Multi-Level Partitioning and Distribution of the Assignment Problem for Large-Scale Multi-Robot Task Allocation

Liu, Lantao; Shell, Dylan A.

doi:10.15607/rss.2011.vii.026

Cited by 19 publications

(11 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other methods for task assignment operate by partitioning the robots and/or tasks into subgroups and repeating the process recursively [20,25,17], as well as behavior-based or rolebased strategies [23]. Recently, we proposed an assignment method independent of the market-based framework [18].…”

Section: B Multi-robot Task Allocation Methodsmentioning

confidence: 99%

Optimal Market-based Multi-Robot Task Allocation via Strategic Pricing

Liu¹,

Shell²

2013

Robotics: Science and Systems IX

Self Cite

View full text Add to dashboard Cite

Abstract-Auction and market-based mechanisms are among the most popular methods for distributed task allocation in multirobot systems. Most of these mechanisms were designed in a heuristic way and analysis of the quality of the resulting assignment solution is rare. This paper presents a new market-based multi-robot task allocation algorithm that produces optimal assignments. Rather than adopting a buyer's "selfish" bidding perspective as in previous auction/market-based approaches, the proposed method approaches auctioning from a merchant's point of view, producing a pricing policy that responds to cliques of customers. The algorithm uses price escalation to clear a market of all its goods, producing a state of equilibrium that satisfies both the merchant and customers. The proposed method can be used as a general assignment algorithm as it has a time complexity (O(n 3 lgn)) close to the fastest state-of-the-art algorithms (O(n 3 )) but is extremely easy to implement. As in previous research, the economic model reflects the distributed nature of markets inherently: in this paper it leads directly to a decentralized method ideally suited for distributed multi-robot systems.

show abstract

Section: B Multi-robot Task Allocation Methodsmentioning

confidence: 99%

Optimal Market-based Multi-Robot Task Allocation via Strategic Pricing

Liu¹,

Shell²

2013

Robotics: Science and Systems IX

Self Cite

View full text Add to dashboard Cite

show abstract

“…k t − d = k t + d and a switching occurs. Theorem 4: The switched multi-robot system (7) under the OGA policy does not suffer from Zeno points.…”

Section: Avoiding Zeno Behaviormentioning

confidence: 99%

“…In the interest of space, here we can not provide an overview of relevant formulations and approaches on target assignment problems in multi-robot networks. A more detailed introduction and literature review is provided in our previous work [1]; the reader is also referred to [2]- [7] and the references therein.…”

Section: Introductionmentioning

confidence: 99%

Decentralized goal assignment and trajectory generation in multi-robot networks: A multiple Lyapunov functions approach

Panagou

Turpin

Kumar

2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

View full text Add to dashboard Cite

This paper considers the problem of decentralized goal assignment and trajectory generation for multi-robot networks when only local communication is available, and proposes an approach based on methods related to switched systems and set invariance. A family of Lyapunov-like functions is employed to encode the (local) decision making among candidate goal assignments, under which the agents pick the assignment which results in the shortest total distance to the goals. An additional family of Lyapunov-like barrier functions is activated in the case when the optimal assignment may lead to colliding trajectories, maintaining thus system safety while preserving the convergence guarantees. The proposed switching strategies give rise to feedback control policies which are scalable and computationally efficient as the number of agents increases, and therefore are suitable for applications including first-response deployment of robotic networks under limited information sharing. Simulations demonstrate the efficacy of the proposed method.

show abstract

“…The distributed task allocation approach handles this shortcoming [16,17]. In a distributed multirobot system, each robot operates independently under local sensing, with high-level system coordination arising from interactions with other robots as well as the task environment [18].…”

Section: Dynamic Task Allocationmentioning

confidence: 99%

Task Allocation and Path Planning for Collaborative Autonomous Underwater Vehicles Operating through an Underwater Acoustic Network

Deng

Beaujean

et al. 2013

Journal of Robotics

View full text Add to dashboard Cite

Dynamic and unstructured multiple cooperative autonomous underwater vehicle (AUV) missions are highly complex operations, and task allocation and path planning are made significantly more challenging under realistic underwater acoustic communication constraints. This paper presents a solution for the task allocation and path planning for multiple AUVs under marginal acoustic communication conditions: a location-aided task allocation framework (LAAF) algorithm for multitarget task assignment and the grid-based multiobjective optimal programming (GMOOP) mathematical model for finding an optimal vehicle command decision given a set of objectives and constraints. Both the LAAF and GMOOP algorithms are well suited in poor acoustic network condition and dynamic environment. Our research is based on an existing mobile ad hoc network underwater acoustic simulator and blind flooding routing protocol. Simulation results demonstrate that the location-aided auction strategy performs significantly better than the well-accepted auction algorithm developed by Bertsekas in terms of task-allocation time and network bandwidth consumption. We also demonstrate that the GMOOP path-planning technique provides an efficient method for executing multiobjective tasks by cooperative agents with limited communication capabilities. This is in contrast to existing multiobjective action selection methods that are limited to networks where constant, reliable communication is assumed to be available.

show abstract

Multi-Level Partitioning and Distribution of the Assignment Problem for Large-Scale Multi-Robot Task Allocation

Cited by 19 publications

References 35 publications

Optimal Market-based Multi-Robot Task Allocation via Strategic Pricing

Optimal Market-based Multi-Robot Task Allocation via Strategic Pricing

Decentralized goal assignment and trajectory generation in multi-robot networks: A multiple Lyapunov functions approach

Task Allocation and Path Planning for Collaborative Autonomous Underwater Vehicles Operating through an Underwater Acoustic Network

Contact Info

Product

Resources

About