A Flexible Evolutionary Algorithm for Task Allocation in Multi-robot Team

Arif, Muhammad Usman; Haider, Sajjad

doi:10.1007/978-3-319-98446-9_9

Cited by 8 publications

(8 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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%

“…RoSTAM is perceived as a flexible task allocation framework capable of handling a variety of MRTA problem representations. A similar EA-based framework has already demonstrated efficient allocation of SR and loosely coupled MR tasks (Arif, 2019; Arif and Haider, 2018). The performance was evaluated against an exact integer program, an auction-based scheme (Arif, 2019) and a genetic algorithm (GA) (Arif and Haider, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…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: 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: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

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

“…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%

“…Few researchers considered integer programming (Li and Li, 2017;Su et al, 2018;Zhou et al, 2019) and various search algorithms (Zhao et al, 2015;Kartal et al, 2016;Mitiche et al, 2019). Many researchers used metaheuristic algorithms (Liu and Kroll, 2012;Alshawi and Shalan, 2017;Li et al, 2017b, Z. Zhu et al, 2017Arif and Haider, 2018;Chen et al, 2018b, Padmanabhan Panchu et al, 2018Wang et al, 2018;Zhou et al, 2019) to solve this optimization problem, and this could be because of the ease of implementation of such algorithms.…”

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

A Generic Evolutionary Algorithm for Efficient Multi-Robot Task Allocations

Arif

2019

Advances in Artificial Intelligence

View full text Add to dashboard Cite

A Flexible Evolutionary Algorithm for Task Allocation in Multi-robot Team

Cited by 8 publications

References 12 publications

Robot coalition formation against time-extended multi-robot tasks

Robot coalition formation against time-extended multi-robot tasks

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

A Generic Evolutionary Algorithm for Efficient Multi-Robot Task Allocations

Contact Info

Product

Resources

About