A Generic Evolutionary Algorithm for Efficient Multi-Robot Task Allocations

Arif, Muhammad Usman

doi:10.1007/978-3-030-18305-9_49

Cited by 4 publications

(6 citation statements)

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“…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%

“…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%

“…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). This paper demonstrates the effectiveness of a similar EA against MRCF while keeping its structure consistent with (Arif, 2019; Arif and Haider, 2017, 2018).…”

Section: Introductionmentioning

confidence: 99%

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Robot coalition formation against time-extended multi-robot tasks

Arif¹

2021

IJIUS

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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.

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Section: Proposed Frameworkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Robot coalition formation against time-extended multi-robot tasks

Arif¹

2021

IJIUS

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“…So far, most studies for MPDA have been focused on static environments, in which all the information regarding the robots and tasks is known in advance [2], [3], [14]. However, in the real world, the environment is dynamic and the information about the new tasks is unknown until the tasks are detected.…”

Section: Introductionmentioning

confidence: 99%

Automated Coordination Strategy Design Using Genetic Programming for Dynamic Multipoint Dynamic Aggregation

Gao

Mei

Xin

et al. 2022

IEEE Trans. Cybern.

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The multi-point dynamic aggregation (MPDA) problem of the multi-robot system is of great significance for its realworld applications like bush fire elimination. The problem is to design the optimal plan for a set of heterogeneous robots to complete some geographically distributed tasks collaboratively. In this paper, we consider the dynamic version of the problem where new tasks keep appearing after the robots are dispatched from the depot. The dynamic MPDA problem is a complicated optimization problem due to several characteristics, such as the collaboration of robots, the accumulative task demand, the relationships among robots and tasks, and the unpredictable task arrivals. In this paper, a new model of the problem considering these characteristics is proposed. To solve the problem, we develop a new genetic programming hyper-heuristic (GPHH) method to evolve reactive coordination strategies (RCS) which can guide the robots to make decisions in real-time. The proposed GPHH method contains a newly designed effective RCS heuristic template to generate the execution plan for the robots according to a GP tree. A new terminal set of features related to both robots and tasks, and a cluster filter which assigns the robots to urgent tasks are designed. The experimental results show that the proposed GPHH significantly outperformed the state-of-the-art methods. Through further analysis, useful insights such as how to distribute and coordinate robots to execute different types of tasks are discovered.

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