Research On Methods Of Cognition And Navigation For Mobile Robot In Unstructured Environment

In recent decades, the pace which mobile robots are implemented their pace to expand human's ability and release them from dangerous, hard and dirty work is quickening. More and more mobile robots work in outdoor unstructured environment. Because the unstructured environment, such as field environment, planetary surface et al, has the characteristics of variability, randomness and complexity, human expect more autonomous and intelligent mobile robot. The ability of environment cognitive and navigation avoidance are two basic elements in the autonomous mobile robot and key difficult points as well. This dissertation focuses on the methods of cognitive and navigation avoidance in unstructured environment. An autonomous navigation hardware and software with high portability are developed, then the challenging problems of scene understanding and autonomous navigation are addressed and some solutions are provided. Main results and contributions of this dissertation are as follows:Firstly, the background and significance of research work about this thesis are introduced. Then, the definition and development of mobile robot is surveyed, and some typical series of intelligent mobile robot and its applications are presented. And the research progresses of key technologies in mobile robot autonomous navigation in unknown environment are generalized. Furthermore, on the basis of the research, the difficult problems in autonomous navigation are discussed.In chapter 2, an autonomous navigation system is developed, and the software and hardware structure of this system is introduced. This system provides us ideas to present these methods in this dissertation, and it can be used as an experiment platform to verify methods.The performance of obstacle detection and recognition are two of basic problems for the mobile robot understanding environment, which affect the following sensing result directly. As the unstructured environment is usually ramdom and variability, it is difficult to detect and recognize accurately by the traditional method. In chapter 3, on the basis of analyzing traditional unstructured environment scene segementation methods, we propose an obstacle detection and recognition approach using image information,3-D information and relevance vector machine(RVM). Firstly, the scene is divided into two distinct regions:interest regions and uninterested regions. Then detected obstacle points are clustered into objects on the basis of their three-dimensional information, connectivity. The key obstacle features are described using geometric property. The RVM is used to categorizing the objects into three groups:stone, rock and slope. Extensive experiments show the utility and performance of the proposed approach. According to the large quantity and various shapes of obstacle, the real-time obstacle classification and recognition in the unstructured environment is realized by training RVM Classifier. In various unstructured environments, results of experiments show that this method is highly robustness, better adapt to random changes in complex scene.Different ground materials have different effect on the motion ability of mobile robots. To determine the surface types through visual analysis, feature extraction is the main difficulty which determine the real-time and reliability of the algorithm. In the unstructured environment, random factors, like illumination and dust, may resulting that the same type of surface appear very differently but different types of surface sometimes appear very similar, which brings further difficulties to the surface types identification based on vision method. In the fourth chapter, the paper analyzed the color, texture feature which commonly used in identification of surface type with vision method. An approach using Gabor wavelet and hybrid evolutionary algorithm optimize selection of surface image characteristics, extract surface features with fewest number of image features nodes and Gabor wavelet scales direction. Then, Relevance Vector Machines neruo-based approach to surface type identification is proposed. Results of experiments show that this method is reliability, and the computation is reduced as well.The mobile robot must have the ability of determining weather the terrain is traversable when operating on the unstructured terrain. Compared with navigation task in planar indoor environment, it is more complex to decide the untraversable region. The key terrain characteristics are identified as roughness, openness, slope, discontinuity and hardness. These characteristics are extracted from vision data and spatial information, and are represented in a fuzzy logic framework. Firstly, we define roughness, openness, discontinuity and hardness. In order to correctly perceive slope terrain, its discription model is discussed. Then, a novel method of estimating the slope of terrain using RBF neural network is proposed. Even on the condition that the position the robot observe the slope is unknown, the slope of the terrain can still be estimated correctly, the relative position between the robot and the slope can be obtained as well. Based on this method, we achieved the terrain traversability assessment through fuzzy inference machine, providing decision-making basis for subsequent mobile robot navigationIn order to realize the effective navigation of mobile robot to target position without collision, this thesis proposed a fast navigation and obstacle avoidance algorithm based on hybrid coordination mechanisms and hierarchical behaviors in chapter 6. This method has a hierarchical architecture that divides the controller into several smaller subsystems will reduce the negative effect that a large rule-based may have on real-time performace. Consequently, the neruo-fuzzy navigation behavior controllers are designed, and the performance of these controllers can be solved by using genetic algorithm. Then, the higher behavior is selected according to the result of identigied environment using neurel network. The weigh of fused basic behaviors are decided by computing the matching degree between the current environment and prototype environment. At last, fusing the terrain traversablity assessment, the autonomous navigation system is realized in the unstructured environment. Results of experiments have been shown that the introduced method has good navigation performance, and it is reliable to different environments.Finally, the main innovations of the dissertation are summarized, and the fields for further research are prospected at the end of the dissertation.