2023/07/07 by Behzad Akbari, Akbari, Behzad, Ya‐Jun Pan +5
Computer Science · Engineering · #FOS: Computer and information sciences #Fluid Dynamics Simulations and Interactions #Robotic Path Planning Algorithms #Robotics (cs.RO) #Vehicle Dynamics and Control Systems
paper · pdf · doi:10.48550/arxiv.2307.03355
openalex publication_date 2023/07/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.