Assessing Optimal Assignment under Uncertainty: An Interval-based Algorithm

Liu, Lantao; Shell, Dylan A.

doi:10.15607/rss.2010.vi.016

Cited by 5 publications

(3 citation statements)

References 7 publications

(10 reference statements)

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“…The work by Gerkey and Matarić [39] and the more recent work by Korsah et al [40] contain detailed survey of this problem. There exists distributed algorithms with provable guarantees for different versions of this problem [41][42][43]. There also exists various multi-robot deployment strategies for task assignment under communication constraints.…”

Section: Multi-robot Task Assignmentmentioning

confidence: 99%

Distributed assignment with limited communication for multi-robot multi-target tracking

et al. 2019

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We study the problem of tracking multiple moving targets using a team of mobile robots. Each robot has a set of motion primitives to choose from in order to collectively maximize the number of targets tracked or the total quality of tracking. Our focus is on scenarios where communication is limited and the robots have limited time to share information with their neighbors. As a result, we seek distributed algorithms that can find solutions in a bounded amount of time. We present two algorithms: (1) a greedy algorithm that is guaranteed to find a 2-approximation to the optimal (centralized) solution but requiring |R| communication rounds in the worst case, where |R| denotes the number of robots; and (2) a local algorithm that finds a O ((1 + )(1 + 1/h))-approximation algorithm in O(h log 1/ ) communication rounds. Here, h and are parameters that allow the user to trade-off the solution quality with communication time. In addition to theoretical results, we present empirical evaluation including comparisons with centralized optimal solutions.

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Section: Multi-robot Task Assignmentmentioning

confidence: 99%

Distributed assignment with limited communication for multi-robot multi-target tracking

et al. 2019

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“…Uncertainty has been considered by several papers, but has been dealt with by each in different ways. The interval Hungarian algorithm was developed in [4] to deal with problems that have uncertainty in the utility estimate of a given assignment. This method relies on knowing the Probability Density Function (PDF) describing the utility.…”

Section: Related Literature and Backgroundmentioning

confidence: 99%

“…The techniques developed in this paper for modelling these effects are applicable to a wide range of scenarios such as machine shop scheduling [1], as well as other robotics problems such as multi-robot path planning [2], and planning for recharging robots [3]. While there is literature on MRTA problems that incorporate either uncertainty [4]- [7] or resource contention [8], [9], to the best of the authors' This work was supported by the Rio Tinto Centre for Mine Automation and the Australian Centre for Field Robotics, University of Sydney, Australia.…”

Section: Introductionmentioning

confidence: 99%

Modelling Resource Contention in Multi-Robot Task Allocation Problems with Uncertain Timing

Palmer

Hill

Scheding

2018

2018 IEEE International Conference on Robotics and Automation (ICRA)

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This paper proposes an analytical framework for modelling resource contention in multi-robot systems, where the travel times and task durations are uncertain. It uses several approximation methods to quickly and accurately calculate the probability distributions describing the times at which the tasks start and finish. Specific contributions include a method for calculating the probability of a set of independent normally distributed random events occurring in a given order, an upper bound on that probability, and a method for calculating the most likely and n-th most likely orders of occurrence for a set of independent normally distributed random events that have equal standard deviations. The complete framework is shown to be much faster than a Monte Carlo approach for the same accuracy in two multi-robot task allocation problems. This is a general framework that is agnostic to the optimisation method and objective function used, and is applicable to a wide range of robotics and non-robotics problems.

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Multi Aerial Robot Planning

Sebbane

2014

Intelligent Systems, Control and Automation: Science and Engineering

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Multi-robot systems are a major research topic in robotics. Designing, testing and deploying in the real world a large number of aerial robots is a concrete possibility due to the recent technological advances. The first section of this chapter treats the different aspects of cooperation in a multi-agent systems. A cooperative control should be designed in terms of the available feedback information. A cascade-type guidance law is proposed, followed by consensus approach and flocking behavior. Since information flow over the network changes over time, cooperative control must react accordingly but ensure group cooperative behavior which is the major issue in analysis and synthesis. Connectivity and convergence of formations are also studied. Team approach is followed by deterministic decision making. Plans may be required for a team of aerial robots to plan for sensing, plan for action or plan for communication. Distributed receding horizon control as well as conflict resolution, artificial potentials and symbolic planning are thus analyzed. Then, association with limited communications is studied, followed by genetic algorithms and game theory reasoning. Next, multi-agent decision making under uncertainty is considered, formulating the Bayesian decentralized team decision problem, with and without explicit communication. Algorithms for optimal planning are then introduced as well as for task allocation and distributed chance constrained task allocation. Finally, some case studies are presented such as reconnaissance mission that can be defined as the road search problem or the general vehicle routing problem. Then, an approach is considered to coordinate a group of aerial robots without a central supervision, by using only local interactions between the robots. The third case is the optimization of perimeter patrol operation. If an aerial robot must be close from a location to monitor it correctly and the number of aerial robots does not allow covering each site simultaneously, a path planning problem arises. Finally stochastic strategies for surveillance are presented.Y.

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Assessing Optimal Assignment under Uncertainty: An Interval-based Algorithm

Cited by 5 publications

References 7 publications

Distributed assignment with limited communication for multi-robot multi-target tracking

Distributed assignment with limited communication for multi-robot multi-target tracking

Modelling Resource Contention in Multi-Robot Task Allocation Problems with Uncertain Timing

Multi Aerial Robot Planning

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