Autonomous Service Robots for Urban Waste Management - Multiagent Route Planning and Cooperative Operation

Gupta, Abhishek; Schoor, Michel Joop van der; Brautigam, Jacob; Justo, Valeria Bladinieres; Umland, Tobias; Göhlich, Dietmar

doi:10.1109/lra.2022.3188900

Cited by 10 publications

(15 citation statements)

References 17 publications

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“…Forest rovers may be adapted to provide a range of ground-based measurements such as physical samples of the soil ( Zaman et al, 2022 ). They could pause to take reliable pollen or greenhouse gas samples close to the ground ( Grau Ruiz and O’Brolchain, 2022 ; Gupta et al, 2022 ). Moreover, small-sized rovers could be operated quietly, being less intrusive to wildlife than drones ( Mulero-Pázmány et al, 2017 ).…”

Section: Discussionmentioning

confidence: 99%

An embarrassingly simple approach for visual navigation of forest environments

et al. 2023

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Navigation in forest environments is a challenging and open problem in the area of field robotics. Rovers in forest environments are required to infer the traversability of a priori unknown terrains, comprising a number of different types of compliant and rigid obstacles, under varying lighting and weather conditions. The challenges are further compounded for inexpensive small-sized (portable) rovers. While such rovers may be useful for collaboratively monitoring large tracts of forests as a swarm, with low environmental impact, their small-size affords them only a low viewpoint of their proximal terrain. Moreover, their limited view may frequently be partially occluded by compliant obstacles in close proximity such as shrubs and tall grass. Perhaps, consequently, most studies on off-road navigation typically use large-sized rovers equipped with expensive exteroceptive navigation sensors. We design a low-cost navigation system tailored for small-sized forest rovers. For navigation, a light-weight convolution neural network is used to predict depth images from RGB input images from a low-viewpoint monocular camera. Subsequently, a simple coarse-grained navigation algorithm aggregates the predicted depth information to steer our mobile platform towards open traversable areas in the forest while avoiding obstacles. In this study, the steering commands output from our navigation algorithm direct an operator pushing the mobile platform. Our navigation algorithm has been extensively tested in high-fidelity forest simulations and in field trials. Using no more than a 16 × 16 pixel depth prediction image from a 32 × 32 pixel RGB image, our algorithm running on a Raspberry Pi was able to successfully navigate a total of over 750 m of real-world forest terrain comprising shrubs, dense bushes, tall grass, fallen branches, fallen tree trunks, small ditches and mounds, and standing trees, under five different weather conditions and four different times of day. Furthermore, our algorithm exhibits robustness to changes in the mobile platform’s camera pitch angle, motion blur, low lighting at dusk, and high-contrast lighting conditions.

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Section: Discussionmentioning

confidence: 99%

An embarrassingly simple approach for visual navigation of forest environments

et al. 2023

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“…For various autonomous service robots in the field of waste management, the biggest challenge lies not only in the the automation of the process, but also in the operational management, in the form of route planning for the Service Event Area [8,12,13]. Optimized route planning means the shortest path and least possible energy consumption for the overall waste-management service [14].…”

Section: State Of the Artmentioning

confidence: 99%

“…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%

“…We developed and tested a prototype of the robot MARBLE, depicted in Figure 1, which can autonomously empty the litter bins [8,27]. With the help of various sensors like Lidar, ultrasonic sensors, depth cameras, and GPS, the robot can autonomously navigate through crowded places, formulating paths while avoiding dynamic and static obstacles.…”

Section: Marble and Seasmentioning

confidence: 99%

“…The authors of [7] presented an algorithm for multi-unmanned aerial vehicles (UAV) path planning that adapts to heterogeneous global navigation satellite systems (GNSSs) coverage, allowing cooperative strategies in challenging conditions and independent flight in open sky scenarios, with simulations demonstrating consistent computational efficiency across varying numbers of UAVs and waypoints. For an urban waste-management process, with the help of a fleet of autonomous urban service robots-MARBLE (Mobile Autonomous Robot for Litter Emptying) [8]-the operation requirements vary from one SEA to another.One single-route-planning algorithm for varying operational requirements might not provide an efficient operation management in the form of the least energy-consuming routes to the multiple co-operative MARBLEs over various target litter bins. This is due to the fact that one route-planning algorithm functions better under certain operational characteristics than other algorithms for such multi-agent problems [9,10].…”

Section: Introductionmentioning

confidence: 99%

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

Justo,

Gupta,

Umland

et al. 2023

Robotics

Self Cite

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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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Integration of Blockchain with Last Mile Delivery Robots Toward Marketing Innovations

Esmaeilian,

Behdad

2024

Financial Innovation and Technology

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Autonomous Service Robots for Urban Waste Management - Multiagent Route Planning and Cooperative Operation

Cited by 10 publications

References 17 publications

An embarrassingly simple approach for visual navigation of forest environments

An embarrassingly simple approach for visual navigation of forest environments

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

Integration of Blockchain with Last Mile Delivery Robots Toward Marketing Innovations

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