School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510640, China.
State Key Laboratory of Smart Manufacturing for Special Vehicles and Transmission System, Inner Mongolia First Machinery Group Co., Ltd., Baotou 014032, China.
Math Biosci Eng. 2021 Jan 5;18(2):1022-1039. doi: 10.3934/mbe.2021055.
With the wide application of unmanned ground vehicles (UGV) in a complex environment, the research on the obstacle avoidance system has gradually become an important research part in the field of the UGV system. Aiming at the complex working environment, a sensor detection system mounted on UGV is designed and the kinematic estimation model of UGV is studied. In order to meet the obstacle avoidance requirements of UGVs in a complex environment, a fuzzy neural network obstacle avoidance algorithm based on multi-sensor information fusion is designed in this paper. MATLAB is used to simulate the obstacle avoidance algorithm. By comparing and analyzing the simulation path of UGV's obstacle avoidance motion under the navigation control of fuzzy controller and fuzzy neural network algorithm, the superiority of the proposed fuzzy neural network algorithm was verified. Finally, the superiority and reliability of the obstacle avoidance algorithm are verified through the obstacle avoidance experiment on the UGV experimental platform.
随着无人地面车辆（UGV）在复杂环境中的广泛应用，对其避障系统的研究逐渐成为 UGV 系统领域的一个重要研究部分。针对复杂的工作环境，设计了安装在 UGV 上的传感器检测系统，并研究了 UGV 的运动学估计模型。为了满足 UGV 在复杂环境中的避障要求，本文设计了一种基于多传感器信息融合的模糊神经网络避障算法。使用 MATLAB 对避障算法进行了仿真。通过比较和分析模糊控制器和模糊神经网络算法导航控制下 UGV 避障运动的仿真路径，验证了所提出的模糊神经网络算法的优越性。最后，通过在 UGV 实验平台上进行避障实验，验证了避障算法的优越性和可靠性。
