REAL-TIME PATH TRACKING METHOD USING DIFFERENTIAL FLATNESS FOR CAR-LIKE MOBILE ROBOT

Author(s):  
Yanfeng Cong ◽  
Hong Chen ◽  
Bingzhao Gao
2012 ◽  
Vol 24 (2) ◽  
pp. 340-346 ◽  
Author(s):  
Teruyoshi Ogawa ◽  
◽  
Taro Nakamura

An omnidirectional movement mechanism is needed that can move a robot in a narrow complicated passage. However, existing mechanisms cannot achieve stable operations. We noted that a snail uses traveling waves and can achieve a stable operation because of a large landing area. We therefore developed a traveling-wave-type mobile robot (TORoIII) using a snail’s locomotive mechanism. However, the directions of the robot were restricted by the number of units, i.e., the directions corresponded to the number of units. In addition, to use this robot as an autonomous robot, self-localization method and path planning method are required. At present, these methods for this robot have not been proposed. In this study, we propose a new perfectly omnidirectional locomotion strategy for TORoIII. In addition, we propose odometry based on kinematics and path planning method based on potential method. Furthermore, we propose online path tracking method using the odometry. We experimentally confirmed the utility of these proposed methods.


Author(s):  
Huiran Wang ◽  
Qidong Wang ◽  
Wuwei Chen ◽  
Linfeng Zhao ◽  
Dongkui Tan

Model predictive control is one of the main methods used in path tracking for autonomous vehicles. To improve the path tracking performance of the vehicle, a path tracking method based on model predictive control with variable predictive horizon is proposed in this paper. Based on the designed model predictive controller for path tracking, the response analysis of path tracking control system under the different predictive horizons is carried out to clarify the influence of predictive horizon on path tracking accuracy, driving comfort and real-time of the control algorithm. Then, taking the lateral offset, the steering frequency and the real-time of the control algorithm as comprehensive performance indexes, the particle swarm optimization algorithm is designed to realize the adaptive optimization for the predictive horizon. The effectiveness of the proposed method is evaluated via numerical simulation based on Simulink/CarSim and hardware-in-the-loop experiment on an autonomous driving simulator. The obtained results show that the optimized predictive horizon can adapt to the different driving environment, and the proposed path tracking method has good comprehensive performance in terms of path tracking accuracy of the vehicle, driving comfort and real-time.


2017 ◽  
Vol 50 (1) ◽  
pp. 4929-4934 ◽  
Author(s):  
Gábor Csorvási ◽  
Ákos Nagy ◽  
István Vajk

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