Decentralised submodular multi-robot Task Allocation

Segui‐Gasco, Pau; Shin, Hyo‐Sang; Tsourdos, Antonios; Segui, Vicente Jesus

doi:10.1109/iros.2015.7353766

Cited by 23 publications

(23 citation statements)

References 15 publications

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“…Efficient approximation algorithms are hard to deisgn. This is different from task allocation problems without constraints, which have a constant factor approximation for positive valued submodular utility functions [Segui-Gasco et al, 2015].…”

Section: Time Window Constraintsmentioning

confidence: 98%

A taxonomy for task allocation problems with temporal and ordering constraints

Nunes

Manner

Mitiche

et al. 2017

Robotics and Autonomous Systems

190

122

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Previous work on assigning tasks to robots has proposed extensive categorizations of allocation of tasks with and without constraints. The main contribution of this paper is a specific categorization of problems that have temporal and ordering constraints. We propose a novel taxonomy that emphasizes the differences between temporal and ordering constraints, and organizes the current literature according to the nature of those constraints. We summarize widely used models and methods from the task allocation literature and related areas, such as vehicle routing and scheduling problems, showing similarities and differences.

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Section: Time Window Constraintsmentioning

confidence: 98%

A taxonomy for task allocation problems with temporal and ordering constraints

Nunes

Manner

Mitiche

et al. 2017

Robotics and Autonomous Systems

190

122

View full text Add to dashboard Cite

show abstract

“…The inclusion of time windows makes it hard to design efficient approximation algorithms. This is not the case for constraint-free task allocation problems, which accept greedy constant factor approximation for sub-modular utility functions [77].…”

Section: Time Window Constraintsmentioning

confidence: 99%

Decentralized allocation of tasks with temporal and precedence constraints to a team of robots

Nunes

McIntire

Gini

2016

2016 IEEE International Conference on Simulation, Modeling, and Programming for Autonomous Robots (SIMPAR)

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We validate the framework in simulation and also run experiments in a robotic testbed with three Turtlebot 2 robots. Additionally, we leverage the power of simulation as a schedule evaluation tool. We present risk and probabilistic analysis that enable users to assess when to readjust tasks' constraints to improve task completion. Taken together, this thesis proposes methods that divide tasks and constraints among the robots, such that each robot controls a subset of the constraints. This decomposition leads to low computational costs, flexibility to handle local failures, and greater individual robot autonomy. These features are important in designing responsive systems for groups of robots that operate in environments where exogenous events are common and may affect robot performance.

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“…independence constraint. In this case, task allocation can be defined as submodular welfare problem [11], [12]. The issue with the task allocation problem is that it has been proven to be NP-hard [13] which means that finding the optimal solution requires exponential time.…”

Section: Introductionmentioning

confidence: 99%

Efficient Decentralized Task Allocation for UAV Swarms in Multi-target Surveillance Missions

Shin

Tsourdos

2019

2019 International Conference on Unmanned Aircraft Systems (ICUAS)

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This paper deals with the large-scale task allocation problem for Unmanned Aerial Vehicle (UAV) swarms in surveillance missions. The task allocation problem is proven to be NP-hard which means that finding the optimal solution requires exponential time. This paper presents a practically efficient decentralized task allocation algorithm for UAV swarms based on lazy sample greedy. The proposed algorithm can provide a solution with an expected optimality ratio of at least p for monotone submodular objective functions and of p(1 − p) for non-monotone submodular objective functions. The individual computational complexity for each UAV is O(pr 2), where p ∈ (0, 0.5] is the sampling probability, r is the number of tasks. The performance of the proposed algorithm is testified through digital simulations of a multitarget surveillance mission. Simulation results indicate that the proposed algorithm achieves a comparable solution quality to state-of-the-art algorithms with dramatically less running time. Moreover, a trade-off between the solution quality and the running time is obtained by adjusting the sampling probability.

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Decentralised submodular multi-robot Task Allocation

Cited by 23 publications

References 15 publications

A taxonomy for task allocation problems with temporal and ordering constraints

A taxonomy for task allocation problems with temporal and ordering constraints

Decentralized allocation of tasks with temporal and precedence constraints to a team of robots

Efficient Decentralized Task Allocation for UAV Swarms in Multi-target Surveillance Missions

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