海流环境下USV的优化路径规划
Optimized Path Planning for USVs under Ocean Currents
AI总结：
本文针对海流环境下的USV路径规划问题，通过引入时空贝叶斯推断跟踪和预测海流的新时空因子改进了GPMP2算法，实现了平滑性、避障和海流因素的优化，有效降低了成本并提升了性能。
摘要：
无人水面艇（USV）在海洋环境中运行时，需考虑海流及其他能耗因素等多种时空因素。本文采用高斯过程运动规划（GPMP2），这是一种在连续和非线性运动规划算法中展现出良好效果的贝叶斯优化方法。本研究通过引入一种新的时空因子，利用时空贝叶斯推断来跟踪和预测海流，从而改进了GPMP2。该算法被应用于USV路径规划，并在充满挑战的环境中实现了对平滑性、避障和海流因素的优化。该成果适用于对USV最优路径规划以最小化成本和优化性能至关重要的海洋实际应用场景。
英文摘要：
Unmanned Surface Vehicles (USVs) in the ocean environment, considering various spatiotemporal factors such as ocean currents and other energy consumption factors. The paper uses Gaussian Process Motion Planning (GPMP2), a Bayesian optimization method that has shown promising results in continuous and nonlinear motion planning algorithms. The proposed work improves GPMP2 by incorporating a new spatiotemporal factor for tracking and predicting ocean currents using a spatiotemporal Bayesian inference. The algorithm is applied to the USV path planning and is shown to optimize for smoothness, obstacle avoidance, and ocean currents in a challenging environment. The work is relevant for practical applications in ocean scenarios where optimal path planning for USVs is essential for minimizing costs and optimizing performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
