Research on Dual-layer Path Planning Method for Lunar Rover Based on Slip Prediction

doi:10.16182/j.issn1004731x.joss.23-1552

Abstract

Abstract:

In response to the challenges faced by lunar rovers in the process of path planning, such as safe obstacle avoidance and target deviation caused by complex terrain, a dual-layer path planning based on slip prediction is proposed. In this approach, flat terrain is adaptively selected to reduce the wheel slip of the lunar rover. The overall complexity of the terrainis calculated using digital elevation information, and a Q-learning algorithm with a three-level reward mechanism is designed to navigate around high-slip areas, achieving global path planning. A depth camera is used to perceive obstacles, a dynamic window method based on adaptive prediction time is designed for autonomous obstacle avoidance, and local path planning is accomplished by introducing local terrain suitability. Numerical and virtual simulation results indicate that this algorithm can efficiently conduct path planning in complex terrain environments, with fast convergence and high safety.

Key words: slip prediction, adaptive prediction time, three-level reward mechanism, comprehensive complexity of terrain, local terrain suitability

CLC Number:

TP391.9

Zhang Xingyu, Wu Baolei, Wang Jun, Hong Miaoying, Wang Jiahui, Qi Yongqiang. Research on Dual-layer Path Planning Method for Lunar Rover Based on Slip Prediction[J]. Journal of System Simulation, 2025, 37(4): 1008-1024.

Figures/Tables 21

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Fig. 8

Table 1

Parameters for terrain accessibility analysis

主要参数	量值
最大安全坡度角/(°)	30
最大越障高度/m	1
$ω 1$	0.4
$ω 2$	0.3
$ω 3$	0.3

Table 1

Table 2

Global path planning parameters

主要参数	量值
学习率	0.2
折扣因子	0.99
贪婪系数	0.9
最大训练次数	2 000
每次训练的最大步数	3 721
禁区距离阈值 $K d$	10
$r a r r i v a l$	-1 000
$r c o l l i s i o n$	1 000
$r f r e e$	-1

Table 2

Fig. 9

Fig. 10

Fig. 11

Table 3

Fig. 12

Table 4

Local path planning parameters

主要参数	量值	主要参数	量值
$w m a x$ /(rad/m)	100	$α 1$	0.005
$L g o a l$ /m	0.3	$α 2$	0.4
$L o b$ /m	0.3	$β$	0.25
$t$ /s	3.5	$γ$	0.1
$m$	10	$λ$	0.003

Table 4

Fig. 13

Fig. 14

Fig. 15

Fig. 16

Fig. 17

References 26

1	李晓婉, 成芳, 赵航, 等. 月球导航的卫星可见性及信号强度分析[J]. 测绘科学, 2022, 47(7): 14-20.
	Li Xiaowan, Cheng Fang, Zhao Hang, et al. Analysis on Visibility and Signal Strength of Satellite for Lunar Navigation[J]. Science of Surveying and Mapping, 2022, 47(7): 14-20.
2	张天翼. 面向在轨应用的火星车滑移行为研究[D]. 长春: 吉林大学, 2022.
	Zhang Tianyi. Research on Slip Behavior of Mars Rover for On-orbit Application[D]. Changchun: Jilin University, 2022.
3	马浩. 基于视觉的月球/火星车滑移率预测研究[D]. 北京: 中国科学院大学(中国科学院遥感与数字地球研究所), 2019.
	Ma Hao. Vision Based Lunar/Mars Rover Slip Prediction Research[D]. Beijing: University of Chinese Academy of Sciences(Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences), 2019.
4	邢琰, 刘祥, 滕宝毅, 等. 月球表面巡视探测自主局部避障规划[J]. 控制理论与应用, 2019, 36(12): 2042-2046.
	Xing Yan, Liu Xiang, Teng Baoyi, et al. Autonomous Local Obstacle Avoidance Path Planning of Lunar Surface Exploration Rovers[J]. Control Theory & Applications, 2019, 36(12): 2042-2046.
5	周兰凤, 杨丽娜, 方华. 基于滑移预测的月球车路径规划研究[J]. 计算机应用与软件, 2019, 36(11): 47-50.
	Zhou Lanfeng, Yang Lina, Fang Hua. Lunar Rover Path Planning Based on Slip Prediction[J]. Computer Applications and Software, 2019, 36(11): 47-50.
6	成毅. 基于DEM的三维地形建模及集输管道路径优化研究[D]. 大庆: 东北石油大学, 2019.
	Cheng Yi. Research on 3D Terrain Modeling Based on DEM and Path Optimization of Gathering Pipeline[D]. Daqing: Northeast Petroleum University, 2019.
7	Hu Jiaming, Hu Yuhui, Lu Chao, et al. Integrated Path Planning for Unmanned Differential Steering Vehicles in Off-road Environment with 3D Terrains and Obstacles[J]. IEEE Transactions on Intelligent Transportation Systems, 2022, 23(6): 5562-5572.
8	Zhang Wei, Wang Naixin, Wu Wenhua. A Hybrid Path Planning Algorithm Considering AUV Dynamic Constraints Based on Improved A^* Algorithm and APF Algorithm[J]. Ocean Engineering, 2023, 285, Part 1: 115333.
9	Liu Chao, Wu Lei, Xiao Wensheng, et al. An Improved Heuristic Mechanism Ant Colony Optimization Algorithm for Solving Path Planning[J]. Knowledge-Based Systems, 2023, 271: 110540.
10	Yang Hui, Qi Jie, Miao Yongchun, et al. A New Robot Navigation Algorithm Based on a Double-layer Ant Algorithm and Trajectory Optimization[J]. IEEE Transactions on Industrial Electronics, 2019, 66(11): 8557-8566.
11	Ning Xuefeng, Li Yuanjia, Liu Zehuai. Improved Genetic Algorithm-based Obstacle Avoidance Path Planning Method for Inspection Robots[C]//2023 2nd International Symposium on Control Engineering and Robotics (ISCER). Piscataway: IEEE, 2023: 346-350.
12	Qi Jie, Yang Hui, Sun Haixin. MOD-RRT*: A Sampling-based Algorithm for Robot Path Planning in Dynamic Environment[J]. IEEE Transactions on Industrial Electronics, 2021, 68(8): 7244-7251.
13	Chi Wenzheng, Ding Zhiyu, Wang Jiankun, et al. A Generalized Voronoi Diagram-based Efficient Heuristic Path Planning Method for RRTs in Mobile Robots[J]. IEEE Transactions on Industrial Electronics, 2022, 69(5): 4926-4937.
14	Ee Soong Low, Ong Pauline, Cheng Yee Low, et al. Modified Q-learning with Distance Metric and Virtual Target on Path Planning of Mobile Robot[J]. Expert Systems with Applications, 2022, 199: 117191.
15	Ee Soong Low, Ong Pauline, Cheng Yee Low. A Modified Q-learning Path Planning Approach Using Distortion Concept and Optimization in Dynamic Environment for Autonomous Mobile Robot[J]. Computers & Industrial Engineering, 2023, 181: 109338.
16	Kobayashi Masato, Zushi Hiroka, Nakamura Tomoaki, et al. Local Path Planning: Dynamic Window Approach with Q-learning Considering Congestion Environments for Mobile Robot[J]. IEEE Access, 2023, 11: 96733-96742.
17	Hun Lee Dhong, Sang Su Lee, Choon Ki Ahn, et al. Finite Distribution Estimation-based Dynamic Window Approach to Reliable Obstacle Avoidance of Mobile Robot[J]. IEEE Transactions on Industrial Electronics, 2021, 68(10): 9998-10006.
18	王旭扬, 梁志伟, 高翔, 等. 基于改进DWA算法的足球机器人局部轨迹规划[J]. 国外电子测量技术, 2023, 42(8): 1-9.
	Wang Xuyang, Liang Zhiwei, Gao Xiang, et al. Local Trajectory Planning of Soccer Robot Based on Improved DWA Algorithm[J]. Foreign Electronic Measurement Technology, 2023, 42(8): 1-9.
19	杨海兰, 祁永强, 吴保磊, 等. 动态环境下基于忆阻强化学习的移动机器人路径规划[J]. 系统仿真学报, 2023, 35(7): 1619-1633.
	Yang Hailan, Qi Yongqiang, Wu Baolei, et al. Path Planning of Mobile Robots Based on Memristor Reinforcement Learning in Dynamic Environment[J]. Journal of System Simulation, 2023, 35(7): 1619-1633.
20	Pei Muleilan, An Hao, Liu Bo, et al. An Improved Dyna-Q Algorithm for Mobile Robot Path Planning in Unknown Dynamic Environment[J]. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2022, 52(7): 4415-4425.
21	黄昱洲, 王立松, 秦小麟. 一种基于深度强化学习的无人小车双层路径规划方法[J]. 计算机科学, 2023, 50(1): 194-204.
	Huang Yuzhou, Wang Lisong, Qin Xiaolin. Bi-level Path Planning Method for Unmanned Vehicle Based on Deep Reinforcement Learning[J]. Computer Science, 2023, 50(1): 194-204.
22	Fox D, Burgard W, Thrun S. The Dynamic Window Approach to Collision Avoidance[J]. IEEE Robotics & Automation Magazine, 1997, 4(1): 23-33.
23	奚玉荣. 基于单目图像的三维火星地形重构方法研究[D]. 长春: 吉林大学, 2022.
	Xi Yurong. Research on 3D Martian Terrain Reconstruction Method Based on Monocular Image[D]. Changchun: Jilin University, 2022.
24	韩浦霞. 基于双目视觉的嫦娥三号月球高程值计算[D]. 天津: 天津理工大学, 2018.
	Han Puxia. The Calculation of the Lunar Elevation Value of Chang'E3 Based on Binocular Vision[D]. Tianjin: Tianjin University of Technology, 2018.
25	巩绪生. 基于三维地形的人机协同路径规划[D]. 长沙: 国防科学技术大学, 2006.
	Gong Xusheng. Human-machine Cooperation Path Planning Based on Three Dimensional Terrain[D]. Changsha: National University of Defense Technology, 2006.
26	郭延宁, 冯振, 马广富, 等. 行星车视觉导航与自主控制进展与展望[J]. 宇航学报, 2018, 39(11): 1185-1196.
	Guo Yanning, Feng Zhen, Ma Guangfu, et al. Advances and Trends in Visual Navigation and Autonomous Control of a Planetary Rover[J]. Journal of Astronautics, 2018, 39(11): 1185-1196.

指标	传统Q-learning算法	Q-learning+ 三级奖励机制	Q-learning+三级奖励机制+退回机制
路径安全性	0.14	0.25	0.21
路径长度/m	48.421 8	47.960 7	47.991 4
运行时间/s	125.33	1 969.40	1 843.20

[1]	Huang Tao, Zhang Zhi, Ding Yujie, Chen Yanbo, Wang Jing, Zhang Wenqian. Robust Emergency Dispatch Method Considering Dynamic Frequency Security and N-k Contingency [J]. Journal of System Simulation, 2025, 37(12): 2981-2993.
[2]	Zhang Runzhao, Chen Yanbo, Huang Tao, Tian Haoxin, Qiang Tuben, Zhang Zhi. Scheduling Method for Virtual Power Plants Based on Analysis and Forecasting of Heterogeneous Load Characteristics [J]. Journal of System Simulation, 2025, 37(12): 2994-3006.
[3]	Yu Xiangxing, Zhao Yandong, Zhang Baolin. Vibration Control of Offshore Wind Turbine Towers Based on Eddy Current Nonlinear Energy Sink [J]. Journal of System Simulation, 2025, 37(12): 3007-3017.
[4]	Li Bin, Wang Yuchuo. Fault Diagnosis Method for Photovoltaic Systems Based on Multi-strategy Fusion [J]. Journal of System Simulation, 2025, 37(12): 3018-3032.
[5]	Li Xiaobin, Hu Bing, Yin Chao, Li Bo, Ma Jun. Spatiotemporal Graph Convolution-based Demand Forecasting and Simulation Analysis for Automotive Parts Supply Chain [J]. Journal of System Simulation, 2025, 37(12): 3060-3074.
[6]	Peng Yi, Lei Yunkui, Yang Qingqing, Li Hui, Wang Jianming. Improved PID Search Algorithm for UAV Path Planning in Mountainous Environments [J]. Journal of System Simulation, 2025, 37(12): 3075-3086.
[7]	Chen Yi, Qiu Sihang, Zhu Zhengqiu, Ji Yatai, Zhao Yong, Ju Rusheng. A Method of Heuristic Human-LLM Collaborative Source Search [J]. Journal of System Simulation, 2025, 37(12): 3112-3127.
[8]	Suo Jingyi, Lu Baihong, Qu Che. Measurement of Luminous Intensity Distribution for Film and Television LED Light Sources and Its Simulation Research in Game Engines [J]. Journal of System Simulation, 2025, 37(12): 3140-3151.
[9]	Gong Jianxing, Hu Hai, Ren Haihui, Wu Ruixiang. Interoperability Model and Application of Military Training System for Combination of Virtuality and Reality [J]. Journal of System Simulation, 2025, 37(12): 3161-3175.
[10]	Xu Zhixia, Wang Rui, Sun Nan, He Bing, Shen Xiaowei, Zhu Xiaofei. Research on Cooperative Interference Allocation of Jamming Resources Based on Improved Genetic Algorithm [J]. Journal of System Simulation, 2025, 37(12): 3176-3189.
[11]	Liu Xiang, Jin Qiankun. Research on PAC-Bayes-Based A2C Algorithm for Multi-objective Reinforcement Learning [J]. Journal of System Simulation, 2025, 37(12): 3212-3223.
[12]	Yang Lanying, Li Chao, Zou Haifeng, Wan Jiangtao, Zhang Renqiang, Liu Hui, Lu Hong. Robot Path Planning Optimization Based on Fusion of Improved Ant Colony Algorithm and A* Algorithm [J]. Journal of System Simulation, 2025, 37(11): 2956-2965.
[13]	Su Xiaoting, Zhang Xiaowei, Tian Yi, Li Qi, Wang Shuaihao. Research on Time Sequence Design Method of Dynamic Simulation Scene for Starlight Navigation [J]. Journal of System Simulation, 2025, 37(11): 2946-2955.
[14]	Zhang Zhili, Liu Jin, Zhou Zhaofa, Liang Zhe, Zhang Yunhao. Research on Temperature Compensation Technology of Fiber Optic Gyroscope based on ISCSO-BP Neural Network Model [J]. Journal of System Simulation, 2025, 37(11): 2904-2917.
[15]	Chen Jitong, Zhou Jiajia, Wu Di, Jiang Hailong. A USV Path Planning Algorithm under Special Environment Based on TD3-RRT [J]. Journal of System Simulation, 2025, 37(11): 2888-2903.