With the advancement of unmanned driving technology, unmanned vehicles' application environment has gradually moved from the structured road surface to the unstructured and complex field environment. The traditional path planning approaches cannot match the path planning needs of unmanned vehicles in the field environment due to the complicated and changing terrain. In this paper, a fast random tree search algorithm is used to find a viable path in an environmental map quickly. By introducing the target probability bias value and the path post-processing method based on the maximum turning radius constraint of the unmanned vehicle into the basic RRT algorithm, and then simulating and comparing the simulation analysis on the 3D raster map of the constructed field environment. The applicability of the RRT algorithm based on the target probability in the field environment is verified, and the algorithm has higher search efficiency, smoother paths planned by the algorithm, and more goal-oriented path generation.
Image processing is a key link in the process of automobile intelligence. Image pretreatment methods are studied, including gray processing, binarization and extraction of regions of interest. Three different methods of gray image are compared, and the maximum interclass variance method is used for binarization processing of images. When the image is too bright or too dark, the recognition is very inaccurate. Then the edge detection and straight line acquisition were studied to identify the lane lines, and the Canny edge detection was studied, including Gaussian filtering smoothing, Sobel operator, non-maximum suppression and double threshold edge extraction, and the Canny algorithm was improved and optimized to study the Hough transform. Hough transform is used to extract the straight lines to recognize the lane lines.
The road condition in the field environment is complex, and the accurate localization of unmanned vehicle is critical to solve the SLAM (Simultaneous Localization and Mapping) problem. In this paper, GPS, IMU and odometer sensors are selected as hardware equipment, and the strategy of combining global positioning and local positioning is formulated. The sensor data fusion is utilized with the more adaptive unscented Kalman filter algorithm as the core. The vehicle experiment is carried out in the real field environment to ensure that the localization system can precisely output the coordinates of unmanned vehicles under the condition of high nonlinearity as well as meet the robustness and accuracy requirements of unmanned vehicle localization in the field environment.
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