College of Automation, Harbin Engineering University, Harbin 15001, China.
Comput Intell Neurosci. 2019 Jan 29;2019:1934575. doi: 10.1155/2019/1934575. eCollection 2019.
This paper proposes a novel motion planning method for an autonomous ground mobile robot to address dynamic surroundings, nonlinear program, and robust optimization problems. A planner based on the recurrent fuzzy neural network (RFNN) is designed to program trajectory and motion of mobile robots to reach target. And, obstacle avoidance is achieved. In RFNN, inference capability of fuzzy logic and learning capability of neural network are combined to improve nonlinear programming performance. A recurrent frame with self-feedback loops in RFNN enhances stability and robustness of the structure. The extended Kalman filter (EKF) is designed to train weights of RFNN considering the kinematic constraint of autonomous mobile robots as well as target and obstacle constraints. EKF's characteristics of fast convergence and little limit in training data make it suitable to train the weights in real time. Convergence of the training process is also analyzed in this paper. Optimization technique and update strategy are designed to improve the robust optimization of a system in dynamic surroundings. Simulation experiment and hardware experiment are implemented to prove the effectiveness of the proposed method. Hardware experiment is carried out on a tracked mobile robot. An omnidirectional vision is used to locate the robot in the surroundings. Forecast improvement of the proposed method is then discussed at the end.
本文提出了一种新的运动规划方法，用于自主地面移动机器人，以解决动态环境、非线性规划和鲁棒优化问题。设计了一种基于递归模糊神经网络（RFNN）的规划器，用于规划移动机器人的轨迹和运动，以达到目标，并实现避障。在 RFNN 中，模糊逻辑的推理能力和神经网络的学习能力相结合，以提高非线性规划性能。RFNN 中的递归框架具有自反馈循环，增强了结构的稳定性和鲁棒性。设计了扩展卡尔曼滤波器（EKF）来训练 RFNN 的权重，考虑到自主移动机器人的运动学约束以及目标和障碍物约束。EKF 的快速收敛和对训练数据限制小的特点使其适合实时训练权重。本文还分析了训练过程的收敛性。优化技术和更新策略被设计用来提高系统在动态环境中的鲁棒优化。通过仿真实验和硬件实验来验证所提出方法的有效性。硬件实验在履带式移动机器人上进行。使用全向视觉来定位机器人在环境中的位置。最后讨论了所提出方法的预测改进。
