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首页> 外文期刊>Journal of Robotic Systems >Multirepresentation, Multiheuristic A* search-based motion planning for a free-floating underwater vehicle-manipulator system in unknown environment
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Multirepresentation, Multiheuristic A* search-based motion planning for a free-floating underwater vehicle-manipulator system in unknown environment

机译：多种特殊，多思考A *搜索运动规划，用于在未知环境中的自由浮动水下车辆操纵系统

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摘要

A key challenge in autonomous mobile manipulation is the ability to determine, in real time, how to safely execute complex tasks when placed in unknown or changing world. Addressing this issue for Intervention Autonomous Underwater Vehicles (I-AUVs), operating in potentially unstructured environment is becoming essential. Our research focuses on using motion planning to increase the I-AUVs autonomy, and on addressing three major challenges: (a) producing consistent deterministic trajectories, (b) addressing the high dimensionality of the system and its impact on the real-time response, and (c) coordinating the motion between the floating vehicle and the arm. The latter challenge is of high importance to achieve the accuracy required for manipulation, especially considering the floating nature of the AUV and the control challenges that come with it. In this study, for the first time, we demonstrate experimental results performing manipulation in unknown environment. The Multirepresentation, Multiheuristic A* (MR-MHA*) search-based planner, previously tested only in simulation and in a known a priori environment, is now extended to control Girona500 I-AUV performing a Valve-Turning intervention in a water tank. To this aim, the AUV was upgraded with an in-house-developed laser scanner to gather three-dimensional (3D) point clouds for building, in real time, an occupancy grid map (octomap) of the environment. The MR-MHA* motion planner used this octomap to plan, in real time, collision-free trajectories. To achieve the accuracy required to complete the task, a vision-based navigation method was employed. In addition, to reinforce the safety, accounting for the localization uncertainty, a cost function was introduced to keep minimum clearance in the planning. Moreover a visual-servoing method had to be implemented to complete the last step of the manipulation with the desired accuracy. Lastly, we further analyzed the approach performance from both loose-coupling and clearance perspectives. Our results show the success and efficiency of the approach to meet the desired behavior, as well as the ability to adapt to unknown environments.
机译：自主移动操作中的一个关键挑战是能够实时确定如何在未知或更改的世界中安全地执行复杂的任务。解决这一问题的干预自治水下车辆（I-AUV），在潜在的非结构化环境中运营正是必不可少的。我们的研究侧重于使用运动计划来增加I-AUVS自主权，并在解决三个主要挑战：（a）制造一致的确定性轨迹，（b）解决系统的高度维度及其对实时响应的影响， （c）协调浮动车辆和臂之间的运动。后一种挑战具有很高的重要性，以实现操纵所需的准确性，特别是考虑到AUV的浮动性质以及随之而来的控制挑战。在这项研究中，我们首次证明了在未知环境中进行操纵的实验结果。以前仅在仿真和已知的先验环境中仅测试的基于多人形A *（MR-MHA *）搜索的计划员，现在扩展到控制水箱中的阀门转动介入的Girona500 I-AUV控制。为此目的，AUV通过内部开发的激光扫描仪升级，以实时地将三维（3D）点云实时构建，实时占用环境占用网格图（Octomap）。 MR-MHA * MOTION PLANNER使用此Octomap计划，实时进行碰撞轨迹。为实现完成任务所需的准确性，采用了视觉的导航方法。此外，为了加强安全，核算本地化不确定性，提出了一种成本函数，以保持规划中的最低许可。此外，必须实现视觉伺服方法以满足所需精度的操作的最后一步。最后，我们进一步分析了从耦合和清关观点的方法表现。我们的结果表明了满足所需行为的方法的成功和效率，以及适应未知环境的能力。

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