School of Software and Microelectronics, Peking University, Beijing 100871, China.
China Waterborne Transport Research Institute, Beijing 100088, China.
Sensors (Basel). 2019 Jan 21;19(2):434. doi: 10.3390/s19020434.
Maritime Autonomous Surface Ships (MASS) with advanced guidance, navigation, and control capabilities have attracted great attention in recent years. Sailing safely and efficiently are critical requirements for autonomous control of MASS. The MASS utilizes the information collected by the radar, camera, and Autonomous Identification System (AIS) with which it is equipped. This paper investigates the problem of optimal motion planning for MASS, so it can accomplish its sailing task early and safely when it sails together with other conventional ships. We develop velocity obstacle models for both dynamic and static obstacles to represent the potential conflict-free region with other objects. A greedy interval-based motion-planning algorithm is proposed based on the Velocity Obstacle (VO) model, and we show that the greedy approach may fail to avoid collisions in the successive intervals. A way-blocking metric is proposed to evaluate the risk of collision to improve the greedy algorithm. Then, by assuming constant velocities of the surrounding ships, a novel Dynamic Programming (DP) method is proposed to generate the optimal multiple interval motion plan for MASS. These proposed algorithms are verified by extensive simulations, which show that the DP algorithm provides the lowest collision rate overall and better sailing efficiency than the greedy approaches.
近年来，具有先进导航、制导与控制能力的海上自主水面船舶（MASS）引起了广泛关注。安全高效地航行是 MASS 自主控制的关键要求。MASS 利用其配备的雷达、摄像机和自动识别系统（AIS）收集的信息。本文研究了 MASS 的最优运动规划问题，以便在与其他常规船舶一起航行时，能够尽早、安全地完成航行任务。我们为动态和静态障碍物开发了速度障碍模型，以表示与其他物体无潜在冲突的区域。基于速度障碍（VO）模型提出了一种基于贪婪区间的运动规划算法，但我们表明，贪婪方法可能无法在连续区间内避免碰撞。提出了一种路径阻塞度量来评估碰撞风险，以改进贪婪算法。然后，通过假设周围船舶的恒定速度，提出了一种新的动态规划（DP）方法，为 MASS 生成最优的多区间运动规划。通过广泛的仿真验证了这些提出的算法，结果表明 DP 算法总体上提供了最低的碰撞率，并且比贪婪方法具有更好的航行效率。
