TAMER: Task Allocation in Multi-robot Systems Through an Entity-Relationship Model

Miloradović, Branko; Frasheri, Mirgita; Çürüklü, Baran; Ekström, Mikael; Papadopoulos, Alessandro Vittorio

doi:10.1007/978-3-030-33792-6_32

Cited by 7 publications

(4 citation statements)

References 12 publications

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“…A more detailed comparison, from the problem definition viewpoint, is provided by expressing the problems addressed in related works, as well as the problem tackled in this paper, through the TAMER model proposed by Miloradović et al [15] (Tab. II).…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

GLocal: A Hybrid Approach to the Multi-Agent Mission Re-Planning Problem

Frasheri,

Miloradović,

Esterle

et al. 2023

2023 IEEE Symposium Series on Computational Intelligence (SSCI)

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Multi-robot systems can be prone to failures during plan execution, depending on the harshness of the environment they are deployed in. As a consequence, initially devised plans may no longer be feasible, and a re-planning process needs to take place to re-allocate any pending tasks. Two main approaches emerge as possible solutions, a global re-planning technique using a centralized planner that will redo the task allocation with the updated world state information, or a decentralized approach that will focus on the local plan reparation, i.e., the re-allocation of those tasks initially assigned to the failed robots. The former approach produces an overall better solution, while the latter is less computationally expensive. The goal of this paper is to exploit the benefits of both approaches, while minimizing their drawbacks. To this end, we propose a hybrid approach that combines a centralized planner with decentralized multi-agent planning. In case of an agent failure, the local plan reparation algorithm tries to repair the plan through agent negotiation.If it fails to re-allocate all of the pending tasks, the global replanning algorithm is invoked, which re-allocates all unfinished tasks from all agents. The hybrid approach was compared to planner approach, and it was shown that it improves on the makespan of a mission in presence of different numbers of failures, as a consequence of the local plan reparation algorithm.

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Section: Related Workmentioning

confidence: 99%

“…Using the TAMER model[15] to classify the problems addressed in combined centralized and decentralized planning.…”

mentioning

confidence: 99%

GLocal: A Hybrid Approach to the Multi-Agent Mission Re-Planning Problem

Frasheri,

Miloradović,

Esterle

et al. 2023

2023 IEEE Symposium Series on Computational Intelligence (SSCI)

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“…Several taxonomies were proposed to describe MRTA problems [33]- [35]. More recently, the TAMER model [36] was proposed, which utilizes the entityrelationship model in order to give a more comprehensive taxonomy of mission planning problems. Following these MRTA-related taxonomies, the ECTSP can be categorized as single-task robots, single-robot tasks, and time-extended assignments (ST-SR-TA) [33] with intraschedule dependencies (ID) [34], PC [35], multiple source/destination depots, and heterogeneous capabilities [36].…”

Section: Related Workmentioning

confidence: 99%

GMP: A Genetic Mission Planner for Heterogeneous Multirobot System Applications

Miloradović¹,

Çürüklü²,

Ekström³

et al. 2022

IEEE Trans. Cybern.

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The use of multiagent systems (MASs) in real-world applications keeps increasing, and diffuses into new domains, thanks to technological advances, increased acceptance, and demanding productivity requirements. Being able to automate the generation of mission plans for MASs is critical for managing complex missions in realistic settings. In addition, finding the right level of abstraction to represent any generic MAS mission is important for being able to provide general solution to the automated planning problem. In this article, we show how a mission for heterogeneous MASs can be cast as an extension of the traveling salesperson problem (TSP), and we propose a mixed-integer linear programming formulation. In order to solve this problem, a genetic mission planner (GMP), with a local plan refinement algorithm, is proposed. In addition, the comparative evaluation of CPLEX and GMP is presented in terms of timing and optimality of the obtained solutions. The algorithms are benchmarked on a proposed set of different problem instances. The results show that, in the presence of timing constraints, GMP outperforms CPLEX in the majority of test instances. Index Terms-Extended colored traveling salesperson problem (ECTSP), genetic algorithm (GA), multirobot mission planning, multirobot systems.

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“…The communication topology therein is assumed to be structurally balanced. Instead of the case of dividing the whole network into two antagonistic interactions, 14 in several interesting scenarios, a group of agents might split into several interacting subgroups, in which the agents tend to reach different consensus values with the different task distributions in MASs, such as the multi‐robot task allocation 15 . It is termed group consensus (different from the cluster consensus, in group consensus problem agents from diverse clusters may be consensus).…”

Section: Introductionmentioning

confidence: 99%

H_∞ group consensus of linear dynamical systems under directed switching topology

Ren

Liu

Sun

2020

Intl J Robust & Nonlinear

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This brief investigates the H ∞ group consensus problem for linear dynamical systems with directed switching topology and external disturbance. The H ∞ group consensus problem for multiagent systems (MASs) is complicated since the agents from different clusters may be cooperative or competitive. By using together algebraic graph theory and Lyapunov stability theory, we first analyze the stability of group consensus with desired H ∞ performance, and then establish a sufficient condition to achieve the H ∞ group consensus. This sufficient condition is derived in terms of the strengths of intra-cluster couplings and the solution to an algebraic Riccati equation, whose solvability can be guaranteed if the MAS is state feedback stabilized with a bounded L 2 gain. Moreover, the lower bound of the inter-cluster coupling strength and dwell time are explicitly specified. Finally, the effectiveness of the theoretical results is verified by a numerical example.

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TAMER: Task Allocation in Multi-robot Systems Through an Entity-Relationship Model

Cited by 7 publications

References 12 publications

GLocal: A Hybrid Approach to the Multi-Agent Mission Re-Planning Problem

GLocal: A Hybrid Approach to the Multi-Agent Mission Re-Planning Problem

GMP: A Genetic Mission Planner for Heterogeneous Multirobot System Applications

H_∞ group consensus of linear dynamical systems under directed switching topology

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TAMER: Task Allocation in Multi-robot Systems Through an Entity-Relationship Model

Cited by 7 publications

References 12 publications

GLocal: A Hybrid Approach to the Multi-Agent Mission Re-Planning Problem

GLocal: A Hybrid Approach to the Multi-Agent Mission Re-Planning Problem

GMP: A Genetic Mission Planner for Heterogeneous Multirobot System Applications

H∞ group consensus of linear dynamical systems under directed switching topology

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Product

Resources

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H_∞ group consensus of linear dynamical systems under directed switching topology