Bi-directional Optimization of Multi-target Task Allocation and Path Planning for Autonomous Vehicles

BAI Liting; LIU Xulei; ZHOU Guofeng; PENG Yiqiang

doi:10.12382/bgxb.2025.0195

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Bi-directional Optimization of Multi-target Task Allocation and Path Planning for Autonomous Vehicles

更新时间：2026-04-24

- Bi-directional Optimization of Multi-target Task Allocation and Path Planning for Autonomous Vehicles
- Acta Armamentarii Vol. 47, Issue 3, Pages: 250195(2026)
- 作者机构：
  
  西华大学汽车与交通学院，四川成都 610039
  西华大学宜宾研究院，四川宜宾 644000
  四川省新能源汽车智能控制与仿真测试技术工程研究中心，四川成都 610039
  上海海洋大学工程学院，上海 201306
- 作者简介：
- 基金信息：
- DOI：10.12382/bgxb.2025.0195
  CLC： TP242.6
- Received：20 March 2025，
  
  Online First：04 February 2026，
  
  Published：2026-03
- 稿件说明：
移动端阅览
BAI Liting, LIU Xulei, ZHOU Guofeng, et al. Bi-directional Optimization of Multi-target Task Allocation and Path Planning for Autonomous Vehicles[J]. Acta Armamentarii, 2026, 47(3): 250195.
DOI：

BAI Liting, LIU Xulei, ZHOU Guofeng, et al. Bi-directional Optimization of Multi-target Task Allocation and Path Planning for Autonomous Vehicles[J]. Acta Armamentarii, 2026, 47(3): 250195. DOI： 10.12382/bgxb.2025.0195.

摘要

目前，无人作战车辆已成为现代战争中重要的作战力量，两方对抗作战的关键在于实现合理的目标分配并寻求快速的接近打击。然而，由于战场环境复杂，存在目标分配不准确以及目标分配与路径规划之间的时间空窗期等问题。针对多目标分配与路径规划的协同优化问题，提出一种双向优化方法。该方法构建了考虑路径规划影响的目标任务分配模型，并引入贪心轮转算法对分配方案进行迭代优化，从而快速获取最优任务分配结果。在此基础上，采用改进的双向A*算法执行路径规划，通过将优化后的分配方案与路径规划深度融合，生成无人车的具体行驶路径。在大型障碍物及密集障碍物环境下的仿真实验表明，该双向优化算法在复杂场景中具有显著的优越性和有效性。

Abstract

In modern warfare

the unmanned combat vehicles have emerged as a critical operational force

and the success of adversarial engagements hinges on rational target allocation and rapid approach-and-strike capabilities. However

the battlefield environmental complexities introduce there are challenges such as inaccurate target allocation and a temporal gap between target allocation and path planning due to the complex battlefield environment. To address these issues

a bidirectional optimization method for target allocation and path planning is proposed. A target allocation model influenced by path planning is established

and the greedy rotation algorithm is employed to optimize the allocation plan

thereby rapidly obtaining optimal target allocation schemes. Subsequently

an improved bidirectional A

algorithm is used for path planning. The specific trajectories for the unmanned vehicles are determined by deeply integrating the optimized allocation plan with the planned paths. The autonomous vehicles are tested in simulated environments with large-scale obstacles and dense obstacle clusters. The test results demonstrate that the proposed bidirectional optimization algorithm method for target allocation and path planning exhibits significantly superior performance.

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references

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