Research On Environment Construction Based On Binocular Vision System And Path Planning Of A Robot

During the research of robot autonomy movement, the core subject is how to perceive the environment and decide their own motion planning. We design and realiz a binocular stereo vision mechanism system firstly. The mechanism of binocular stereo vision system has compact design, its electrical control system is designed based on distributed framework with CAN bus, in which DSP processor acts as the core. The motion controler uses servomotor as driver, which has high precision and flexibility. The binocular stereo vision mechanism system and the hexapod robot make up a perfect hexapod robot stereo vision platform. To acquire the intrinsic and external parameters of the cameras used in binocular system, we achieved monocular and binocular camera calibration respectively and provide ideal stereo image pairs and camera parameters for stereo matching study, the matching results lay the foundation for three-dimension scene construction.The disparity map is the basic for building three-dimensional information, and the matching algorithm is the hot point in researches on stereoscopic vision. Although local optimization stereo matching algorithm is easy to implement, it is usually has low quality which difficult to apply in real application. The global optimization algorithm, which building a high quality disparity map, is an emphases in the research of stereo matching. Currently available globally optimal algorithms can get high precision disparity map, but the matching time is relatively long and need several minutes usually.Two types of stereovision matching algorithm with high precision are designed in this paper. The first algorithm bases on MRF/MAP and use maximum accumulation rule of belief propagation algorithm to obtain disparity. In order to improve the matching speed, an global optimal division algorithm is designed. The algorithm uses optimal segmentation strategy to divide the whole image into a number of small areas, matching each independent split region using OpenMP acceleration tools to reduce overall disparity map matching time. Experimental results show that using optimal segmentation and parallel processing, the matching time of the improved belief propagation algorithm reduce to less than3seconds with overall accuracy of91%. Although global segmentation algorithm accelerate the matching time, it is hard to use in practical application still. To achieved real-time disparity matching, the support points expansion algorithm is designed to speed up the matching time. The algorithm redesigns simple objective function firstly. During the searching of optimal solution, design the support points expansion method. The matching points with a clear matching relationships, which named as support points are searched out. In subsequent step, expansion the neighborhood of the support points and matching the extended space. For the support point give the prior knowledge to the neighbor points, it reducing global solution space, thereby improving the matching speed. According to the results of the experiment, the matching time less than0.5seconds, the whole match accuracy up to78%, although its match quality is relatively lower than the global segmentation algorithms, the matching speed has improved greatly, which satisfied application requirements.After acquire disparity map, the three dimensional scene is recovered. The obstacle recognition method based on Hidden Markov Model is used to classify the scene, which supports environmental information for robot autonomy movement. In the research of robot autonomous motion, design a apposite way polygon construction algorithm and robot motion controller based on potential field function. Apposite way polygon construction algorithm can construct a surrounds polygons of the barrier in a complex environment, generate optimal paths pass through obstacles. Robot motion controller set attracting potential field function and repulsive potential field function as input vector, calculate speed and angle of the robot as output vector. The controller controls the robot movement, so that robots can effectively avoid obstacles and move towards target point; controller set the basic speed of the robot to solve local minimization problem caused by zero potential field. Experiments based on artificial map showed that compared with the artificial potential field-APF and wall-following–Bug, our method acquire better optimization path and real-time performance, with a wide range of practical applications.To reflect the value of the two matching algorithms in hexapod robot experiment, designed a comprehensive perception method. Comprehensive perception method composes with two steps. Using rapid stereo matching method to restore the entire scene3D points, with hidden Markov models to identify passable area. In order to ensure the safety of the robot in passable area, according the corresponding relationship between disparity map and3D points, using the high quality matching method to identify the passable area again to obtain more precise scene information. Experimental results show that compare with fast matching method or high quality matching method alone, comprehensive perception method is more effective in the total path length and total exercise time.To verify the value of our autonomous algorithm, rough terrain recognition algorithm and stereo matching algorithm, the final hexapod robot experiments are designed. Two types of ground is designed namely flat ground and rough ground. The experiments result show that the proposed algorithms have research value on the robot practical application.