Experimental Validation of a Car-Like Automated Guided Vehicle with Trajectory Tracking, Obstacle Avoidance, and Target Approach

摘要

The proposed hierarchical sliding mode control (HSMC) for the car-like automated guided vehicle (CLAGV) includes two parts: one is virtual control input (VCI), the other is sliding mode tracking control (SMTC) [1]. Moreover, a single software/hardware based platform develops the software for the control, image processing and trajectory planning algorithms, and the hardware for the control signal (e.g., the PWM for driving the motor) and for the sensor inputs (e.g., the decoder for obtaining the position or velocity of motor, the USB interface for capturing the image). The RGB-D vision system can detect the obstacle(s) through the depth image and recognize the specific object through the SURF (Speed-Up Robust Feature) method. The estimated distance with respect to the vehicle can execute the task of obstacle avoidance (OA) and target approach (TA). Finally, the experiments of the straight-line and circular trajectory tracking with the simultaneous OA and TA of the vehicle confirm the effectiveness, efficiency, and robustness of the proposed system.