基于APF-IPPO算法的机器人路径规划

陈思冶; 李锋; 刘清云; 徐铮; 彭豪杰; 董辉

doi:10.12382/bgxb.2025.0389

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基于APF-IPPO算法的机器人路径规划

更新时间：2026-04-24

- 基于APF-IPPO算法的机器人路径规划
- RobotPath Planning Based on APF-IPPO Algorithm
- 兵工学报 2026年47卷第3期页码：250389
- 作者机构：
  
  信息工程大学地理空间信息学院，河南郑州 450001
  军委后勤保障部军事设施建设局，北京 100039
- 作者简介：
  
  通信作者邮箱：jueyun1020@126.com
- 基金信息：
  
  国家自然科学基金重点项目(42130112)
- DOI：10.12382/bgxb.2025.0389
  中图分类号： TP242
- 收稿：2025-05-20，
  
  网络首发：2026-01-27，
  
  纸质出版：2026-03
- 稿件说明：
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陈思冶, 李锋, 刘清云, 等. 基于APF-IPPO算法的机器人路径规划[J]. 兵工学报, 2026,47(3):250389.

CHEN Siye, LI Feng, LIU Qingyun, et al. RobotPath Planning Based on APF-IPPO Algorithm[J]. Acta Armamentarii, 2026, 47(3): 250389.
陈思冶, 李锋, 刘清云, 等. 基于APF-IPPO算法的机器人路径规划[J]. 兵工学报, 2026,47(3):250389. DOI： 10.12382/bgxb.2025.0389.

CHEN Siye, LI Feng, LIU Qingyun, et al. RobotPath Planning Based on APF-IPPO Algorithm[J]. Acta Armamentarii, 2026, 47(3): 250389. DOI： 10.12382/bgxb.2025.0389.

摘要

针对传统近端策略优化

（

Proximal Policy Optimization，PPO

）

算法在机器人路径规划中存在的奖励稀疏及样本效率低问题，提出一种基于人工势场

（

Artificial Potential Field，APF

）

法与广义优势估计

（

Generalized Advantage Estimation，GAE

）

及混合策略改进的近端策略优化

（

Improved Proximal Policy Optimization，IPPO

）

路径规划APF-IPPO算法。引入广义优势估计改进传统的PPO算法，促进平均奖励的加速收敛，提高优势函数估计的准确性；在动作选择时，提出基于方向相似度的混合策略来动态调整策略网络的动作分布概率；在奖励函数设计方面，设计融合APF的复合奖励函数来缓解稀疏回报问题。为验证算法性能，在多场景仿真实验中与DQN、PPO、A

及APF算法进行对比。实验结果表明，APF-IPPO算法在复杂静态环境中能够综合考虑多个性能指标，规划出最优路径，同时展现出更优的泛化能力与动态环境适应性，验证了其在路径规划任务中的有效性与优越性。

Abstract

The traditional proximal policy optimization（PPO）algorithm has the problems such as sparse rewards and low sample efficiency in robot path planning. This paper proposes an improved PPO path planning algorithm based on artificial potential field（APF）

generalized advantage estimation（GAE）and hybrid strategy optimization

namely APF-IPPO algorithm. GAE is introduced to improve the traditional PPO algorithm

promoting the accelerated convergence of average rewards and enhancing the accuracy of dominant function estimation. A hybrid strategy based on direction similarity is proposed to dynamically adjust the action distribution probability of policy network during action selection. In terms of reward function design

a composite reward function fused with APF is designed to alleviate the sparse reward problem. To verify the performance of APF-IPPO algorithm

the comparative simulations are conducted with DQN

PPO

and APF algorithms in multiple scenarios. Experimental results demonstrate that the APF-IPPO alg

orithm is capable of comprehensively considering multiple performance metrics to generate the optimal path in complex static environments

while also exhibiting the superior generalization capability and the adaptability to dynamic environments

thereby validating its effectiveness and superiority in path planning tasks.

关键词

Keywords

references

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