深度强化学习和蚁群优化支持三维环境中的多 UGV 路径规划和任务分配

IF 2.3 4区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC IET Intelligent Transport Systems Pub Date : 2024-07-10 DOI:10.1049/itr2.12535
Binghui Jin, Yang Sun, Wenjun Wu, Qiang Gao, Pengbo Si
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引用次数: 0

摘要

随着人工智能的发展,无人地面车辆(UGV)在户外危险场景中的应用受到越来越多的关注。然而,这些环境中的地形往往复杂且起伏较大,这也对多 UGV 路径规划和任务分配(MUPPTA)优化提出了更高的挑战。为了有效改善三维环境中的多 UGV 协作,本文提出了一种基于双深度 Q 学习网络(DDQN)和蚁群优化(ACO)的 MUPPTA 方法,以联合优化多 UGV 的路径规划和任务分配决策。作者首先综合考虑了三维环境的特点,将 MUPPTA 问题建模为一个组合优化问题。为了解决这个问题,作者将原问题分解为多 UGV 路径规划子问题和任务分配子问题,并分别求解。首先,将三维环境下的路径规划子问题转化为马尔可夫决策过程(MDP)模型,并提出了基于 DDQN 的多 UGV 路径规划算法(MUPP-DDQN),通过大量的离线学习和训练,获得任务间的最优路径和实际路径成本。在此基础上,进一步提出了基于 ACO 的多 UGV 任务分配算法(MUTA-ACO)来解决任务分配子问题,并实现最优任务分配方案。仿真结果表明,与其他比较算法相比,所提出的方法更经济、更省时。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Deep reinforcement learning and ant colony optimization supporting multi-UGV path planning and task assignment in 3D environments

With the development of artificial intelligence, the application of unmanned ground vehicles (UGV) in outdoor hazardous scenarios has received more attention. However, the terrains in these environments are often complex and undulating, which also pose higher challenges to the multi-UGV path planning and task assignment (MUPPTA) optimization. To efficiently improve the multi-UGV collaboration in 3D environments, a MUPPTA method is proposed based on double deep Q learning network (DDQN) and ant colony optimization (ACO) to jointly optimize the path planning and task assignment decisions of multiple UGVs. The authors first comprehensively consider the characteristics of the 3D environments, and model the MUPPTA problem as a combinatorial optimization problem. To tackle it, the original problem is decomposed into the multi-UGV path planning sub-problem and task assignment sub-problem, and solve them separately. First, the path planning sub-problem in the 3D environments is transformed into a Markov decision process (MDP) model, and a multi-UGV path planning algorithm based on DDQN (MUPP-DDQN) is proposed to obtain the optimal paths and actual path costs between tasks through extensive offline learning and training. Based on this, a multi-UGV task assignment algorithm is further proposed based on ACO (MUTA-ACO) to solve the task assignment sub-problem and achieve the optimal task assignment solution. Simulation results show that the proposed method is more cost-effective and time-saving compared to other comparison algorithms.

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来源期刊
IET Intelligent Transport Systems
IET Intelligent Transport Systems 工程技术-运输科技
CiteScore
6.50
自引率
7.40%
发文量
159
审稿时长
3 months
期刊介绍: IET Intelligent Transport Systems is an interdisciplinary journal devoted to research into the practical applications of ITS and infrastructures. The scope of the journal includes the following: Sustainable traffic solutions Deployments with enabling technologies Pervasive monitoring Applications; demonstrations and evaluation Economic and behavioural analyses of ITS services and scenario Data Integration and analytics Information collection and processing; image processing applications in ITS ITS aspects of electric vehicles Autonomous vehicles; connected vehicle systems; In-vehicle ITS, safety and vulnerable road user aspects Mobility as a service systems Traffic management and control Public transport systems technologies Fleet and public transport logistics Emergency and incident management Demand management and electronic payment systems Traffic related air pollution management Policy and institutional issues Interoperability, standards and architectures Funding scenarios Enforcement Human machine interaction Education, training and outreach Current Special Issue Call for papers: Intelligent Transportation Systems in Smart Cities for Sustainable Environment - https://digital-library.theiet.org/files/IET_ITS_CFP_ITSSCSE.pdf Sustainably Intelligent Mobility (SIM) - https://digital-library.theiet.org/files/IET_ITS_CFP_SIM.pdf Traffic Theory and Modelling in the Era of Artificial Intelligence and Big Data (in collaboration with World Congress for Transport Research, WCTR 2019) - https://digital-library.theiet.org/files/IET_ITS_CFP_WCTR.pdf
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