Research on Lunar Surface Path Planning Optimization Algorithm of Lunar Probes
-
摘要: 针对复杂环境下的月面探测器路径搜索困难的问题,建立多约束的栅格地图模型,研究了一种改进的蚁群算法用于全局路径规划方法。在蚁群算法中加入了参数自适应调整和双向搜索并行策略以提高蚂蚁搜索路径的成功性,并对路径进行了拐角处理,使规划的全局路径更加平滑、安全,使探测器有效地在大规模地图里避开障碍物。仿真试验结果表明,该方法结合全局规划的特点,使探测器可以沿着一条尽可能短而平滑的最优路径快速、安全地到达目标点。Abstract: Aiming at the problem of the path search of the lunar probe in complex terrain environment, a multi-constrained grid map model was established, and an improved ant colony optimization algorithm was used for global path planning. The parameter adaptive adjustment and two-way search parallel strategy were added to the ant colony algorithm to improve the success of the ant search path, and the corners of the path were processed to make the planned global path smoother and safer, making the detector effective in large avoid obstacles on the scale map. Simulation test results show that this path planning method combines the characteristics of global planning, so that the detector can reach the target point quickly and safely along an optimal path that is as short and smooth as possible.
-
表 1 全局路径规划结果比较
算法 平均运算时间/s 最优路径 平均路径 综合长度/m 最优长度/m 拐角个数/m 综合长度/m 最优长度/m 拐角个数/m 本文算法 301.21 271.79 167.68 23 349.31 262.59 43 基本的蚁群算法 268.88 321.17 197.42 45 400.04 284.15 89 A*算法 36.57 382.27 382.27 62 382.27 382.27 62 遗传算法 287.57 - - - - - - 注: 在迭代次数300次之内, 基础的遗传算法无法在本地图上获得有效路径 -
[1] 金晟毅, 李海飞, 彭松, 等. 嫦娥四号巡视器遥操作地面支持系统设计[J]. 航天器工程, 2019, 28(4): 116-124 https://www.cnki.com.cn/Article/CJFDTOTAL-HTGC201904018.htmJIN S Y, LI H F, PENG S, et al. Design of Tele-operation ground support system for Chang′e-4 rover[J]. Spacecraft Engineering, 2019, 28(4): 116-124 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-HTGC201904018.htm [2] SIMMONS R, HENRIKSEN L, CHRISMAN L, et al. Obstacle avoidance and safeguarding for a lunar rover[C]// AIAA Forum on Advanced Developments in Space Robotics. Madison Wisconsin: AIAA, 1996: 213-218 [3] GARRIDO S, MORENO L, MARTÍN F, et al. Fast marching subjected to a vector field-path planning method for mars rovers[J]. Expert Systems with Applications, 2017, 78: 334-346 doi: 10.1016/j.eswa.2017.02.019 [4] SUTOH M, OTSUKI M, WAKABAYASHI S, et al. The right path: comprehensive path planning for lunar exploration rovers[J]. IEEE Robotics & Automation Magazine, 2015, 22(1): 22-33 [5] 束磊. 基于栅格地图的月球车任务层路径规划及平滑处理[D]. 哈尔滨: 哈尔滨工业大学, 2013SHU L. Mission-level path planning and smoothing of lunar rover based on grid map[D]. Harbin: Harbin Institute of Technology, 2013 (in Chinese) [6] 王利敏. 基于A*算法和B样条函数的月球车路径规划研究[D]. 长春: 吉林大学, 2016WANG L M. Path planning of lunar rover based on A star algorithm and B spline function[D]. Changchun: Jilin University, 2016 (in Chinese) [7] 王楷文, 彭松, 刘少创, 等. 基于A*算法优化的月面巡视器路径规划研究[J]. 航天器工程, 2019, 28(1): 19-26 https://www.cnki.com.cn/Article/CJFDTOTAL-HTGC201901003.htmWANG K W, PENG S, LIU S C, et al. Study on path planning of lunar rover based on A* algorithm optimization[J]. Spacecraft Engineering, 2019, 28(1): 19-26 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-HTGC201901003.htm [8] 苏庆华, 李俊韬, 眭晓虹, 等. 基于空间可视性的星球表面巡视路径分析[J]. 航天器工程, 2017, 26(3): 17-22 https://www.cnki.com.cn/Article/CJFDTOTAL-HTGC201703004.htmSU Q H, LI J T, SUI X H, et al. Aster surface patrol path analysis based on space-visibility[J]. Spacecraft Engineering, 2017, 26(3): 17-22 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-HTGC201703004.htm [9] 张鑫. 基于规则DEM的地形识别及路径规划研究[D]. 桂林: 桂林电子科技大学, 2017ZHANG X. Research on terrain recognition and path planning based on regular DEM[D]. Guilin: Guilin University of Electronic Technology, 2017 (in Chinese) [10] 赵芊. 基于地理信息系统的全地形车路径规划技术研究[D]. 北京: 中国航天科技集团公司第一研究院, 2016ZHAO Q. Research on All-terrain vehicle path planning technology based on geographic information system[D]. Beijing: The First Research Institute of China Aerospace Science and Technology Corporation, 2016 (in Chinese) [11] 朱庆保. 动态复杂环境下的机器人路径规划蚂蚁预测算法[J]. 计算机学报, 2005, 28(11): 1898-1906 https://www.cnki.com.cn/Article/CJFDTOTAL-JSJX200511016.htmZHU Q B. Ants predictive algorithm for path planning of robot in a complex dynamic environment[J]. Chinese Journal of Computers, 2005, 28(11): 1898-1906 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JSJX200511016.htm [12] 吴天羿, 许继恒, 刘建永, 等. 多策略蚁群算法求解越野路径规划[J]. 解放军理工大学学报, 2014, 15(2): 158-164 https://www.cnki.com.cn/Article/CJFDTOTAL-JFJL201402010.htmWU T Y, XU J H, LIU J Y, et al. Multi-strategy ant colony algorithm for cross-country path planning[J]. Journal of PLA University of Science and Technology, 2014, 15(2): 158-164 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JFJL201402010.htm [13] 朱庆保, 张玉兰. 基于栅格法的机器人路径规划蚁群算法[J]. 机器人, 2005, 27(2): 132-136 https://www.cnki.com.cn/Article/CJFDTOTAL-JQRR200502007.htmZHU Q B, ZHANG Y L. An ant colony algorithm based on grid method for mobile robot path planning[J]. Robot, 2005, 27(2): 132-136 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JQRR200502007.htm [14] 赵娟平, 高宪文, 符秀辉. 改进蚁群优化算法求解移动机器人路径规划问题[J]. 南京理工大学学报, 2011, 35(5): 637-641 https://www.cnki.com.cn/Article/CJFDTOTAL-NJLG201105010.htmZHAO J P, GAO X W, FU X H. Improved ant colony optimization algorithm for solving path planning problem of mobile robot[J]. Journal of Nanjing University of Science and Technology, 2011, 35(5): 637-641 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-NJLG201105010.htm [15] 万晓凤, 胡伟, 郑博嘉, 等. 基于改进蚁群算法与Morphin算法的机器人路径规划方法[J]. 科技导报, 2015, 33(3): 84-89 https://www.cnki.com.cn/Article/CJFDTOTAL-KJDB201503030.htmWAN X F, HU W, ZHENG B J, et al. Robot path planning method based on improved ant colony algorithm and Morphin algorithm[J]. Science & Technology Review, 2015, 33(3): 84-89 (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-KJDB201503030.htm