Research Of Avoidance-Obstacle Control System Of Robot By Ultrasonic

At present,With the development of robot technology,the robot intelligence becomes more and more important,therefore,it is an important indicator for the robot intelligence to avoid obstacles.This article uses the ultrasonic sensor to realize the control of robotic avoiding obstacles. Firstly, we design an ultrasonic ranging system to probe the environment on every side of the robot, and analysis the obstruction information by building an overall situation coordinate. Secondly,a control algorithm has been worked out to accomplish pattern recognition. Lastly, avoiding obstacle system has been applied on the crawl robot and intelligent wheel chair by the experiments,then independently realizes the avoiding obstacle.The content as follows:with the accuracy of±5 centimeters,Use 51 micro-chip to design a measurement ranging system, which satisfy the requirement of blocker avoiding controls.In order to increase the measurement precision of single ultrasonic sensor and decrease the interference degree produced by multiple ultrasonic sensors,we have adopted the method of many sensors time division measures on the hardware and set the isolation interposed time to decrease system interference on the software.This paper also presents a Fuzzy-CMAC recognition algorithm,which used to achieve autonomous obstacle avoidance of a mobile robot.Through the combination of heuristic fuzzy rules with the CMAC network,a heuristic fuzzy-neuro network is developed. By applying the off-line and unsupervised training in this method,we build up pattern-mapping between ultrasonic sensory input data and velocity command.Thus,the mobile robot realizes continuous and fast motion for obstacle avoidance.Finally,a virtual environment has been built and the emulation has been in progress in MATLAB.In order to verify the effectiveness of these proposed methods,this paper gives the results of obstacle avoidance through the experiments in crawl robot and intelligent wheel chair.The results show that the system meets the requirements of obstacle avoidance.