基于D~*算法的农用履带机器人路径规划研究
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摘要
针对传统A~*算法在路径规划中的不足,采用了一种实时性更强的D~*算法,与A~*算法不同的是,D~*算法的OPEN列表中包含了弧长代价递增的RAISE和弧长代价递减的LOWE两种状态类型。将传统A~*算法和D~*算法进行仿真试验对比,试验结果表明,D~*算法缩短了搜索长度和搜索时间且收敛速度快、计算量小。同时,在真实环境下进行了导航试验,结果表明机器人能稳定安全的按照规划路径到达目的点,验证了D~*算法的高效率性。
Aiming at the shortcomings of traditional A~* algorithm in path planning, a more real-time D~* algorithm is adopted. Unlike the A~* algorithm, the OPEN list of D~* algorithm includes RAISE with arc length cost increasing and LOWE with arc length decreasing. The traditional A~* algorithm and D~* algorithm were compared by simulation test. The experimental results show that the D~* algorithm shortens the search length and search time and has fast convergence and small calculation. At the same time, the navigation experiment was carried out in the real environment. The results show that the robot can reach the destination point in a stable and safe way according to the planned path, and verify the high efficiency of the D~* algorithm.
Aiming at the shortcomings of traditional A~* algorithm in path planning, a more real-time D~* algorithm is adopted. Unlike the A~* algorithm, the OPEN list of D~* algorithm includes RAISE with arc length cost increasing and LOWE with arc length decreasing. The traditional A~* algorithm and D~* algorithm were compared by simulation test. The experimental results show that the D~* algorithm shortens the search length and search time and has fast convergence and small calculation. At the same time, the navigation experiment was carried out in the real environment. The results show that the robot can reach the destination point in a stable and safe way according to the planned path, and verify the high efficiency of the D~* algorithm.
引文
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[2] 任世军,洪炳熔,黄德海.一种基于栅格扩展的机器人路径规划方法[J].哈尔滨工业大学学报,2001,33(1):68-72.
[3] 周郭许,唐西林.基于栅格模型的机器人路径规划快速算法[J].计算机工程与应用,2006,42(21):197-199.
[4] Ge S S,CUI Y J.Dynamic Motion Planning for Mobile Robots Using Potential Field Method[J].Autonomous Robots,2002,13(3):207-222.
[5] CSISZAR A,DRUST M,DIETZ T,et al.Dynamic and Interactive Path Planning and Collision Avoidance for an Industrial Robot Using Artificial Potential Field Based Method[J].Mechatronics,2011:413-421.
[6] PAN C Z,LAI X Z,YANG S X,et al.An efficient neural network approach to tracking control of an autonomous surface vehicle with unknown dynamics[J].Expert Systems with Applications,2013,40(5):1 629-1 635.
[7] MASOUND A A.Managing the Dynamics of a Harmonic Potential Field-Guided Robot in a Cluttered Environment[J].IEEE Transactions on Industrial Electronics,2009,56(2):488-496.
[8] 李志海,付宜利.基于遗传算法的仿生双足爬壁机器人越障运动规划[J].机器人,2012,34(6):751-757.
[9] 陈雄,赵一路,韩建达.一种改进的机器人路径规划的蚁群算法[J].控制理论与应用,2010,27(6):821-825.
[10] 罗乾又,张华,王姮,等.改进人工势场法在机器人路径规划中的应用[J].计算机工程与设计,2011,32(4):1 411-1 413.
[11] 史久根,李凯业.基于分层改进D～*算法的室内路径规划[J].计算机应用研究,2015,32(12):3 609-3 612.
[12] 张贺,胡越黎,王权,等.基于改进D*算法的移动机器人路径规划[J].工业控制计算机,2016,29(11):73-74.
[13] 陈乐,胡国清,杨光永.D*算法在柔性输送系统路径规划中的应用研究[J].科学技术与工程,2014,14(3):200-203.
[14] 刘荣.自动机器人轨迹控制系统及相关算法研究[D].成都:电子科技大学,2008.
[15] 孟珠李,焦俊,李郑涛,等.基于A*与B样条算法的农用机器人路径规划系统[J].安徽大学学报(自然科学版),2018,42(1):45-53.
[16] 胡国彪.基于CAN总线的电梯群控系统设计与研究[D].武汉:华中科技大学,2009.
[17] 张传斌.基于CAN总线的移动机器人分布式控制系统研究[D].威海:山东大学,2013.
[18] 焦俊,陈靖,乔焰,等.直流电机驱动农用履带机器人轨迹跟踪自适应滑模控制[J].农业工程学报,2018,34(4):64-70.
[19] 焦俊,孔文,辜丽川,等.基于UKF和SMO农用履带机器人滑动参数计算[J].系统仿真学报,2015,27(7):1 577-1 583.
[20] 郭烈,黄晓慧,葛平淑,等.基于反演法的智能车辆弯路换道轨迹跟踪控制[J].吉林大学学报(工学版),2013,43(2):323-328.
[21] MANORATHNA R P,PHAIRATT P,OGUN P,et al.Feature extraction and tracking of a weld joint for adaptive robotic welding[C]//International Conference on Control Automation Robotics & Vision.IEEE,2014:1 368-1 372.
[22] 王红卫,马勇,谢勇,等.基于平滑A^*算法的移动机器人路径规划[J].同济大学学报(自然科学版),2010,38(11):1 647-1 650.
[23] 朱庆保.复杂环境下的机器人路径规划蚂蚁算法[J].Acta Automatica Sinica,2006,32(4):586-593.
[24] 陈天宏,崔天时,李广军.基于遗传算法的采摘机器人轨迹规划[J].农机化研究,2010,32(8):31-34.
[25] 刘天孚,程如意.带精英策略和视觉探测蚁群算法的机器人路径规划[J].计算机应用,2008,28(1):92-93.