Multi-Robot Task Allocation Based on Swarm Intelligence

Liu, Shuhua; Sun, Tieli; Hung, Chih‐Cheng

doi:10.5772/13106

Cited by 14 publications

(7 citation statements)

References 15 publications

(2 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A distributed method is proposed to assign tasks to multiple robots based on particle swarm optimization (PSO) [39]. Authors of works [40,41] used swarm intelligence for task allocation in large-scale multiple robot systems. [42] dealt with the MRTA problem under spatial, temporal, and energetic constraints.…”

Section: Algorithms For Mrtamentioning

confidence: 99%

A Novel Multi-Robot Task Allocation Model in Marine Plastics Cleaning Based on Replicator Dynamics

Hong

Cui

Chen

2021

JMSE

View full text Add to dashboard Cite

As marine plastic pollution threatens the marine ecosystem seriously, the government needs to find an effective way to clean marine plastics. Due to the advantages of easy operation and high efficiency, autonomous underwater vehicles (AUVs) have been applied to clean marine plastics. As for the large-scale marine environment, the marine plastic cleaning task needs to be accomplished through the collaborative work of multiple AUVs. Assigning the cleaning task to each AUV reasonably and effectively has an essential impact on improving cleaning efficiency. The coordination of AUVs is subjected to harsh communication conditions. Therefore, to release the dependence on the underwater communications among AUVs, proposing a reliable multi-robot task allocation (MRTA) model is necessary. Inspired by the evolutionary game theory, this paper proposes a novel multi-robot task allocation (MRTA) model based on replicator dynamics for marine plastic cleaning. This novel model not only satisfies the minimization of the cost function, but also reaches a relatively stable state of the task allocation. A novel optimization algorithm, equilibrium optimizer (EO), is adopted as the optimizer. The simulation results validate the correctness of the results achieved by EO and the applicability of the proposed model. At last, several valuable conclusions are obtained from the simulations on the three different assumed AUVs.

show abstract

Section: Algorithms For Mrtamentioning

confidence: 99%

A Novel Multi-Robot Task Allocation Model in Marine Plastics Cleaning Based on Replicator Dynamics

Hong

Cui

Chen

2021

JMSE

View full text Add to dashboard Cite

show abstract

“…Particle swarm optimization were used to propose a distributed algorithm to dynamically allocate tasks to real robots [46]. Authors of works [39], [69], [70] used swarm intelligence for task assignment in large-scale multi-robot systems. The works in [77], [82] treated the MRTA problem by handling spatial, temporal and energetic constraints.…”

Section: B Optimization-based Approachesmentioning

confidence: 99%

A Distributed Approach to the Multi-Robot Task Allocation Problem Using the Consensus-Based Bundle Algorithm and Ant Colony System

2020

View full text Add to dashboard Cite

We propose a distributed approach to solve the multi-robot task allocation problem. This problem consists of two distinct sets: robots and tasks. The objective is to assign tasks to robots while optimizing a given criterion. This problem is known to be N P-hard even with small numbers of robots and tasks. The field of survivors' search and rescue is adopted: i.e. some Unmanned Aerial Vehicles are used to rescue a number of survivors. We choose this problem, given its importance in everyday life: (a) survivors are the tasks; (b) Unmanned Aerial Vehicles are the robots; and (c) the objective is to rescue the maximum number of survivors while minimizing the makespan (time elapsed between rescuing the first and last survivors) and traveled distances. The approach is composed of two phases: inclusion and consensus. During the inclusion phase, each Unmanned Aerial Vehicle builds a bundle of survivors using the Ant Colony System. During the consensus phase, Unmanned Aerial Vehicles resolve conflicts in their bundles of survivors (i.e. a survivor is being chosen by more than two Unmanned Aerial Vehicles), using an adequate coordination mechanism. The approach is implemented using Java programming language and JADE multi-agent Framework. The performance of our approach is compared to five state-of-the-art multi-robot task allocation solutions. Simulation results show that the proposed approach outperforms these solutions, in terms of: (i) makespans; (ii) traveled distances; and (iii) exchanged messages.INDEX TERMS Ant colony system, consensus-based bundle algorithm, coordination, multi-robot systems, multi-robot task allocation problem, resolution of conflicts.

show abstract

“…At present, unmanned systems research is widely concerned in military and civilian fields, especially unmanned systems with autonomous capabilities [1][2][3][4][5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Ontology-based task planning for autonomous unmanned system: framework and principle

Pang¹,

Gu²,

Li³

2022

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

Operational knowledge is an important factor to improve the autonomy of unmanned systems. How to store and represent operational knowledge semantically and normatively, so that unmanned systems can autonomously accomplish task planning according to the high-level order of humans, carry out re-planning, and make decisions has been a new topic in the autonomous unmanned system field. This paper reviews several typical robot ontologies, as well as modeling and representation methods of uncertain information, proposes the concept of Ontology-based Task Planning(OTP) for the unmanned system, and introduces the principles of ontology construction. Finally, the application of ontology in unmanned combat systems are discussed.

show abstract

Multi-Robot Task Allocation Based on Swarm Intelligence

Cited by 14 publications

References 15 publications

A Novel Multi-Robot Task Allocation Model in Marine Plastics Cleaning Based on Replicator Dynamics

A Novel Multi-Robot Task Allocation Model in Marine Plastics Cleaning Based on Replicator Dynamics

A Distributed Approach to the Multi-Robot Task Allocation Problem Using the Consensus-Based Bundle Algorithm and Ant Colony System

Ontology-based task planning for autonomous unmanned system: framework and principle

Contact Info

Product

Resources

About