我们的计划在未来。
Seabed mapping is a common application for marine robots, and it is often framed as a coverage path planning problem in robotics. During a robot-based survey, the coverage of perceptual sensors (e.g., cameras, LIDARS and sonars) changes, especially in underwater environments. Therefore, online path planning is needed to accommodate the sensing changes in order to achieve the desired coverage ratio. In this paper, we present a sensing confidence model and a uncertainty-driven sampling-based online coverage path planner (SO-CPP) to assist in-situ robot planning for seabed mapping and other survey-type applications. Different from conventional lawnmower pattern, the SO-CPP will pick random points based on a probability map that is updated based on in-situ sonar measurements using a sensing confidence model. The SO-CPP then constructs a graph by connecting adjacent nodes with edge costs determined using a multi-variable cost function. Finally, the SO-CPP will select the best route and generate the desired waypoint list using a multi-variable objective function. The SO-CPP has been evaluated in a simulation environment with an actual bathymetric map, a 6-DOF AUV dynamic model and a ray-tracing sonar model. We have performed Monte Carlo simulations with a variety of environmental settings to validate that the SO-CPP is applicable to a convex workspace, a non-convex workspace, and unknown occupied workspace. So-CPP is found outperform regular lawnmower pattern survey by reducing the resulting traveling distance by upto 20%. Besides that, we observed that the prior knowledge about the obstacles in the environment has minor effects on the overall traveling distance. In the paper, limitation and real-world implementation are also discussed along with our plan in the future.