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Alternative TitleResearch on Mobile Robot Autonomous Tracking and Dynamic Obstacle Avoidance
Thesis Advisor徐方
Keyword同时定位与建图 社会力模型 行人模型 人工势场法 A*算法
Degree Discipline检测技术与自动化装置
Degree Name硕士
Degree Grantor中国科学院沈阳自动化研究所
Place of Conferral沈阳
Other AbstractFirstly, the development status of autonomous tracking and dynamic obstacle avoidance technology for mobile robots is summarized, and the research difficulties and future trends are clarified. Then, in chapter 2, the construction of omnidirectional mobile platform is briefly introduced. Then, three parts of SLAM, pedestrian detection and recognition and path planning are studied. The details are as follows: (1) An omni-directional mobile platform was built. With the advantage that the robot can move in any direction without nonholonomic constraints, it can make the robot move towards the target pedestrian as far as possible in tracking, thus reducing the possibility of tracking loss. This part lays the hardware foundation for the subsequent pedestrian recognition and tracking work. (2) Using the depth camera based ORB-SLAM achieve simultaneous localization and mapping function reconstruction. When the robot is too far away from the target pedestrian or because of interference and loss, the robot can use this part to locate itself and map the perception of the surrounding environment, so that the robot can catch up with the target pedestrian again. (3) This paper presents a pedestrian model for pedestrian detection and recognition. The model uses the directional gradient histogram information to preliminarily detect the possible pedestrian position in the current frame. Then, the actual three-dimensional coordinates of pedestrians are obtained by combining the corresponding depth in the depth map. Then, the social force model is used to predict the desired position of each pedestrian in the previous image frame. In addition, color histogram is extracted for each pedestrian segment. With the above information as the input of the pedestrian model, the pedestrian model matches the current frame with the pedestrian in the previous image to complete pedestrian detection and recognition. Experiments show that the model can accurately recognize the human body when the pedestrians occlude each other in a short time or because the pedestrians are too close to the robot and can only collect local information of the human body. (4) The direction factor is added to the artificial potential field method to improve it, and it is combined with A* algorithm to carry out path planning. Experiments show that the robot can achieve autonomous tracking of the target pedestrian and dynamic collision avoidance of obstacles.
Contribution Rank1
Document Type学位论文
Recommended Citation
GB/T 7714
任恒乐. 移动机器人自主跟踪与动态避障研究[D]. 沈阳. 中国科学院沈阳自动化研究所,2019.
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移动机器人自主跟踪与动态避障研究.pdf(2390KB)学位论文 开放获取CC BY-NC-SAApplication Full Text
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