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动态环境下仿生水下机器人的实时轨迹规划与跟踪控制

Real-time Trajectory Planning and Tracking Control of Bionic Underwater Robot in Dynamic Environment.

作者信息

Ding Feng, Wang Rui, Zhang Tiandong, Zheng Gang, Wu Zhengxing, Wang Shuo

机构信息

State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China.

School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China.

出版信息

Cyborg Bionic Syst. 2024 May 9;5:0112. doi: 10.34133/cbsystems.0112. eCollection 2024.

DOI:10.34133/cbsystems.0112
PMID:38725972
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11079444/
Abstract

In this article, we study the trajectory planning and tracking control of a bionic underwater robot under multiple dynamic obstacles. We first introduce the design of the bionic leopard cabinet underwater robot developed in our lab. Then, we model the trajectory planning problem of the bionic underwater robot by combining its dynamics and physical constraints. Furthermore, we conduct global trajectory planning for bionic underwater robots based on the temporal-spatial Bezier curves. In addition, based on the improved proximal policy optimization, local dynamic obstacle avoidance trajectory replanning is carried out. In addition, we design the fuzzy proportional-integral-derivative controller for tracking control of the planned trajectory. Finally, the effectiveness of the real-time trajectory planning and tracking control method is verified by comparative simulation in dynamic environment and semiphysical simulation of UWSim. Among them, the real-time trajectory planning method has advantages in trajectory length, trajectory smoothness, and planning time. The error of trajectory tracking control method is controlled around 0.2 m.

摘要

在本文中，我们研究了多动态障碍物环境下仿生水下机器人的轨迹规划与跟踪控制。我们首先介绍了实验室研发的仿生豹纹水下机器人的设计。然后，结合其动力学和物理约束对仿生水下机器人的轨迹规划问题进行建模。此外，基于时空贝塞尔曲线对仿生水下机器人进行全局轨迹规划。另外，基于改进的近端策略优化进行局部动态避障轨迹重规划。同时，设计模糊比例积分微分控制器对规划轨迹进行跟踪控制。最后，通过动态环境下的对比仿真以及UWSim半物理仿真验证了实时轨迹规划与跟踪控制方法的有效性。其中，实时轨迹规划方法在轨迹长度、轨迹平滑度和规划时间方面具有优势。轨迹跟踪控制方法的误差控制在0.2 m左右。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7042/11079444/fae979e483e9/cbsystems.0112.fig.013.jpg
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2
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3
Multiple Bio-Inspired Father-Son Underwater Robot for Underwater Target Object Acquisition and Identification.
用于改进基于点的跟踪和丰富特征表示的增强中心。
Sci Rep. 2025 Mar 3;15(1):5214. doi: 10.1038/s41598-025-88924-2.
用于水下目标物体获取与识别的多仿生父子水下机器人
Micromachines (Basel). 2021 Dec 26;13(1):25. doi: 10.3390/mi13010025.
4
Data-Driven Performance-Prescribed Reinforcement Learning Control of an Unmanned Surface Vehicle.基于数据驱动的无人水面艇性能规定强化学习控制
IEEE Trans Neural Netw Learn Syst. 2021 Dec;32(12):5456-5467. doi: 10.1109/TNNLS.2021.3056444. Epub 2021 Nov 30.
5
Reinforcement Learning-Based Optimal Tracking Control of an Unknown Unmanned Surface Vehicle.基于强化学习的未知无人水面舰艇最优跟踪控制
IEEE Trans Neural Netw Learn Syst. 2021 Jul;32(7):3034-3045. doi: 10.1109/TNNLS.2020.3009214. Epub 2021 Jul 6.
6
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Sensors (Basel). 2018 Nov 9;18(11):3859. doi: 10.3390/s18113859.