Adaptive Moving-Target Tracking Control of a Vision-Based Mobile Robot via a Dynamic Petri Recurrent Fuzzy Neural Network

2013 ◽  
Vol 21 (4) ◽  
pp. 688-701 ◽  
Author(s):  
Rong-Jong Wai ◽  
You-Wei Lin
Keyword(s):  
Neural Network ◽  
Mobile Robot ◽  
Target Tracking ◽  
Tracking Control ◽  
Fuzzy Neural Network ◽  
Moving Target ◽  
Moving Target Tracking ◽  
Fuzzy Neural ◽  
Recurrent Fuzzy Neural Network

scholarly journals Motion Planning of Autonomous Mobile Robot Using Recurrent Fuzzy Neural Network Trained by Extended Kalman Filter

2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Qidan Zhu ◽  
Yu Han ◽  
Peng Liu ◽  
Yao Xiao ◽  
Peng Lu ◽  
...  
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
Mobile Robot ◽  
Robust Optimization ◽  
Motion Planning ◽  
Mobile Robots ◽  
Extended Kalman Filter ◽  
Fuzzy Neural Network ◽  
Fuzzy Neural ◽  
Recurrent Fuzzy Neural Network

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.

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