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目的:复杂环境中使用传统TEB算法进行导航时，会出现机器人与障碍物贴近运行或是与障碍物“抢位”,并且会出现突然的速度跳变，导致路径不平滑及震荡，给机器人带来碰撞的风险。针对以上问题，提出一种改进TEB算法。方法:首先，对障碍物边界进行膨胀处理，使机器人与障碍物保持安全距离。其次，在算法中加入加加速度约束以减少机器人在运动时的速度跳变，使规划的路径更加平滑。最后，在ROS中进行仿真实验并在阿克曼车体上对改进TEB算法进行实物测试。结果:实验表明改进TEB算法使机器人的震荡次数减少45.5%,线、角加加速度极差分别降低29.1%和31.2%。在复杂环境中算法规划出来的轨迹更加安全平滑且有效，较少机器人在导航过程中出现速度突变。结论:改进TEB算法适用于阿克曼机器人，使其更合理地运动。
Abstract：Aims: When the traditional TEB algorithm is used for navigation in a complex environment, the robot will run close to the obstacle or “grab position” with the obstacle leading to the sudden acceleration and pumpy increasing the risk of collision. To solve these problems, an improved TEB algorithm is proposed. Methods: Firstly, the boundaries of obstacles were expanded to maintain a safe distance between the robot and the obstacles. Secondly, the acceleration constraint was added to the algorithm to reduce the speed jump of the robot during movement and smooth the planned path. Finally, the experiments were carried out in ROS to improve the TEB algorithm on the Ackermann car body. Results: Simulation results showed that the improved TEB algorithm could reduce the vibration number of the robot by 45.5%, and the range of line and angular acceleration by 13.1% and 31.2%.The trajectory planned by the algorithm in the complex environment was safer and smoother, and could effectively reduce the speed mutation of the robot during navigation. Conclusions: The improved TEB algorithm is suitable for the Ackermann robot to help it move more reasonably.
[1] KOTHARI M,POSTLETHWAITE I.A proba-bilistically robust path planning algorithm for UAVs using rapidly-exploring random trees[J].Journal of Intelligent and Robotic Systems,2013,71(2):231-253.
[2] DORIGO M,GAMBARDELLA L M.Ant colony system:A cooperative learning approach to the Traveling Salesman Problem[J].IEEE Transactions on Evolutionary Computation,1997,1(1):53-66.
[3] 曾胜，戴贤君，肖文，等.基于蚁群算法冷链物流运输路径最优化设计[J].中国计量大学学报，2020,31(3):357-362.ZENG S,DAI X J,XIAO W,et al.Optimization design of cold chain logistics transportation path based on ant colony algorithm[J].Journal of China University of Metrology,2020,31(3):357-362.
[4] DIJKSTRA E W.A note on two problems in Connexion with graphs[J].Numerische Mathematik,1959,1(1):269-271.
[5] 刘生伟，马钺，孟树峰，等.改进A*算法的AGV路径规划[J].计算机应用，2019,39(增刊2):41-44.LIU S W,MA Y,MENG S F,et al.Improved A* algorithm for path planning of AGV[J].Journal of Computer Applications,2019,39(suppl 2):41-44.
[6] MONTIEL O,SEPULVEDA R,OROZCO-ROSAS U.Optimal path planning generation for mobile robots using parallel evolutionary artificial potential field[J].Journal of Intelligent & Robotic Systems:Theory & Application,2015,79(2):237-257.
[7] 朱大奇，孙兵，李利.基于生物启发模型的AUV三维自主路径规划与安全避障算法[J].控制与决策，2015,30(5):798-806.ZHU D Q,SUN B,LI L.Algorithm for AUV’s 3D path planning and safe obstacle avoidance based on biological inspired model[J].Control and Decision,2015,30(5):798-806.
[8] FOX D,BURGARD W,THRUN S.The dynamic window approach to collision avoidance[J].IEEE Robotics & Automation Magazine,2022,4(1):23-33.
[9] 周睿，周坤，刘大亮，等.基于动态窗口法与人工势场法融合的差速机器人路径规划算法[J].中国计量大学学报，2023,34(4):556-562.ZHOU R,ZHOU K,LIU D L,et al.A differential robot path planning algorithm combining the dynamic window algorithm and the artificial potential field[J].Journal of China University of Metrology,2023,34(4):556-562.
[10] 王志中.复杂动态环境下自主机器人路径规划研究[J].组合机床与自动化加工技术，2018(1):64-68.WANG Z Z.Automatic robot path planning und-er complicit dynamic environment[J].Modular Machine Tool & Automatic Manufacturing Technique,2018(1):64-68.
[11] 王洪斌，尹鹏衡，郑维，等.基于改进的A*算法与动态窗口法的移动机器人路径规划[J].机器人，2020,42(3):346-353.WANG H B,YIN P H,ZHENG W,et al.Mobile robot path planning based on Improved A* algorithm and dynamic window method[J].Robot,2020,42(3):346-353.
[12] ROESMANN C,FEITEN W,WOESCH T,et al.Trajectory modification considering dynamic constraints of autonomous robots[C]//Robotics Proceedings of ROBOTIK 2012 7th German Conference on Munich.Munich,Germany:VDE,2012:1-6.
[13] 李国涛.轮式机器人运动控制及路径规划[D].马鞍山：安徽工业大学，2019:41-52.LI G T.Wheeled Robot Motion Control and Path Planning[D].Maanshan:Anhui Univers-ity of Technology,2019:41-52.
[14] 余冬冬.移动机器人避障与轨迹规划[D].杭州：浙江大学，2017:37-53.YU D D.Obstacles Avoidance and Traject-ory Planning of Mobile Robots[D].Hangzhou:Zhejiang University,2017:37-53.
[15] 陈奕梅，沈建峰，李柄棋.改进TEB算法的多机器人动态避障策略研究[J].电光与控制，2022,29(5):107-112.CHEN Y M,SHEN J F,LI B Q.On dynamic obstacle avoidance strategy for multi-robot with improved TEB algorithm[J].Electronics Optics & Control,2022,29(5):107-112.
[16] 盖军雄，雷晓春，江泽涛.基于ROS的阿克曼机器人室内导航实现[J].中国体视学与图像分析，2021,26(3):215-225.GAI J X,LEI X C,JIANG Z T.Realization of Ackerman robot indoor navigation based on ROS[J].Chinese Journal of Stereology and Image,2021,26(3):215-225.
[17] 刘超杰，刘洋，张文博，等.融合TEBStanley的移动机器人路径规划控制算法[J].制造业自动化，2023,45(4):180-186.LIU C J,LIU Y,ZHANG W B,et al.Mobile robot path planning and control algorithm incorporating TEB and Stanley[J].Manufacturing Automation,2023,45(4):180-186.
[18] 赵晓东，曹梦颖，宿景芳.TEB算法中机器人平稳避障策略研究[J].无线电工程，2024,54(1):223-229.ZHAO X D,CAO M Y,SU J F.Research on the stability of obstacle avoidance for mobile robots in TEB algorithm[J].Radio Engineering,2024,54(1):223-229.
基本信息:
DOI：
中图分类号:TP242
引用信息:
[1]桂鹏,金英连,廖根兴等.基于改进TEB算法的阿克曼移动机器人[J].中国计量大学学报,2024,35(03):432-442.
基金信息:
浙江省重点研发计划项目(No.2023C03186); 浙江省基础公益计划项目(No.TGG24E050007)