模块化机器人最优越野构型神经网络规划方法

党婉莹; 周乐来; 李贻斌; 张辰

doi:10.12382/bgxb.2023.0713

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模块化机器人最优越野构型神经网络规划方法

更新时间：2025-08-18

- 模块化机器人最优越野构型神经网络规划方法
- Neural Network Planning Method for Optimal Off-road Configuration of Modular Robots
- 兵工学报 2024年45卷第10期页码：3674-3685
- 作者机构：
  
  1. 山东大学控制科学与工程学院, 山东济南 250061
  2. 山东大学智能无人系统教育部工程研究中心, 山东济南 250061
- 作者简介：
  
  *邮箱: zhoulelai@sdu.edu.cn
- 基金信息：
  
  国家自然科学基金项目(61973191)
- DOI：10.12382/bgxb.2023.0713
  中图分类号：
- 收稿：2023-08-01，
  
  网络出版：2024-10-30，
  
  纸质出版：2024-10-31
- 稿件说明：
移动端阅览
党婉莹, 周乐来, 李贻斌, 等. 模块化机器人最优越野构型神经网络规划方法[J]. 兵工学报, 2024,45(10):3674-3685.

Wanying DANG, Lelai ZHOU, Yibin LI, et al. Neural Network Planning Method for Optimal Off-road Configuration of Modular Robots[J]. Acta Armamentarii, 2024, 45(10): 3674-3685.
党婉莹, 周乐来, 李贻斌, 等. 模块化机器人最优越野构型神经网络规划方法[J]. 兵工学报, 2024,45(10):3674-3685. DOI： 10.12382/bgxb.2023.0713.

Wanying DANG, Lelai ZHOU, Yibin LI, et al. Neural Network Planning Method for Optimal Off-road Configuration of Modular Robots[J]. Acta Armamentarii, 2024, 45(10): 3674-3685. DOI： 10.12382/bgxb.2023.0713.

摘要

轮式模块化机器人在满足人类对于无人自主任务需求时具有很多优势

机器人组合体构型在搬运物资、山地越障等方面更具有独特优势

为此提出一种多模块机器人构型优化规划方法。构建数字化地形表达

建立参数化地形辨识模型

运用遗传算法构建能耗与时间加权组合的最优构型

改变约束条件在不同地形下进行大量平行运行得到大量地形-最优构型参数结果对

将地形集合构建为输入集

将最优构型集合构建为输出集

训练借助神经网络技术快速得到面向任意地形的最佳组合体构型

使得组合体在面对三维复杂地形时实现高成功率、高可靠性越障运动

同时将能耗成本和时间成本降至最低。通过物理引擎平台仿真搭建仿真野外地形

对规划得到的构型进行通过性验证和性能测试

各构型均能完成地形跨越

同时验证规划算法的优化能力;搭建模块化机器人样机实物进行实验

以6×1刚性连接构型完成了2倍轴距宽沟壑的跨越。研究结果表明

所提方法能够高效地规划各类地形下满足通过性要求和时间能耗最优的组合体越障构型。

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.

关键词

Keywords

references

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