An Evolutionary Traveling Salesman Approach for Multi-Robot Task Allocation

Arif, Muhammad Usman; Haider, Sajjad

doi:10.5220/0006197305670574

Cited by 15 publications

(8 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The performance was evaluated against an exact integer program, an auction-based scheme (Arif, 2019) and a genetic algorithm (GA) (Arif and Haider, 2018). This paper demonstrates the effectiveness of a similar EA against MRCF while keeping its structure consistent with (Arif, 2019; Arif and Haider, 2017, 2018). This allows the development of a generic task allocation framework capable of working across a range of MRTA problem distributions.…”

Section: Introductionmentioning

confidence: 71%

“…This paper expands the operational domain of an existing EA-based task allocation framework from the literature. The existing EA provides efficient allocations against single-robot and loosely coupled MR task distributions (Arif, 2019; Arif and Haider, 2017, 2018). The proposed EA, named RoSTAM, handles tightly coupled MRCF (ST–MR–TA) while keeping its structure consistent with the existing EA.…”

Section: Literature Reviewmentioning

confidence: 99%

“…This section discusses the details of RoSTAM. First, the basic structure of the EA is discussed having most of the details common to the existing EA (Arif, 2019; Arif and Haider, 2017, 2018). Later, functional blocks including the limited horizon, penalty calculation, and fitness evaluation schemes specific to MRCF are discussed in more detail.…”

Section: Proposed Frameworkmentioning

confidence: 99%

See 2 more Smart Citations

Robot coalition formation against time-extended multi-robot tasks

Arif¹

2021

IJIUS

View full text Add to dashboard Cite

PurposeMulti-robot coalition formation (MRCF) refers to the formation of robot coalitions against complex tasks requiring multiple robots for execution. Situations, where the robots have to participate in multiple coalitions over time due to a large number of tasks, are called Time-extended MRCF. While being NP-hard, time-extended MRCF also holds the possibility of resource deadlocks due to any cyclic hold-and-wait conditions among the coalitions. Existing schemes compromise on solution quality to form workable, deadlock-free coalitions through instantaneous or incremental allocations.Design/methodology/approachThis paper presents an evolutionary algorithm (EA)-based task allocation framework for improved, deadlock-free solutions against time-extended MRCF. The framework simultaneously allocates multiple tasks, allowing the robots to participate in multiple coalitions within their schedule. A directed acyclic graph–based representation of robot plans is used for deadlock detection and avoidance.FindingsAllowing the robots to participate in multiple coalitions within their schedule, significantly improves the allocation quality. The improved allocation quality of the EA is validated against two auction schemes inspired by the literature.Originality/valueTo the best of the author's knowledge, this is the first framework which simultaneously considers multiple MR tasks for deadlock-free allocation while allowing the robots to participate in multiple coalitions within their plans.

show abstract

Section: Introductionmentioning

confidence: 71%

Section: Literature Reviewmentioning

confidence: 99%

Section: Proposed Frameworkmentioning

confidence: 99%

See 1 more Smart Citation

Robot coalition formation against time-extended multi-robot tasks

Arif¹

2021

IJIUS

View full text Add to dashboard Cite

show abstract

“…Both problems have been proved to be NP-hard [5], [6]; thus, the integration of these two NP-hard problems is a challenging issue. Multi-Robot Task Allocation (MRTA) problem addresses the problem of determining the optimal assignment of a group of tasks to a team of robots for efficient completion of the jobs at hand [7] and is closely related to the multiple traveling salesperson problem (m-TSP). A survey on the current state-of-the-art methods in MRTA problems [8] reveals the current trends on this topic.…”

Section: Related Workmentioning

confidence: 99%

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

Xidias¹,

Zacharia

2023

Preprint

View full text Add to dashboard Cite

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.

show abstract

“…However, heuristic modelling of dynamic task allocation problems furnishes contiguous optimal solutions. The multiple mobile robots' dynamic task allocation problem is generalized as multiple travelling salesman problem (Arif and Haider, 2017) and classified as a combinatorial optimization problem (dos Reis and Bastos, 2017). It is solved by evolutionary optimization algorithms: genetic algorithm (Arif and Haider, 2017;Arif and Haider, 2018;Bänziger et al, 2018), particle swarm optimization (PSO) (Alshawi and Shalan, 2017), ant colony optimization (ACO) (Li et al, 2017b), the variants of PSO and ACO algorithms (Muhuri and Rauniyar, 2017).…”

Section: Optimization-based Task Allocationmentioning

confidence: 99%

Review on state-of-the-art dynamic task allocation strategies for multiple-robot systems

Seenu

Chetty

Ramya

et al. 2020

View full text Add to dashboard Cite

Purpose This paper aims to present a concise review on the variant state-of-the-art dynamic task allocation strategies. It presents a thorough discussion about the existing dynamic task allocation strategies mainly with respect to the problem application, constraints, objective functions and uncertainty handling methods. Design/methodology/approach This paper briefs the introduction of multi-robot dynamic task allocation problem and discloses the challenges that exist in real-world dynamic task allocation problems. Numerous task allocation strategies are discussed in this paper, and it establishes the characteristics features between them in a qualitative manner. This paper also exhibits the existing research gaps and conducive future research directions in dynamic task allocation for multiple mobile robot systems. Findings This paper concerns the objective functions, robustness, task allocation time, completion time, and task reallocation feature for performance analysis of different task allocation strategies. It prescribes suitable real-world applications for variant task allocation strategies and identifies the challenges to be resolved in multi-robot task allocation strategies. Originality/value This paper provides a comprehensive review of dynamic task allocation strategies and incites the salient research directions to the researchers in multi-robot dynamic task allocation problems. This paper aims to summarize the latest approaches in the application of exploration problems.

show abstract

An Evolutionary Traveling Salesman Approach for Multi-Robot Task Allocation

Cited by 15 publications

References 19 publications

Robot coalition formation against time-extended multi-robot tasks

Robot coalition formation against time-extended multi-robot tasks

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

Review on state-of-the-art dynamic task allocation strategies for multiple-robot systems

Contact Info

Product

Resources

About