A sustainable approach towards smart waste management means reduced impact on the health of the workers, lower greenhouse gas emissions and low operational costs. Incorporating fleets of the robot MARBLE (Mobile Autonomous RoBot for Litter Emptying) effectuates these requirements for autonomously emptying the dustbins on the streets of Berlin. The dedicated waypoints for the route formulation are the global positions of the dustbins, and the garbage emptying position from the robots after reaching the maximum compressed-garbage storage capacity. In this paper, we provide a cost function efficient solution for providing global routes to the robots. The cost function includes operational energy expenditure. After 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 as a well-performing set. This set was used in three different operation scenarios. The results of the cost function on the basis of the chosen parameters show an improvement of average 28% for two different exemplary datasets, and 30% improvement for an implementation in an actual existing park in Berlin for operation scenarios as compared to results from a nearest neighbour heuristic.

Many service robots have to operate in a variety of different Service Event Areas (SEAs). In the case of the waste collection robot MARBLE (Mobile Autonomous Robot for Litter Emptying) every SEA has characteristics like varying area and number of litter bins, with different distances between litter bins and uncertain filling levels of litter bins. Global positions of litter bins and garbage drop-off positions from MARBLEs after reaching their maximum capacity are defined as task-performing waypoints. We provide boundary delimitation for characteristics that describe the SEA. The boundaries interpolate synergy between individual SEAs and the developed algorithms. This helps in determining which algorithm best suits an SEA, dependent on the characteristics. The developed route-planning methodologies are based on vehicle routing with simulated annealing (VRPSA) and knapsack problems (KSPs). VRPSA uses specific weighting based on route permutation operators, initial temperature, and the nearest neighbor approach. The KSP optimizes a route’s given capacity, in this case using smart litter bins (SLBs) information. The game-theory KSP algorithm with SLBs information and the KSP algorithm without SLBs information performs better on SEAs lower than 0.5 km${}^2$, and with fewer than 50 litter bins. When the standard deviation of the fill rate of litter bins is ≈$10\%$, the KSP without SLB is preferred, and if the standard deviation is between 25 and 40%, then the game-theory KSP is selected. Finally, the vehicle routing problem outperforms in SEAs with an area of $0.5\le 5$ km², 50–450 litter bins, and a fill rate of 10–40%.