Improving Multi-Robot Behavior Using Learning-Based Receding Horizon Task Allocation

Schillinger, Philipp; Buerger, Mathias; Dimarogonas, Dimos V.

doi:10.15607/rss.2018.xiv.031

Cited by 7 publications

(5 citation statements)

References 20 publications

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“…However, robot failure is only one possible source of uncertainty. Markov decision processes (MDPs) model systems with non-deterministic action choice and uncertain action outcomes, and have been used for MRTA by Faruq et al (2018) and Schillinger et al (2018) to allocate temporal logic tasks. Faruq et al (2018) build a team model, where robots select tasks and plan sequentially.…”

Section: Mrta Under Uncertaintymentioning

confidence: 99%

“…Faruq et al (2018) build a team model, where robots select tasks and plan sequentially. Schillinger et al (2018) decompose a global temporal logic specification into single-robot tasks which are allocated through an auction, where the synthesised allocation satisfies the global specification. Moreover, Schillinger et al (2018) consider uncertain action durations during bidding.…”

Section: Mrta Under Uncertaintymentioning

confidence: 99%

“…Schillinger et al (2018) decompose a global temporal logic specification into single-robot tasks which are allocated through an auction, where the synthesised allocation satisfies the global specification. Moreover, Schillinger et al (2018) consider uncertain action durations during bidding. This is similar to our framework, where navigation action durations are continuous and stochastic.…”

Section: Mrta Under Uncertaintymentioning

confidence: 99%

See 2 more Smart Citations

Right Place, Right Time: Proactive Multi-Robot Task Allocation Under Spatiotemporal Uncertainty

Street,

Lacerda,

Mühlig

et al. 2024

jair

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For many multi-robot problems, tasks are announced during execution, where task announcement times and locations are uncertain. To synthesise multi-robot behaviour that is robust to early announcements and unexpected delays, multi-robot task allocation methods must explicitly model the stochastic processes that govern task announcement. In this paper, we model task announcement using continuous-time Markov chains which predict when and where tasks will be announced. We then present a task allocation framework which uses the continuous-time Markov chains to allocate tasks proactively, such that robots are near or at the task location upon its announcement. Our method seeks to minimise the expected total waiting duration for each task, i.e. the duration between task announcement and a robot beginning to service the task. Our framework can be applied to any multi-robot task allocation problem where robots complete spatiotemporal tasks which are announced stochastically. We demonstrate the efficacy of our approach in simulation, where we outperform baselines which do not allocate tasks proactively, or do not fully exploit our task announcement models.

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Section: Mrta Under Uncertaintymentioning

confidence: 99%

Section: Mrta Under Uncertaintymentioning

confidence: 99%

Section: Mrta Under Uncertaintymentioning

confidence: 99%

See 1 more Smart Citation

Right Place, Right Time: Proactive Multi-Robot Task Allocation Under Spatiotemporal Uncertainty

Street,

Lacerda,

Mühlig

et al. 2024

jair

View full text Add to dashboard Cite

show abstract

“…It uses linear temporal logic (LTL) to define a high-level mission and task specifications. Some other works use LTL are [163,164]. In [165] motion planning incremental algorithm is presented based on satisfiability modulo theories [166], robots are assigned priorities and divided into groups.…”

Section: Coordinated Task Planningmentioning

confidence: 99%

Multi-Robot Coordination Analysis, Taxonomy, Challenges and Future Scope

Verma

Ranga

2021

J Intell Robot Syst

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Recently, Multi-Robot Systems (MRS) have attained considerable recognition because of their efficiency and applicability in different types of real-life applications. This paper provides a comprehensive research study on MRS coordination, starting with the basic terminology, categorization, application domains, and finally, give a summary and insights on the proposed coordination approaches for each application domain. We have done an extensive study on recent contributions in this research area in order to identify the strengths, limitations, and open research issues, and also highlighted the scope for future research. Further, we have examined a series of MRS state-of-the-art parameters that affect MRS coordination and, thus, the efficiency of MRS, like communication mechanism, planning strategy, control architecture, scalability, and decision-making. We have proposed a new taxonomy to classify various coordination approaches of MRS based on the six broad dimensions. We have also analyzed that how coordination can be achieved and improved in two fundamental problems, i.e., multi-robot motion planning, and task planning, and in various application domains of MRS such as exploration, object transport, target tracking, etc.

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“…Approaches in this space include our prior work [14,15] which uses verificationbased methods to produce probabilistically-guaranteed behaviour policies for a mobile robot, where elements of the MDP are learnt from experience. When extending MDP planning approaches to multi-robot settings, authors either assume communication and sparse interactions between robots in order to maintain full observability and mitigate scalability issues [16,17]; resort to auctioning approaches for TA, thus keeping the planning over single robot models [18,19,20]; or otherwise use the computationallydemanding decentralised, partially-observable MDP (DecPOMDP) formalisation which accounts for the unknown state of other robot in the problem [21]. As is appropriate in many service robot domains, we make the assumption of perfect communication, thus allowing this work to retain the MDP formalisation.…”

Section: Related Workmentioning

confidence: 99%

Simultaneous Task Allocation and Planning Under Uncertainty

Faruq

Parker

Laccrda

et al. 2018

2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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We propose novel techniques for task allocation and planning in multi-robot systems operating in uncertain environments. Task allocation is performed simultaneously with planning, which provides more detailed information about individual robot behaviour, but also exploits independence between tasks to do so efficiently. We use Markov decision processes to model robot behaviour and linear temporal logic to specify tasks and safety constraints. Building upon techniques and tools from formal verification, we show how to generate a sequence of multi-robot policies, iteratively refining them to reallocate tasks if individual robots fail, and providing probabilistic guarantees on the performance (and safe operation) of the team of robots under the resulting policy. We implement our approach and evaluate it on a benchmark multi-robot example.

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Improving Multi-Robot Behavior Using Learning-Based Receding Horizon Task Allocation

Cited by 7 publications

References 20 publications

Right Place, Right Time: Proactive Multi-Robot Task Allocation Under Spatiotemporal Uncertainty

Right Place, Right Time: Proactive Multi-Robot Task Allocation Under Spatiotemporal Uncertainty

Multi-Robot Coordination Analysis, Taxonomy, Challenges and Future Scope

Simultaneous Task Allocation and Planning Under Uncertainty

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