Neural Network Planning Method for Optimal Off-road Configuration of Modular Robots

doi:10.12382/bgxb.2023.0713

Abstract

Abstract:

Wheeled modular robots have many advantages in meeting human needs for unmanned autonomous tasks, and the robot combination configuration has unique advantages in materials handling, mountain obstacle crossing, and other aspects. Therefore, a multi-module robot configuration optimization planning method is proposed. A digital terrain representation is constructed, and a parameterized terrain identification model is established. And then the genetic algorithm is used to construct an optimal configuration of energy consumption and time weighted combination, change the constraint conditions and perform a large number of parallel runs under different terrains to obtain a large number of terrain optimal configuration parameter pairs. The terrain set is constructed as the input set, and the optimal configuration set is constructed as the output set. The optimal combination configuration for any terrain is quickly obtained by training with neural network technology, so that the combination can achieve high success rate and high reliability obstacle crossing motion when facing a three-dimensional complex terrain, while minimizing the energy and time costs. A simulated field terrain is simulated and built through the physics engine platform, the feasibility and performance of the planned configurations are verified, ensuring that each configuration can complete terrain crossing. At the same time, the optimization ability of the planning algorithm is verified. A modular robot prototype is tested, and the crossing of a 2-times wheelbase wide ravine is completed by using a 6×1 rigid connection configuration. The research results show that the proposed method can be used to efficiently plan the combination obstacle crossing configuration that meets the requirements of passability, and optimal time and energy consumption in various terrains.

Key words: wheeled robot, mountain obstacle crossing, configuration optimization, A^* algorithm, genetic algorithm, BP neural network

CLC Number:

TP242.3

DANG Wanying, ZHOU Lelai, LI Yibin, ZHANG Chen. Neural Network Planning Method for Optimal Off-road Configuration of Modular Robots[J]. Acta Armamentarii, 2024, 45(10): 3674-3685.

Figures/Tables 22

Fig.1 Flowchart of system configuration optimization

Fig.2 Schematic diagram of motion model of a four-wheel differential robot

Fig.3 Schematic diagram of configuration turning

Table 1 Input set parameters

参数	数值
沟壑宽度/m	3
横向最大坡度/(°)	5
最大可通行宽度/m	1
纵向最大上升坡度/(°)	15

Table 2 Output set parameters

输出集	表达式
1	$p 1 e 1$
2	$p 2 e 2$
3	1,2

Table 2 Output set parameters

输出集	表达式
1	$p 1 e 1$
2	$p 2 e 2$
3	1,2

Table 3 Parameters of robot module trolley

参数	参数
车身尺寸(长×宽×高)/m	0.7×0.3×0.6
对接机构总长度/m	0.1
车体质量/kg	30
离地间隙/m	0.55
接近角/(°)	75
离去角/(°)	60
运动速度/(m·s^-1)	0.5

Fig.4 Simulation diagram of a 6×1 configuration using a six- module robot

Fig.5 Error values among three sets of predicted and true values

Fig.6 Schematic diagram of side slope path finding

Fig.7 Side slope simulation model

Fig.8 Obstacle crossing process for 2×3 rigid configuration

Fig.9 Configuration z coordinate change curve

Fig.10 State diagrams of lateral overturning in a 1×6 rigid configuration comparative experiment

Fig.11 Schematic diagram of path finding in high terrain and continuous terrain

Fig.12 Schematic diagram of the combination of high terrain and continuous terrain

Fig.13 State diagram of obstacle crossing process for 6×1 flexible configuration

Fig.14 Configuration z coordinate change curve

Fig.15 Contrast test

Table 4 Optimization versus simulated results

构型	通过性	能耗/J	时间/s
1×6刚性	×	×	×
1×6柔性	×	×	×
2×3刚性	√	4837.5	43
2×3柔性	√	4837.5	43
3×2刚性	√	5017.5	44.6
3×2柔性	√	5017.5	44.6
4×3刚性	√	5197.5	46.2
4×3柔性	√	5197.5	46.2
5×2刚性	√	5377.5	47.8
5×2柔性	√	5377.5	47.8
6×1刚性	×	×	×
6×1刚性	×	×	×

Table 5 Mecanum wheel platform parameters

参数	数值
车身尺寸(长×宽×高)/m	0.22×0.26×0.2
对接机构总长度/m	0.07
车体质量/kg	2.9
离地间隙/m	0.05
接近角/(°)	69
离去角/(°)	78
运动速度/(m·s^-1)	1.0

Fig.16 State diagram of 6×1 rigid configuration crossing a gully

Table 6 Neural network operation times

序号	遗传算法	神经网络
1	2.77	1.41
2	3.402	1.58
3	2.46	1.09

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