Adaptive Task Allocation Based on Social Utility and Individual Preference in Distributed Environments

Iijima, Naoki; Sugiyama, Ayumi; Hayano, Masashi; Sugawara, Toshiharu

doi:10.1016/j.procs.2017.08.177

Cited by 13 publications

(7 citation statements)

References 6 publications

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“…Typically, adaptivity is incorporated by defining a time-varying propensity of robots to participate in different tasks. These measures of utility are based on predefined objective functions and aim to capture the effectiveness of the robots at performing tasks in real-time [31]. Such frameworks, however, do not account for drastic unexpected failures in the capabilities of the robots, adversarial attacks, or varying environmental conditions that might affect the operations of the robots.…”

Section: B Related Workmentioning

confidence: 99%

A Resilient and Energy-Aware Task Allocation Framework for Heterogeneous Multi-Robot Systems

Notomista¹,

Mayya²,

Emam³

et al. 2021

Preprint

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In the context of heterogeneous multi-robot teams deployed for executing multiple tasks, this paper develops an energy-aware framework for allocating tasks to robots in an online fashion. With a primary focus on long-duration autonomy applications, we opt for a survivability-focused approach. Towards this end, the task prioritization and execution-through which the allocation of tasks to robots is effectively realizedare encoded as constraints within an optimization problem aimed at minimizing the energy consumed by the robots at each point in time. In this context, an allocation is interpreted as a prioritization of a task over all others by each of the robots. Furthermore, we present a novel framework to represent the heterogeneous capabilities of the robots, by distinguishing between the features available on the robots, and the capabilities enabled by these features. By embedding these descriptions within the optimization problem, we make the framework resilient to situations where environmental conditions make certain features unsuitable to support a capability and when component failures on the robots occur. We demonstrate the efficacy and resilience of the proposed approach in a variety of use-case scenarios, consisting of simulations and real robot experiments.

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Section: B Related Workmentioning

confidence: 99%

A Resilient and Energy-Aware Task Allocation Framework for Heterogeneous Multi-Robot Systems

Notomista¹,

Mayya²,

Emam³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Adaptive task allocation for heterogeneous multi-agent systems is a well-studied topic [1], [9], [10], [11], [12], [13], [14]. A common trend in these existing frameworks is to let each agent have a preference towards the various tasks depending on its current state and corresponding objective (e.g.…”

Section: Literature Reviewmentioning

confidence: 99%

“…A common trend in these existing frameworks is to let each agent have a preference towards the various tasks depending on its current state and corresponding objective (e.g. [10], [13]). The corresponding utility function of each agent is usually pre-determined based on its role or predefined strategy and serves to represent the effectiveness of the agent at performing a task in real-time.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The task allocation is then performed using market-based, centralized, decentralized, or learning protocols based on the values of the robots' so-called utility functions in real-time. For example, in [13], the multi-agent system is composed of computational entities each having specific functionalities and resources. The authors formulate a method based on ε-greedy Q-learning to assign the incoming stream of tasks to agents depending on their respective preferences.…”

Section: Literature Reviewmentioning

confidence: 99%

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Adaptive Task Allocation for Heterogeneous Multi-Robot Teams with Evolving and Unknown Robot Capabilities

Emam¹,

Mayya²,

Notomista³

et al. 2020

Preprint

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For multi-robot teams with heterogeneous capabilities, typical task allocation methods assign tasks to robots based on the suitability of the robots to perform certain tasks as well as the requirements of the task itself. However, in real-world deployments of robot teams, the suitability of a robot might be unknown prior to deployment, or might vary due to changing environmental conditions. This paper presents an adaptive task allocation and task execution framework which allows individual robots to prioritize among tasks while explicitly taking into account their efficacy at performing the tasks-the parameters of which might be unknown before deployment and/or might vary over time. Such a specialization parameter-encoding the effectiveness of a given robot towards a task-is updated on-the-fly, allowing our algorithm to reassign tasks among robots with the aim of executing them. The developed framework requires no explicit model of the changing environment or of the unknown robot capabilities-it only takes into account the progress made by the robots at completing the tasks. Simulations and experiments demonstrate the efficacy of the proposed approach during variations in environmental conditions and when robot capabilities are unknown before deployment.

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“…Li et al proposed to dynamically select appropriate workers for given tasks while keeping the constraints satisfied [22], Jin et al incorporated quality of data to design the incentive mechanisms for MCS systems [25]. Iijima et al considered the individual preference in distributed environments and proposed an adaptive task allocation mechanism that maximizes the social utility [32].…”

Section: Introductionmentioning

confidence: 99%

Time-Efficient Allocation Mechanisms for Crowdsensing Tasks with Precedence Constraints

Sun

Huang

et al. 2019

Sensors

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Crowdsensing has emerged as an efficient and inexpensive way to perform specialized tasks by leveraging external crowds. In some crowdsensing systems, different tasks may have different requirements, and there may be precedence constraints among them, such as the Unmanned Aerial Vehicle (UAV) crowdsensing systems. Moreover, minimizing the total execution time is a regular target for finishing the crowdsensing tasks with precedence constraints. As far as we know, only a few existing studies consider the precedence constraints among crowdsensing tasks, and none of them can minimize the total execution time simultaneously. To tackle this challenge, an efficient allocation mechanism for tasks with precedence constraints is first proposed, which can minimize the total execution time. Then, a case study is given to show how to fit our mechanism in the UAV crowdsensing system. Finally, the simulation results show that the proposed mechanisms have good approximate optimal ratios under different parameter settings and are efficient for the UAV crowdsensing system as well.

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Adaptive Task Allocation Based on Social Utility and Individual Preference in Distributed Environments

Cited by 13 publications

References 6 publications

A Resilient and Energy-Aware Task Allocation Framework for Heterogeneous Multi-Robot Systems

A Resilient and Energy-Aware Task Allocation Framework for Heterogeneous Multi-Robot Systems

Adaptive Task Allocation for Heterogeneous Multi-Robot Teams with Evolving and Unknown Robot Capabilities

Time-Efficient Allocation Mechanisms for Crowdsensing Tasks with Precedence Constraints

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