A Rapid Global Path Planning Method for Unmanned Tracked Vehicles Considering Energy Consumption

GU Yuqi; LI Junqiu; YANG Yongxi; LI Xueping

doi:10.12382/bgxb.2025.0212

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A Rapid Global Path Planning Method for Unmanned Tracked Vehicles Considering Energy Consumption

更新时间：2026-04-24

- A Rapid Global Path Planning Method for Unmanned Tracked Vehicles Considering Energy Consumption
- Acta Armamentarii Vol. 47, Issue 1, Pages: 250212(2026)
- 作者机构：
  
  北京理工大学电动车辆国家工程研究中心，北京 100081
- 作者简介：
- 基金信息：
- DOI：10.12382/bgxb.2025.0212
  CLC： TJ811
- Received：25 February 2025，
  
  Online First：11 February 2026，
  
  Published：31 January 2026
- 稿件说明：
移动端阅览
GU Yuqi, LI Junqiu, YANG Yongxi, et al. A Rapid Global Path Planning Method for Unmanned Tracked Vehicles Considering Energy Consumption[J]. Acta Armamentarii, 2026, 47(1): 250212.
DOI：

GU Yuqi, LI Junqiu, YANG Yongxi, et al. A Rapid Global Path Planning Method for Unmanned Tracked Vehicles Considering Energy Consumption[J]. Acta Armamentarii, 2026, 47(1): 250212. DOI： 10.12382/bgxb.2025.0212.

摘要

复杂越野环境下的全局路径规划是实现陆上装备无人驾驶的关键技术之一，而业界针对履带车辆节能续航路径规划的研究较少，且目前常用的算法难以兼顾求解质量和计算快速性，无法实际运用在履带车辆实时节能全局路径规划中。为解决该问题，提出履带车辆最优能耗快速概率图算法：搭建考虑履带车辆特性的越野行驶能耗代价模型，量化履带车辆越野能耗情况；通过创建特定矢量，有指向性地改进越野环境下概率图算法的采样方法，在降低履带车辆能耗的前提下提高算法运行速度；同时通过增密路径节点的方法防止产生路径与环境间的干涉。仿真实验结果表明，在越野环境下，新方法相较于传统算法能耗最大可下降32%，规划时间最大减少89. 2%，实现了越野条件下履带车辆全局规划路径行驶能耗和算法运行时间的综合优化。

Abstract

Global path planning in complex off-road environments is one of the key technologies for realizing the autonomous driving of unmanned ground vehicles (UGVs ) . However

there is limited research on energy-efficient path planning for tracked vehicles in the industry

and currently commonly used path planning algorithms are unable to balance both the solution quality and the computational efficiency

making them impractical for real-time energy-optimal global path planning of tracked vehicles. To address this issue

this paper proposes an optimal energy-fast probabilistic roadmap algorithm (OPRA) for tracked vehicles

An off-road energy consumption cost model considering the characteristics of tracked vehicles is established to quantify their energy consumption under off-road conditions

The sampling method of the probabilistic roadmap algorithm in off-road environments is directionally improved by creating specific vectors

enhancing the running speed of the algorithm while reducing the energy consumption of tracked vehicles. Meanwhile

a method for increasing the density of path node ia used to prevent the interference between paths and the environment. Compared with traditional algorithms

the proposed algorithm can reduce the energy consumption and the planning time by up to 32% and 89. 2%

respectively

in off-road environments

achieving the comprehensive optimization of both travel energy consumption and algorithm runtime for global path planning of tracked vehicles under off-road conditions.

关键词

Keywords

references

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