基于拓扑路网的多挡无人履带平台路径重规划

doi:10.12382/bgxb.2021.0827

摘要/Abstract

摘要：

针对越野环境中无人履带混合动力平台重规划鲁棒性差、场景适应性弱的问题,提出一种通过融入多目标函数模型进行规划路径重构,采用三次螺旋线对重构路径进行多阶段采样优化来生成重规划路径的方法。该方法主要研究无人履带平台运动过程中行驶路径出现局部动态无解、全局规划被动触发重规划功能、重新生成可达目标点的参考轨迹的策略,重点解决重规划时回退道路行驶的成本最优问题。平台搭载两挡行星自动机械变速器,无人平台在行进过程中可根据行驶速度切换到不同挡位,以应对不同路面条件下的速度需求,保证平台具有较强的可通过性。通过实车平台验证新方法针对不同越野场景,根据通行时间、能量消耗和换挡次数的三参数目标代价函数模型,得到最佳的重规划策略并生成一条最优可通行路径。研究结果表明:基于拓扑路网的多挡无人履带平台路径重规划方法降低了重规划过程的时间和能量成本,在兼备考虑时间、能量以及不同转弯半径和速度时选择适当的挡位,确保平台纵向运动动力性的同时,也充分考虑了时效性和经济性。

关键词: 无人履带混合动力平台, 越野环境, 自动换挡, 重规划, 三次螺旋线

Abstract:

To solve the problem of poor robustness and weak scene adaptability of replanning for unmanned tracked hybrid platforms in the off-road environment, a method that incorporates a multi-objective function model and uses a three-order spiral of multi-stage sampling optimization to reconstruct the planned path is proposed. This method regenerates a reference trajectory that can reach the target when no local path is found in dynamic driving. It focuses on solving the cost optimization problem during the back-off road driving of a unmanned tracked platform. The platform is also equipped with a two-speed planetary automatic mechanical transmission. The unmanned platform can switch to different gears according to the driving speed during the traveling process to meet the speed requirements under different road conditions, and thus it has high adaptability. The real vehicle platform verifies that the method proposed in this study can be used for different off-road scenarios. According to the objective cost function model of the three parameters of travel time, energy consumption, and number of shifts, the best replanning strategy can be obtained, as well as an optimal and passable route. The results show that the path replanning method reduces the time and energy costs of the planning process. The appropriate gear is selected while both time and energy, different turning radiuses and speeds are considered. Thus, the longitudinal movement performance, effectiveness, and economy of the platform are guaranteed.

Key words: unmanned tracked hybrid platform, off-road, automatic shift, replanning, three-order spiral

中图分类号:

TJ810.2

刘龙龙, 陈慧岩, 刘海鸥, 关海杰, 卢佳兴. 基于拓扑路网的多挡无人履带平台路径重规划[J]. 兵工学报, 2023, 44(1): 279-289.

LIU Longlong, CHEN Huiyan, LIU Hai’ou, GUAN Haijie, LU Jiaxing. Path Replanning of Multi-speed Unmanned Tracked Platforms Based on Topological Road Network[J]. Acta Armamentarii, 2023, 44(1): 279-289.

图/表 17

图1 平台动力学模型

Fig.1 Dynamic model of the platform

图2 平台驱动系统单侧结构简图

Fig.2 Single-sided structure of the platform-driven system

表1 无人履带平台系统参数

Table 1 Parameters of the unmanned tracked platform system

参数	数值
长L×宽W×高H/mm	5476×2978×1800
质量m/kg	9360
电机峰值功率/kW	110
电机峰值转矩/(N·m)	800
电机持续转矩/(N·m)	500
i₀	5.5
i_g	2.7/1
r/mm	265

图3 全局双向图示意图

Fig.3 Global bidirectional diagram

图4 道路阻断时重规划场景

Fig.4 Replanning scenario when roads are blocked

图5 触发重规划模块方框图

Fig.5 Block diagram of the replanning triggering module

表2 重规划触发过程相关参数

Table 2 Parameters of the replanning triggering process

参数	数值
l/m	40
D_h/m	8
N/次	20
D_m/m	5
T_wait/s	3

图6 重规划触发流程图

Fig.6 Flow chart of replanning triggering

图7 重规划拓扑关系打断策略

Fig.7 Topological relationship interruption strategy in replanning inreplanning

表3 熵权法赋值结果

Table 3 Entropy method assignment results

子目标参数	熵权e_j	重要性比r_j	权重v_j
P₁	0.9557	1.0441	0.32
P₂	0.9978	1.0000	0.55
P₃	0.9435	1.0129	0.13

图8 试验平台系统组合

Fig.8 Test platform with subsystems

图9 试验场地卫星图

Fig.9 Satellite image of the test site

图10 重规划触发并执行

Fig.10 Replanning triggered and executed

图11 不同路段单目标结果对比

Fig.11 Comparison of single-target results for different road sections

图12 单参数目标下重规划路径结果

Fig.12 Results of path replanning using single parameter

图13 多目标下的重规划路径

Fig.13 Replanning path using multiple parameters

表4 不同道路熵权法试验结果

Table 4 Results of the entropy method for different roads

路段	时间/s	能量/%	换挡次数	综合结果
路段1	278	2.65	3	0.8104
路段2	341	2.42	3	0.8295
路段3	300	3.50	2	0.9187
路段4	309	2.53	2	0.7743

参考文献 20

[1]	陈慧岩, 张玉. 军用地面无人机动平台技术发展综述[J]. 兵工学报, 2014, 35(10):1696-1706. doi: 10.3969/j.issn.1000-1093.2014.10.026
	CHEN H Y, ZHANG Y. An overview of research on military unmanned ground vehicles[J]. Acta Armamentarii, 2014, 35(10): 1696-1706. (in Chinese)
[2]	GANGANATH N, CHENG C T, TSE C K. Rapidly replanning A^*[J] ∥Proceedings of 2016 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery. Chengdu,China:IEEE, 2016:386-389.
[3]	张晓晔, 谢志文, 吴晖. 基于时间栅格法和最优搜索的电网巡检机器人避障路径规划方法[J]. 机械与电子, 2021, 39(9):71-75.
	ZHANG X Y, XIE Z W, WU H. Obstacle avoidance path planning method for power grid inspection robot based on time grid method and optimal search[J]. Machinery & Electronics, 2021, 39(9):71-75. (in Chinese)
[4]	CHEN Y, LI W, QI R. Research and simulation of UAV three-dimensional path replanning in complex environment[C]∥Proceedings of 2021 IEEE Asia-Pacific Conference on Image Processing, Electronics and Computers.Dalian,China:IEEE, 2021:746-751.
[5]	朱杰, 鲁艺, 张辉明. 突发威胁情况下的无人机航迹重规划[J]. 计算机工程与应用, 2018, 54(8):255-259. doi: 10.3778/j.issn.1002-8331.1709-0345
	ZHU J, LU Y, ZHANG H M. Path replanning for UAV in emergent threats[J]. Computer Engineering and Applications, 2018, 54(8):255-259. (in Chinese) doi: 10.3778/j.issn.1002-8331.1709-0345
[6]	邹启杰, 刘世慧, 张跃. 基于强化学习的快速探索随机树特殊环境中路径重规划算法[J]. 控制理论与应用, 2020, 37(8):1737-1748.
	ZOU Q J, LIU S H, ZHANG Y. Rapidly-exploring random tree algorithm for path re-planning based on reinforcement learning under the peculiar environment[J]. Control Theory & Applications, 2020, 37(8): 1737-1748. (in Chinese)
[7]	房立金, 吴政翰, 王怀震. 基于改进RRT*FN算法的机械臂多场景运动规划[J]. 中国机械工程, 2021, 32(21):2590-2597.
	FANG L J, WU Z H, WANG H Z. Multi-scene motion planning of manipulators based on improved RRT*FN algorithm[J]. China Mechanical Engineering, 2021, 32(21):2590-2597. (in Chinese) doi: 10.3969/j.issn.1004-132X.2021.21.008
[8]	丛玉华, 赵宗豪, 邢长达, 等. 基于改进人工势场的无人机动态避障路径规划[J]. 兵器装备工程学报, 2021, 42(9):170-176.
	CONG Y H, ZHAO Z H, XING C D, et al. Dynamic obstacle avoidance path planning of UAV based on improved artificial potential field[J]. Journal of Ordnance Equipment Engineering, 2021, 42(9):170-176. (in Chinese)
[9]	朱大奇, 刘雨, 孙兵, 等. 自治水下机器人的自主启发式生物启发神经网络路径规划算法[J]. 控制理论与应用, 2019, 36(2):183-191.
	ZHU D Q, LIU Y, SUN B, et al. Autonomous underwater vehicles path planning based on autonomous inspired Glasius bio-inspired neural network algorithm[J]. Control Theory & Applications, 2019, 36(2):183-191. (in Chinese)
[10]	孙鹏耀, 黄炎焱, 潘尧. 基于改进势场法的移动机器人路径规划[J]. 兵工学报, 2020, 41(10):2106-2121. doi: 10.3969/j.issn.1000-1093.2020.10.021
	SUN P Y, HUANG Y Y, PAN Y. Path planning of mobile robots based on improved potential field algorithm[J]. Acta Armamentarii, 2020, 41(10):2106-2121. (in Chinese) doi: 10.3969/j.issn.1000-1093.2020.10.021
[11]	赵梓烨, 刘海鸥, 陈慧岩. 分布式电驱动无人高速履带车辆越野环境轨迹预测方法研究[J]. 兵工学报, 2019, 40(4):680-688. doi: 10.3969/j.issn.1000-1093.2019.04.002
	ZHAO Z Y, LIU H O, CHEN H Y. Research on trajectory prediction method of distributed high speed electric drive unmanned tracked vehicle in off-road conditions[J]. Acta Armamentarii, 2019, 40(4):680-688. (in Chinese) doi: 10.3969/j.issn.1000-1093.2019.04.002
[12]	赵屹东, 陈慧岩, 胡家铭. 双侧独立电驱动履带车辆自动机械变速器线控换挡控制方法[J]. 兵工学报, 2021, 42(3):459-467. doi: 10.3969/j.issn.1000-1093.2021.03.002
	ZHAO Y D, CHEN H Y, HU J M. Control method for amt shifting of a shift-by-wire bilateral electric drive tracked vehicle[J]. Acta Armamentarii, 2021, 42(3):459-467. (in Chinese) doi: 10.3969/j.issn.1000-1093.2021.03.002
[13]	于泉, 姚宗含. 动态重规划的多目标路径产生方法研究[J]. 交通运输工程与信息学报, 2019, 17(4):105-112.
	YU Q, YAO Z H. Research on a multi-objective path generation method for dynamic replanning[J]. Journal of Transportation Engineering and Information, 2019, 17(4):105-112. (in Chinese)
[14]	魏唯, 欧阳丹彤, 吕帅, 等. 动态不确定环境下多目标路径规划方法[J]. 计算机学报, 2011, 34(5):836-846.
	WEI W, OUYANG D T, LÜ S, et al. Multiobjective path planning under dynamic uncertain environment[J]. Chinese Journal of Computers, 2011, 34(5):836-846. (in Chinese) doi: 10.3724/SP.J.1016.2011.00836 URL
[15]	李刚, 张明, 宋兆杰, 等. 基于标准差修正G1组合赋权的科技评价模型及实证[J]. 科技管理研究, 2011, 31(20):47-50,58.
	LI G, ZHANG M, SONG Z J, et al. The science and technology evaluation model based on revised G1 by standard deviation and empirical research[J]. Science and Technology Management Research, 2011, 31(20):47-50,58. (in Chinese)
[16]	孙伟, 赵耀, 华玉龙. 无人履带装甲车的速度控制策略[J]. 制造业自动化, 2015, 37(9):49-51.
	SUN W, ZHAO Y, HUA Y L. Research on speed control method of unmanned tracked armored carriers[J]. Manufacturing Automation, 2015, 37(9):49-51. (in Chinese)
[17]	WU M Z, JIN H, TANG B, et al. Constructing topological road network of wild environment using Google Earth Pro[C]∥Proceedings of International Conference on Intelligent Transportation. Auckland, New Zealand: IEEE, 2019: 2095-2100.
[18]	YANG L, GONG J W, XIONG G M, et al. Unmanned vehicle path planning for unknown off-road environments with sparse waypoints[C]∥Proceedings of International Conference on Intelligent Transportation. Auckland, New Zealand: IEEE, 2019: 3136-3141.
[19]	胡毓达. 实用多目标最优化[M]. 上海: 上海科学技术出版社, 1990.
	HU Y D. Practical multi-objective optimization methods[M]. Shanghai: Shanghai Scientific & Technical Publishers, 1990. (in Chinese)
[20]	ZHANG Y, CHEN H Y, WASLANDER S L, et al. Hybrid trajectory planning for autonomous driving in highly constrained environments[J]. IEEE Access, 2018, 6: 32800-32819. doi: 10.1109/ACCESS.2018.2845448 URL