Obstacle Avoidance of Multi-Sensor Intelligent Robot Based on Road Sign Detection.

Journal: Sensors (Basel, Switzerland)
Published Date:

Abstract

The existing ultrasonic obstacle avoidance robot only uses an ultrasonic sensor in the process of obstacle avoidance, which can only be avoided according to the fixed obstacle avoidance route. Obstacle avoidance cannot follow additional information. At the same time, existing robots rarely involve the obstacle avoidance strategy of avoiding pits. In this study, on the basis of ultrasonic sensor obstacle avoidance, visual information is added so the robot in the process of obstacle avoidance can refer to the direction indicated by road signs to avoid obstacles, at the same time, the study added an infrared ranging sensor, so the robot can avoid potholes. Aiming at this situation, this paper proposes an intelligent obstacle avoidance design of an autonomous mobile robot based on a multi-sensor in a multi-obstruction environment. A CascadeClassifier is used to train positive and negative samples for road signs with similar color and shape. A multi-sensor information fusion is used for path planning and the obstacle avoidance logic of the intelligent robot is designed to realize autonomous obstacle avoidance. The infrared sensor is used to obtain the environmental information of the ground depression on the wheel path, the ultrasonic sensor is used to obtain the distance information of the surrounding obstacles and road signs, and the information of the road signs obtained by the camera is processed by the computer and transmitted to the main controller. The environment information obtained is processed by the microprocessor and the control command is output to the execution unit. The feasibility of the design is verified by analyzing the distance acquired by the ultrasonic sensor, infrared distance measuring sensors, and the model obtained by training the sample of the road sign, as well as by experiments in the complex environment constructed manually.

Authors

  • Jianwei Zhao
    Department of Mathematics and Information Sciences, China Jiliang University, Hangzhou 310018, Zhejiang Province, PR China. Electronic address: zhaojw@amss.ac.cn.
  • Jianhua Fang
    School of Mechanical Electronic & Information Engineering, China University of Mining and Technology-Beijing, Beijing 100089, China.
  • Shouzhong Wang
    Beijing Special Engineering and Design Institute, Beijing 100028, China.
  • Kun Wang
    CAS Key Laboratory of Molecular Imaging, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
  • Chengxiang Liu
    School of Mechanical Electronic & Information Engineering, China University of Mining and Technology-Beijing, Beijing 100089, China.
  • Tao Han
    Food Science and Engineering College, Beijing University of Agriculture, Beijing, 102206, China.