Zhao Jintao, Liu Tao, Huang Junhao
School of Ocean Engineering and Technology, Sun Yat-sen University & Southern Marine Science and Engineering Guangdong Laboratory, Zhuhai 519000, China.
School of Ocean Engineering and Technology, Sun Yat-sen University & Southern Marine Science and Engineering Guangdong Laboratory, Zhuhai 519000, China; Zhuhai Research Center, Hanjiang National Laboratory, Zhuhai 519000, China; Guangdong Provincial Key Laboratory of Information Technology for Deep Water Acoustics, Zhuhai 519000, China.
ISA Trans. 2025 Jul 11. doi: 10.1016/j.isatra.2025.07.020.
Autonomous Underwater Vehicles (AUVs) face significant challenges in tracking, navigation, and obstacle avoidance-critical aspects for advancing intelligent underwater robotics. This research presents a new navigation technique that combines Guiding Vector Field (GVF) concepts with Model Predictive Path Integral (MPPI) control to improve the precision and efficiency of vectored thruster AUV operating in complex environments. The proposed approach utilizes the AUV's relative positioning and environmental data to generate obstacle avoidance trajectories as desired GVF. Subsequently, MPPI optimization is applied to control inputs, considering dynamic constraints, to achieve effective tracking and obstacle avoidance. Extensive simulation experiments demonstrate the method's efficacy in navigating complex scenarios with non-convex obstacles. In the aspect of path tracking, the tracking error is reduced by 64 %, while maintaining safe distances in various obstacle configurations. Results show that the integrated method successfully combines local optimization prediction capabilities of MPPI with the global velocity planning of GVF, enabling efficient AUV navigation in intricate environments while ensuring the effectiveness and safety of the execution process. The method demonstrates robust performance even under disturbance conditions, maintaining a tracking error of only 0.017 m. This research contributes a solution for AUV operation in challenging maritime settings, with applications in marine surveying, underwater search and rescue, and offshore operations. By addressing key challenges in underwater navigation, this study advances the practical capabilities of AUV technology, paving the way for more efficient and reliable underwater robotic systems.
自主水下航行器（AUV）在跟踪、导航和避障方面面临重大挑战，而这些方面对于推进智能水下机器人技术至关重要。本研究提出了一种新的导航技术，该技术将引导向量场（GVF）概念与模型预测路径积分（MPPI）控制相结合，以提高在复杂环境中运行的矢量推进器AUV的精度和效率。所提出的方法利用AUV的相对定位和环境数据来生成作为期望GVF的避障轨迹。随后，考虑动态约束，将MPPI优化应用于控制输入，以实现有效的跟踪和避障。大量的仿真实验证明了该方法在具有非凸障碍物的复杂场景中导航的有效性。在路径跟踪方面，跟踪误差降低了64%，同时在各种障碍物配置下保持安全距离。结果表明，该集成方法成功地将MPPI的局部优化预测能力与GVF的全局速度规划相结合，使AUV能够在复杂环境中高效导航，同时确保执行过程的有效性和安全性。即使在干扰条件下，该方法也表现出强大的性能，跟踪误差仅为0.017米。本研究为AUV在具有挑战性的海洋环境中的运行提供了一种解决方案，可应用于海洋测量、水下搜索和救援以及海上作业。通过解决水下导航中的关键挑战，本研究提高了AUV技术的实际应用能力，为更高效、可靠的水下机器人系统铺平了道路。
