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Reinforcement learning‐based trajectory planning for continuous digging of excavator working devices in trenching tasks
Computer-Aided Civil and Infrastructure Engineering ( IF 8.5 ) Pub Date : 2025-01-28 , DOI: 10.1111/mice.13428
X. Tan, W. Wei, C. Liu, K. Cheng, Y. Wang, Z. Yao, Q. Huang
Computer-Aided Civil and Infrastructure Engineering ( IF 8.5 ) Pub Date : 2025-01-28 , DOI: 10.1111/mice.13428
X. Tan, W. Wei, C. Liu, K. Cheng, Y. Wang, Z. Yao, Q. Huang
This paper addresses the challenge of real‐time, continuous trajectory planning for autonomous excavation. A hybrid method combining particle swarm optimization (PSO) and reinforcement learning (RL) is proposed. First, three types of excavation trajectories are defined for different geometric shapes of the digging area. Then, an excavation trajectory optimization method based on the PSO algorithm is established, resulting in optimal trajectories, the sensitive parameters, and the corresponding variation ranges. Second, an RL model is built, and the optimization results obtained offline are used as training samples. The RL‐based method can be applied for continuous digging tasks, which is beneficial for improving the overall efficiency of the autonomous operation of the excavator. Finally, simulation experiments were conducted in four distinct conditions. The results demonstrate that the proposed method effectively accomplishes excavation tasks, with trajectory generation completed within 0.5 s. Comprehensive performance metrics remained below 0.14, and the excavation rate exceeded 92%, surpassing or matching the performance of the optimization‐based method and PINN‐based method. Moreover, the proposed method produced consistently balanced trajectory performance across all sub‐tasks. These results underline the method's effectiveness in achieving real‐time, multi‐objective, and continuous trajectory planning for autonomous excavators.
中文翻译:
基于强化学习的挖掘机工作装置在挖沟任务中连续挖掘的轨迹规划
本文解决了自主挖掘的实时、连续轨迹规划的挑战。提出了一种粒子群优化 (PSO) 和强化学习 (RL) 相结合的混合方法。首先,针对挖掘区域的不同几何形状定义了三种类型的挖掘轨迹。然后,建立基于 PSO 算法的开挖轨迹优化方法,得到最优轨迹、敏感参数和相应的变化范围。其次,构建 RL 模型,并将离线获得的优化结果作为训练样本;基于 RL 的方法可应用于连续挖掘任务,有利于提高挖掘机自主运行的整体效率。最后,在四种不同的条件下进行了模拟实验。结果表明,所提方法有效完成了开挖任务,轨迹生成在 0.5 s 内完成。综合性能指标保持在 0.14 以下,挖掘率超过 92%,超过或匹配基于优化的方法和基于 PINN 的方法的性能。此外,所提出的方法在所有子任务中产生了一致平衡的轨迹性能。这些结果强调了该方法在实现自动挖掘机实时、多目标和连续轨迹规划方面的有效性。
更新日期:2025-01-28
中文翻译:
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基于强化学习的挖掘机工作装置在挖沟任务中连续挖掘的轨迹规划
本文解决了自主挖掘的实时、连续轨迹规划的挑战。提出了一种粒子群优化 (PSO) 和强化学习 (RL) 相结合的混合方法。首先,针对挖掘区域的不同几何形状定义了三种类型的挖掘轨迹。然后,建立基于 PSO 算法的开挖轨迹优化方法,得到最优轨迹、敏感参数和相应的变化范围。其次,构建 RL 模型,并将离线获得的优化结果作为训练样本;基于 RL 的方法可应用于连续挖掘任务,有利于提高挖掘机自主运行的整体效率。最后,在四种不同的条件下进行了模拟实验。结果表明,所提方法有效完成了开挖任务,轨迹生成在 0.5 s 内完成。综合性能指标保持在 0.14 以下,挖掘率超过 92%,超过或匹配基于优化的方法和基于 PINN 的方法的性能。此外,所提出的方法在所有子任务中产生了一致平衡的轨迹性能。这些结果强调了该方法在实现自动挖掘机实时、多目标和连续轨迹规划方面的有效性。