{"title":"基于模拟退火的城市服务机器人节能路径规划","authors":"Jacob Bräutigam, Abhishek Gupta, D. Göhlich","doi":"10.1109/MMAR55195.2022.9874325","DOIUrl":null,"url":null,"abstract":"Product development and operational methods of autonomous robot MURMEL (Mobiler Urbaner Roboter zur MüllEimer Leerung) for smart waste management focuses on all three aspects of sustainability. The robot autonomously empties the dustbins on the streets of Berlin, and concentrates on social sustainability by reducing the impact on the health of the workers. As one of the approaches towards efficient ecological and economical sustainability, MURMEL has its own route planning method delivered from an operation management model. For the operation management, the dustbins are allocated waypoints to the robot. In this paper, a cost function considering energy consumption is optimized in order to improve the robot's global route planning. By conducting numerous experiments with varying algorithm parameter values, the specific weightage of route permutation operators and the initial temperature for a simulated annealing method with nearest neighbour approach was narrowed down to a well-performing set. For an implementation in an actual existing park in Berlin for operation scenario, our simulation results show an average overall reduction of 11 % energy consumption as compared to results from a nearest neighbour heuristic.","PeriodicalId":169528,"journal":{"name":"2022 26th International Conference on Methods and Models in Automation and Robotics (MMAR)","volume":"5 2","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Simulated Annealing-based Energy Efficient Route Planning for Urban Service Robots\",\"authors\":\"Jacob Bräutigam, Abhishek Gupta, D. Göhlich\",\"doi\":\"10.1109/MMAR55195.2022.9874325\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Product development and operational methods of autonomous robot MURMEL (Mobiler Urbaner Roboter zur MüllEimer Leerung) for smart waste management focuses on all three aspects of sustainability. The robot autonomously empties the dustbins on the streets of Berlin, and concentrates on social sustainability by reducing the impact on the health of the workers. As one of the approaches towards efficient ecological and economical sustainability, MURMEL has its own route planning method delivered from an operation management model. For the operation management, the dustbins are allocated waypoints to the robot. In this paper, a cost function considering energy consumption is optimized in order to improve the robot's global route planning. By conducting numerous experiments with varying algorithm parameter values, the specific weightage of route permutation operators and the initial temperature for a simulated annealing method with nearest neighbour approach was narrowed down to a well-performing set. For an implementation in an actual existing park in Berlin for operation scenario, our simulation results show an average overall reduction of 11 % energy consumption as compared to results from a nearest neighbour heuristic.\",\"PeriodicalId\":169528,\"journal\":{\"name\":\"2022 26th International Conference on Methods and Models in Automation and Robotics (MMAR)\",\"volume\":\"5 2\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-08-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 26th International Conference on Methods and Models in Automation and Robotics (MMAR)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/MMAR55195.2022.9874325\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 26th International Conference on Methods and Models in Automation and Robotics (MMAR)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/MMAR55195.2022.9874325","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
用于智能废物管理的自主机器人MURMEL (Mobiler Urbaner Roboter zur m lleimer Leerung)的产品开发和操作方法侧重于可持续性的所有三个方面。机器人在柏林的街道上自动清理垃圾箱,并通过减少对工人健康的影响,专注于社会的可持续性。作为高效的生态和经济可持续发展的途径之一,MURMEL有自己的路线规划方法,从运营管理模型交付。为了进行操作管理,垃圾箱被分配给机器人路径点。为了提高机器人的全局路径规划能力,本文对考虑能量消耗的成本函数进行了优化。通过对不同算法参数值进行多次实验,将最近邻法模拟退火方法的路由置换算子的权重和初始温度缩小到一个性能较好的集合。对于在柏林的一个实际现有公园中的操作场景的实现,我们的模拟结果显示,与最近邻居启发式的结果相比,平均总体能耗降低了11%。
Simulated Annealing-based Energy Efficient Route Planning for Urban Service Robots
Product development and operational methods of autonomous robot MURMEL (Mobiler Urbaner Roboter zur MüllEimer Leerung) for smart waste management focuses on all three aspects of sustainability. The robot autonomously empties the dustbins on the streets of Berlin, and concentrates on social sustainability by reducing the impact on the health of the workers. As one of the approaches towards efficient ecological and economical sustainability, MURMEL has its own route planning method delivered from an operation management model. For the operation management, the dustbins are allocated waypoints to the robot. In this paper, a cost function considering energy consumption is optimized in order to improve the robot's global route planning. By conducting numerous experiments with varying algorithm parameter values, the specific weightage of route permutation operators and the initial temperature for a simulated annealing method with nearest neighbour approach was narrowed down to a well-performing set. For an implementation in an actual existing park in Berlin for operation scenario, our simulation results show an average overall reduction of 11 % energy consumption as compared to results from a nearest neighbour heuristic.