Multi-robot exploration in task allocation problem

Alitappeh, Reza Javanmard; Jeddisaravi, Kossar

doi:10.1007/s10489-021-02483-3

Cited by 37 publications

(16 citation statements)

References 63 publications

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“…In the process of monitoring 90 task-points, the authors used a team of three robots. In [17], the problem is decomposed in region partitioning and routing problem. The first objective is to find robots' initial position and the second one is to minimize the cost of visiting task points over the routes.…”

Section: Related Workmentioning

confidence: 99%

Balanced task allocation and motion planning of a multi-robot system under fuzzy time windows

Xidias¹,

Zacharia

2023

Preprint

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A fleet of robots has been effectively used in various application domains such as industrial plant inspection. This paper proposes a solution to the combined problem of task allocation and motion planning problem for a fleet of mobile robots which are requested to operate in an intelligent industry. More specifically, the robots are requested to serve a set of inspection points within given service time windows. In comparison with the conventional time windows, our problem considers fuzzy time windows to express the decision maker’s satisfaction for visiting an inspection point. The paper develops a unified approach to the combined problem of task allocation and motion planning for a fleet of mobile robots with three objectives (a) minimizing the total travel cost considering all robots and tasks (b) balancing fairly the workloads among robots and (c) maximizing the satisfaction grade of the decision maker for receiving the services. The optimization problem is solved by using a novel combination of a Genetic Algorithm and fuzzy set theory. The computational results illustrate the efficiency and effectiveness of the proposed approach.

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Section: Related Workmentioning

confidence: 99%

Balanced task allocation and motion planning of a multi-robot system under fuzzy time windows

Xidias¹,

Zacharia

2023

Preprint

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“…This paper focuses on the complexity of the task overall to be undertaken by a cobot or cobots with respect to the programming effort required. While multiple-robot systems (MRS) can reduce the overall complexity of a task, their need for collaboration and accurate coordination [41] can add significantly to the programming workload. Further compounding the technical complexity of MRS is the need to select a suitable task allocation strategy that is robust, scalable and can be optimised for a specific task [42].…”

Section: Cobot Task Complexitymentioning

confidence: 99%

Task Complexity and the Skills Dilemma in the Programming and Control of Collaborative Robots for Manufacturing

et al. 2023

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While traditional industrial robots participate in repetitive manufacturing processes from behind caged safety enclosures, collaborative robots (cobots) offer a highly flexible and human-interactive solution to manufacturing automation. Rather than operating from within cages, safety features such as force and proximity sensors and programmed protection zones allow cobots to work safely, close to human workers. Cobots can be configured to either stop or slow their motion if they come in contact with a human or obstacle or enter a protection zone, which may be a high pedestrian traffic area. In this way, a task can be divided into sub-processes allocated to the cobot or the human based on suitability, capability or human preference. The flexible nature of the cobot makes it ideal for low-volume, ‘just-in-time’ manufacturing; however, this requires frequent reprogramming of the cobot to adapt to the dynamic processes. This paper reviews relevant cobot programming and control methods currently used in the manufacturing industry and alternative solutions proposed in the literature published from 2018 to 2023. The paper aims to (1) study the features and characteristics of existing cobot programming and control methods and those proposed in the literature, (2) compare the complexity of the task that the cobot is to perform with the skills needed to program it, (3) determine who is the ideal person to perform the programming role, and (4) assess whether the cobot programming and control methods are suited to that person’s skillset or if another solution is needed. The study is presented as a guide for potential adopters of cobots for manufacturing and a reference for further research.

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“…As previously explained, the objective of the generator is to generate realistic vehicle allocations. Based on the Voronoi diagram [29,30] and the operating concepts of paratransit services, we developed a novel space partitioning algorithm that divides the paratransit service area into multiple sub-regions based on the locations of the dispatch stations. Citizen homes in the sub-regions are assigned to the nearest or second-nearest dispatch stations so that the number of citizen requests in each sub-region each day will equal the generator input.…”

Section: Number Of Requestsmentioning

confidence: 99%

“…For each pair, we draw the perpendicular bisector. This creates the basic The Voronoi diagram is a widely used algorithm that is described fully in [29,30]. For our purposes, we first draw all of the dispatch stations on the map and then connect the stations in pairs.…”

Section: Division Of Paratransit Service Regionmentioning

confidence: 99%

Application of Generative Adversarial Network to Optimize Vehicle Allocation at Dispatch Stations of Paratransit Services

et al. 2022

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As aging populations increase worldwide, many governments have introduced the concept of paratransit services to assist individuals with limited mobility with transportation. A successful paratransit service must be able to satisfy most requests to the system; this success is typically related to the allocation of vehicles to dispatch stations. A suitable configuration can reduce unnecessary travel time and thus serve more people. This resembles the classic Dial-a-Ride problem, which previous studies have solved using heuristic algorithms. Most of these algorithms, however, incur heavy computational costs and, therefore, cannot be operated online, especially when there are many conditions to consider, many configuration requirements, or many vehicles requested. Therefore, this paper proposes an approach based on the generative adversary network (GAN), which can reduce computation significantly. In online environments, this approach can be implemented in just a few seconds. Furthermore, the amount of computation is not affected by the number of conditions, configuration requirements, or vehicles requested. This approach is based on three important concepts: (1) designing a GAN to solve the target problem; (2) using an improved Voronoi diagram to divide the overall service area to generate the input of the GAN generator; (3) using well-known system simulation software Arena to swiftly generate many conditions for the target problem and their corresponding best solutions to train the GAN. The efficiency of the proposed approach was verified using a case study of paratransit services in Yunlin, Taiwan.

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Multi-robot exploration in task allocation problem

Cited by 37 publications

References 63 publications

Balanced task allocation and motion planning of a multi-robot system under fuzzy time windows

Balanced task allocation and motion planning of a multi-robot system under fuzzy time windows

Task Complexity and the Skills Dilemma in the Programming and Control of Collaborative Robots for Manufacturing

Application of Generative Adversarial Network to Optimize Vehicle Allocation at Dispatch Stations of Paratransit Services

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