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

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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

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