The problem of the unmanned surface vessel (USV) path planning in static and dynamic obstacle environments is addressed in this paper. Multi-behavior fusion based potential field method is proposed, which contains thr...The problem of the unmanned surface vessel (USV) path planning in static and dynamic obstacle environments is addressed in this paper. Multi-behavior fusion based potential field method is proposed, which contains three behaviors: goal-seeking, boundary-memory following and dynamic-obstacle avoidance. Then, different activation conditions are designed to determine the current behavior. Meanwhile, information on the positions, velocities and the equation of motion for obstacles are detected and calculated by sensor data. Besides, memory information is introduced into the boundary following behavior to enhance cognition capability for the obstacles, and avoid local minima problem caused by the potential field method. Finally, the results of theoretical analysis and simulation show that the collision-free path can be generated for USV within different obstacle environments, and further validated the performance and effectiveness of the presented strategy.展开更多
视觉导航作为移动机器人自主运行的核心技术支撑,其性能直接决定移动机器人的环境感知精度、定位建图可靠性与路径规划的合理性。文章系统综述移动机器人视觉导航的研究进展,围绕视觉传感器、同步定位与地图构建(Simultaneous Localizat...视觉导航作为移动机器人自主运行的核心技术支撑,其性能直接决定移动机器人的环境感知精度、定位建图可靠性与路径规划的合理性。文章系统综述移动机器人视觉导航的研究进展,围绕视觉传感器、同步定位与地图构建(Simultaneous Localization and Mapping,SLAM)和路径规划三大核心环节展开分析:在视觉传感器层面,重点探讨单模态、多模态融合视觉传感器和新型视觉传感器的技术特性与适配场景;在SLAM层面,总结传统几何SLAM、多模态融合SLAM以及神经隐式SLAM的技术演进与性能优势;在路径规划层面,重点介绍传统算法与生物启发算法的特点与适用场景。最后,总结当前技术面临的挑战,并对未来研究方向进行展望,为视觉导航技术的进一步发展提供参考。展开更多
基金financially supported by the National Natural Science Foundation of China(Grant No.51879049)DK-I Dynamic Positioning System Console Project
文摘The problem of the unmanned surface vessel (USV) path planning in static and dynamic obstacle environments is addressed in this paper. Multi-behavior fusion based potential field method is proposed, which contains three behaviors: goal-seeking, boundary-memory following and dynamic-obstacle avoidance. Then, different activation conditions are designed to determine the current behavior. Meanwhile, information on the positions, velocities and the equation of motion for obstacles are detected and calculated by sensor data. Besides, memory information is introduced into the boundary following behavior to enhance cognition capability for the obstacles, and avoid local minima problem caused by the potential field method. Finally, the results of theoretical analysis and simulation show that the collision-free path can be generated for USV within different obstacle environments, and further validated the performance and effectiveness of the presented strategy.
文摘视觉导航作为移动机器人自主运行的核心技术支撑,其性能直接决定移动机器人的环境感知精度、定位建图可靠性与路径规划的合理性。文章系统综述移动机器人视觉导航的研究进展,围绕视觉传感器、同步定位与地图构建(Simultaneous Localization and Mapping,SLAM)和路径规划三大核心环节展开分析:在视觉传感器层面,重点探讨单模态、多模态融合视觉传感器和新型视觉传感器的技术特性与适配场景;在SLAM层面,总结传统几何SLAM、多模态融合SLAM以及神经隐式SLAM的技术演进与性能优势;在路径规划层面,重点介绍传统算法与生物启发算法的特点与适用场景。最后,总结当前技术面临的挑战,并对未来研究方向进行展望,为视觉导航技术的进一步发展提供参考。
