土壤污染场地多无人车的路径规划与运输任务分配算法  

Path planning and transportation task assignment algorithm for multiple unmanned ground vehicles in soil contaminated site

作　　者：金冰慧 孙阳 吴文君 翟梦荻 高强 司鹏搏 JIN Binghui;SUN Yang;WU Wenjun;ZHAI Mengdi;GAO Qiang;SI Pengbo(Faculty of Information Technology,Beijing University of Technology)

机构地区：[1]北京工业大学信息学部

出　　处：《环境工程技术学报》2023年第5期1717-1724,共8页Journal of Environmental Engineering Technology

基　　金：国家重点研发计划项目(2020YFC1807904,2020YFC1807903);国家自然科学基金青年基金项目(62001011)。

摘　　要：为提升我国土壤生物修复技术智能化装备水平,以某一污染严重的焦化厂为研究环境,针对焦化厂的地形地貌特点,采用深度双Q网络(DDQN)和蚁群优化算法(ACO)建立多无人车路径规划和任务分配系统,实现土壤修复过程中污染土壤的安全、精准运输,提高污染土壤运输的效率。结果表明:基于DDQN和ACO的多无人车运输系统具备良好的路径规划能力,与其他基于简单的线性距离或基于贪婪算法得到的任务分配策略相比,基于实际系统时间开销的ACO任务分配算法在不同装载量情况下均可实现无人车系统时间开销的稳定降低。In order to improve the intelligent equipment level of bioremediation technology,a heavily polluted coke plant was taken as the research environment,and the double deep Q network(DDQN)and ant colony optimization algorithm(ACO)were used to establish a multiple unmanned ground vehicles(multi-UGV)path planning and task assignment system for the topographical features of the coke plant to achieve safe and accurate transportation of contaminated soil in the soil remediation process and improve the efficiency of contaminated soil transportation.The results showed that the multi-UGV transportation system based on DDQN and ACO had good path planning capability,and the ACO task assignment algorithm based on the actual system time cost could achieve a stable reduction of UGV system time cost under different loading quantities compared with other task assignment strategies obtained based on simple linear distance or based on the greedy algorithm.

关 键 词：土壤污染场地 路径规划 任务分配 深度强化学习 蚁群优化 

分 类 号：X53[环境科学与工程—环境工程]