Decentralized Task Allocation in Multi-Agent Systems Using a Decentralized Genetic Algorithm

Patel, Ruchir; Rudnick-Cohen, Eliot; Azarm, Shapour; Otte, Michael W.; Xu, Huan; Herrmann, Jeffrey W.

doi:10.1109/icra40945.2020.9197314

Cited by 35 publications

(9 citation statements)

References 16 publications

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“…the number of assigned tasks for each agent is known and fixed. [12] proposes a decentralized genetic algorithm (GA) to search a task sequence parallelly. [13] proposes a graph-based search method to allocate tasks to agents given a finite linear temporal logic objective, where the allocation order is partially known.…”

Section: A Related Workmentioning

confidence: 99%

DrMaMP: Distributed Real-time Multi-agent Mission Planning in Cluttered Environment

Lu¹,

Zhou²,

Mou³

2023

Preprint

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Solving a collision-aware multi-agent mission planning (task allocation and path finding) problem is challenging due to the requirement of real-time computational performance, scalability, and capability of handling static/dynamic obstacles and tasks in a cluttered environment. This paper proposes a distributed real-time (on the order of millisecond) algorithm DrMaMP, which partitions the entire unassigned task set into subsets via approximation and decomposes the original problem into several single-agent mission planning problems. This paper presents experiments with dynamic obstacles and tasks and conducts optimality and scalability comparisons with an existing method, where DrMaMP outperforms the existing method in both indices. Finally, this paper analyzes the computational burden of DrMaMP which is consistent with the observations from comparisons, and presents the optimality gap in small-size problems.

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Section: A Related Workmentioning

confidence: 99%

DrMaMP: Distributed Real-time Multi-agent Mission Planning in Cluttered Environment

Lu¹,

Zhou²,

Mou³

2023

Preprint

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“…ere are many types of population-based metaheuristic approaches, including genetic algorithms (e.g. [58,[68][69][70][71][72]), Particle Swarm Optimization (PSO) (e.g. [73,74]), Ant Colony Optimization (ACO) (e.g.…”

Section: Stochastic and Heuristic Approachesmentioning

confidence: 99%

Task Allocation Using a Team of Robots

et al. 2022

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Purpose of Review Task allocation using a team or coalition of robots is one of the most important problems in robotics, computer science, operational research, and artificial intelligence. We present a survey of multi-robot task allocation covering many problem variants and solution approaches, both centralized and decentralized. Recent Findings In recent work, research has focused on handling complex objectives and feasibility constraints amongst other variations of the multi-robot task allocation problem. There are many examples of important research and recent progress in these directions, which are captured in this survey, along with similar examples for the various solutions that have been developed to solve such problems. Summary We first present a general formulation of the task allocation problem that generalizes several versions that are well-studied. Our formulation includes the states of robots, tasks, and the surrounding environment in which they operate, and we describe how the problem can be varied depending on the feasibility constraints, objective functions, and the level of dynamically changing information. In addition, we discuss existing solution approaches for the problem including optimization-based approaches, and market-based approaches.

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“…This section mentions three areas that can be explored in future research. First, we will consider comparison with additional multi-agent task allocation mechanisms, exploring more complex bidding strategies for the auction mechanisms mentioned in Section 2 , as well as the use of evolutionary inspired approaches, such as Genetic Algorithms (GAs) and Particle Swarm Optimization (PSO), both of which have previously been applied to task allocation problems ( Salman et al, 2002 ; Liu and Kroll, 2012 ; Patel et al, 2020 ). A solution to our task allocation problem can be represented as a vector of integers (with each integer referring to which field a worker is assigned to).…”

Section: Future Workmentioning

confidence: 99%

“…the population. Patel et al (2020) introduce a decentralised GA and compare minimising the total distance travelled by robots, minimising the maximum distance travelled by the robots and a combination of the two. In contrast, Martin et al (2021) compare Branch and Bound (B&B) to GAs for allocating tasks to ground and aerial vehicles within a solar thermal plant.…”

Section: Introductionmentioning

confidence: 99%

Multi-agent task allocation for harvest management

Harman

Sklar²

2022

Front. Robot. AI

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Multi-agent task allocation methods seek to distribute a set of tasks fairly amongst a set of agents. In real-world settings, such as soft fruit farms, human labourers undertake harvesting tasks. The harvesting workforce is typically organised by farm manager(s) who assign workers to the fields that are ready to be harvested and team leaders who manage the workers in the fields. Creating these assignments is a dynamic and complex problem, as the skill of the workforce and the yield (quantity of ripe fruit picked) are variable and not entirely predictable. The work presented here posits that multi-agent task allocation methods can assist farm managers and team leaders to manage the harvesting workforce effectively and efficiently. There are three key challenges faced when adapting multi-agent approaches to this problem: (i) staff time (and thus cost) should be minimised; (ii) tasks must be distributed fairly to keep staff motivated; and (iii) the approach must be able to handle incremental (incomplete) data as the season progresses. An adapted variation of Round Robin (RR) is proposed for the problem of assigning workers to fields, and market-based task allocation mechanisms are applied to the challenge of assigning tasks to workers within the fields. To evaluate the approach introduced here, experiments are performed based on data that was supplied by a large commercial soft fruit farm for the past two harvesting seasons. The results demonstrate that our approach produces appropriate worker-to-field allocations. Moreover, simulated experiments demonstrate that there is a “sweet spot” with respect to the ratio between two types of in-field workers.

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Decentralized Task Allocation in Multi-Agent Systems Using a Decentralized Genetic Algorithm

Cited by 35 publications

References 16 publications

DrMaMP: Distributed Real-time Multi-agent Mission Planning in Cluttered Environment

DrMaMP: Distributed Real-time Multi-agent Mission Planning in Cluttered Environment

Task Allocation Using a Team of Robots

Multi-agent task allocation for harvest management

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