Optimized Operation Management With Predicted Filling Levels of the Litter Bins for a Fleet of Autonomous Urban Service Robots
Anton Pollak, Abhishek Gupta, Dietmar Göhlich
Abstract
Autonomous smart waste management services are becoming an essential component of sustainable urbanization. However, the lack of data and insights from current service-providers impedes a reliable transition from labor-intensive to autonomous services. Deploying information gathering devices make services expensive and resource-demanding. In project MARBLE (Mobile Autonomous RoBot for Litter Emptying) we are currently investigating the implementation of a fleet of service robots. In this framework, we could show that the absent filling data of litter bins (LBs) hinders the possibility of providing an energy-efficient and time-effective service. Hence, we introduce an approach where machine learning-based predictions for filling levels of LBs, derived from our extensive data gathering, are used to effectively manage the autonomous emptying process. The novel <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Simulated Rebalancing</i> approach in route-planning combined with the Knapsack algorithm ensures efficient service, in comparison to the Nearest Neighbor algorithm. A promising 82% filling level prediction accuracy was achieved with the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">XGBoost</i> binary classifier, as compared to the 59% baseline accuracy. Through incorporating the reliable predicted filling level data in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Simulated Rebalancing</i> approach, a reduction of 26% in operational time and 31% in energy consumption was achieved for our simulated tests for service-event-area (SEA) James-Simon-Monbijoupark in Berlin with 49 LBs.