Multi-objective Route Planning for Underwater Cleaning Robot in Water Reservoir Tank

Abidin, Mastura Shafinaz Zainal; Buyamin, Salinda; Emmanuel, Abioye Abiodun; Hasan, Hameedah Sahib

doi:10.1007/s10846-020-01291-0

Cited by 25 publications

(9 citation statements)

References 33 publications

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“…Using the articles collected in this manner, effective and efficient technologies that can be used for sorting mixed industrial waste were investigated in this study. From these investigations, domain-specific sorting approaches for underwater [40], [41], [42], [43], floating [44], [45], [46], space [47], [48], nuclear [49], [50], [51], [52], [53], [54], [55], and biomedical [56], [57] waste were excluded. Rather, Municipal and CNI [58] waste, along with ND, CND [36],…”

Section: B Search and Collection Strategymentioning

confidence: 99%

Challenges for Future Robotic Sorters of Mixed Industrial Waste: A Survey

Kiyokawa

Takamatsu

Koyanaka

2024

IEEE Trans. Automat. Sci. Eng.

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To achieve recycling of mixed industrial waste toward an advanced sustainable society, waste sorting automation through robots is crucial and urgent. For this purpose, a robot is required to recognize the category, shape, pose, and condition of different waste items and manipulate them according to the category to be sorted. This survey considers three potential difficulties in the sorting automation: 1) End-effector: to robustly grasp and manipulate different waste items with dirt and deformations; 2) Sensor: to recognize the category, shape, and pose of existing objects to be manipulated and the wet and dirty conditions of their surfaces; and 3) Planner: to generate feasible and efficient sequences and trajectories. This survey includes 76 references to studies related to automatic waste sorting and 159 references to worldwide waste recycling attempts. This pioneering investigation reveals the possibility and limitations of conventional systems; thus, providing insights on open issues and potential technologies to achieve a robot-incorporated sorter for the chaotic mixed waste is one of its contributions. This paper further presents a system design policy for readers and discusses future advanced sorters, thereby contributing to the field of robotics and automation.Note to Practitioners-Most automated sorting systems operate for limited target waste items. This study is motivated by the automation of mixed industrial waste treatment facilities using advanced robotic sorters. Emerging advances and increasing functionalities of robot system components will widen system applicability and increase use cases in the chaotic mixed industrial waste domain. This paper surveys the research conducted to date, discusses open issues and potential approaches, and presents user guides that provide practitioners with a system design policy. The user guides created according to the strengths and weaknesses of each system configuration provide future researchers and developers with a useful a priori design policy that has been thus far validated on efficiency, quality, productivity, and reliability. A question-and-answer style guide Manuscript

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Section: B Search and Collection Strategymentioning

confidence: 99%

Challenges for Future Robotic Sorters of Mixed Industrial Waste: A Survey

Kiyokawa

Takamatsu

Koyanaka

2024

IEEE Trans. Automat. Sci. Eng.

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“…Hang et al [32] presented a more pragmatic model of stochastic networks that not only takes into account deterministic variables, but also the mean and variance of random variables, and combined an artificial immune system, a chaos operator, and PSO to speed up the solution. In [33] a multi-objective approach was presented to design a Decision Support System (DSS) for underwater cleaning robots. The Probabilistic Roadmap (PRM) is used to explore all possible paths at every cleaning point in the tank.…”

Section: Hybrid Approachesmentioning

confidence: 99%

Multi Objective Optimization Algorithms for Mobile Robot Path Planning: A Survey

Abed

Jasim²

2022

Int. J. Onl. Eng.

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Path planning algorithms is the most significant area in the robotics field. Path Planning (PP) can be defined as the process of determining the most appropriate navigation path before a mobile robot moves. Optimization of path planning refers to finding the optimal or near-optimal path. Multi-objective optimization (MOO) is concerned with finding the best solution values that satisfy multiple objectives, such as shortness, smoothness, and safety. MOOs present the challenge of making decisions while balancing these contradictory issues through compromise (tradeoff). As a result, there is no single solution appropriate for all purposes in MOO, but rather a range of solutions. The purpose of this paper is to present an overview of mobile robot navigation strategies employed to find the path that has the minimum number of criteria (shortest, smoothness, and safest) so far. Here, multi objective approaches are discussed in detail in order to identify research gaps. In addition, it is important to understand how path planning strategies are developed under various environmental circumstances.

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“…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]. The methodologies behind such algorithms serve as solution for specific requirements and not for all the requirements of the varying operation SEAs [11]. Developing a dedicated route-planning algorithm for every individual SEA is not just time-consuming, but is also a non-viable solution, as the characteristics of an SEA can change any given moment and it is not necessary for a new SEA to have characteristics that are similar to another SEA.…”

Section: Introductionmentioning

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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Multi-objective Route Planning for Underwater Cleaning Robot in Water Reservoir Tank

Cited by 25 publications

References 33 publications

Challenges for Future Robotic Sorters of Mixed Industrial Waste: A Survey

Challenges for Future Robotic Sorters of Mixed Industrial Waste: A Survey

Multi Objective Optimization Algorithms for Mobile Robot Path Planning: A Survey

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

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