Simulated Annealing-based Energy Efficient Route Planning for Urban Service Robots

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.