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

Brautigam, Jacob; Gupta, Abhishek; Göhlich, Dietmar

doi:10.1109/mmar55195.2022.9874325

Cited by 4 publications

(5 citation statements)

References 27 publications

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“…The first one is the Simulated Annealing (SA) algorithm, which involves a metaheuristic, modelled after the cooling process in metallurgy, intended to "guide" a heuristic route optimization algorithm [28]. The algorithm starts with an initial route created using a nearest neighbor or random node order algorithm [29], which is then iteratively optimized (see previous publication [29] for more information). Furthermore, the Knapsack problem (KSP) algorithm was implemented for a single as well as a swarm of MARBLE robots.…”

Section: Figure 1: Marble Performing Its Functionalities During a Tes...mentioning

confidence: 99%

“…Similarly, MARBLE has no previous knowledge about the filling levels of the LBs. Preceding works have provided assistance in route planning by developing simulated routes for emptying the LBs with the least possible energy consumption [18]- [20]. These routes are till now either based on the assumption that the dustbins will be 50% full [18], [20] or that they will be provided with assistance from future smart LBs [19].…”

Section: Introductionmentioning

confidence: 99%

“…Preceding works have provided assistance in route planning by developing simulated routes for emptying the LBs with the least possible energy consumption [18]- [20]. These routes are till now either based on the assumption that the dustbins will be 50% full [18], [20] or that they will be provided with assistance from future smart LBs [19]. But neither is the assumption of 50% reliable, nor converting all the conventional LBs into smart LBs economically and ecologically feasible in the near future.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimized Operation Management With Predicted Filling Levels of the Litter Bins for a Fleet of Autonomous Urban Service Robots

Pollak,

Gupta,

Göhlich

2024

IEEE Access

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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 learningbased predictions for filling levels of LBs, derived from our extensive data gathering, are used to effectively manage the autonomous emptying process. The novel Simulated Rebalancing 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 XGBoost binary classifier, as compared to the 59% baseline accuracy. Through incorporating the reliable predicted filling level data in the Simulated Rebalancing 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.

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Section: Figure 1: Marble Performing Its Functionalities During a Tes...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimized Operation Management With Predicted Filling Levels of the Litter Bins for a Fleet of Autonomous Urban Service Robots

Pollak,

Gupta,

Göhlich

2024

IEEE Access

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“…For SEA Monbijou Park, Berlin, with 51 LBs, a management system framework was established, with a physical layer defining robot, MS, and SEA characteristics, and a platform layer using algorithms to optimize routes for minimal energy consumption and operation time based on LB positions and 50% filling levels (Bräutigam et al, 2022). Routes considered MARBLE's limited garbage storing capacity.…”

Section: Phase 3: Operation Management System As Servicementioning

confidence: 99%

Service centric design methodology for integrated robot-infrastructure systems

Gupta,

Göhlich

2024

Proc. Des. Soc.

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The ongoing development of technology and AI facilitates the emergence of service robots in various application fields. Hence, the development of robot-infrastructure product-service systems (PSS) will become increasingly important. Based on the existing literature we propose a new methodological approach for a joint development of robot and infrastructure in the context of a socio-technical system with various stakeholders. We suggest digital models and physical prototypes to synchronize service and product development. The applicability is demonstrated for autonomous waste management robots.

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“…Optimized route planning means the shortest path and least possible energy consumption for the overall waste-management service [14]. The route-planning algorithm can be solved as a vehicle routing problem using meta-heuristics like simulated annealing for single [15] and multiple actors [8]. The knapsack problem is a combinatorial optimization problem related to capacity constraints; it focuses on optimizing the weight of items in a specific sack.…”

Section: State Of the Artmentioning

confidence: 99%

Minimum Energy Utilization Strategy for Fleet of Autonomous Robots in Urban Waste Management

Justo,

Gupta,

Umland

et al. 2023

Robotics

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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 km2, 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≤5 km2, 50–450 litter bins, and a fill rate of 10–40%.

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

Cited by 4 publications

References 27 publications

Optimized Operation Management With Predicted Filling Levels of the Litter Bins for a Fleet of Autonomous Urban Service Robots

Optimized Operation Management With Predicted Filling Levels of the Litter Bins for a Fleet of Autonomous Urban Service Robots

Service centric design methodology for integrated robot-infrastructure systems

Minimum Energy Utilization Strategy for Fleet of Autonomous Robots in Urban Waste Management

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