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

In the context of heterogeneous multirobot teams deployed for executing multiple tasks, this article 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. Towar...

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Bibliographic Details
Published in:IEEE transactions on robotics Vol. 38; no. 1; pp. 159 - 179
Main Authors: Notomista, Gennaro, Mayya, Siddharth, Emam, Yousef, Kroninger, Christopher, Bohannon, Addison, Hutchinson, Seth, Egerstedt, Magnus
Format: Journal Article
Language:English
Published: New York IEEE 01.02.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Context
Energy and environment-aware automation
Energy management
failure detection and recovery
Multiple robots
multirobot systems
Optimization
path planning for multiple mobile robots or agents
Resilience
Resource management
Robot kinematics
Robot sensing systems
Robots
robust/adaptive control of robotic systems
Survivability
Task analysis
task planning
ISSN:1552-3098, 1941-0468
Online Access:Get full text
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Summary:In the context of heterogeneous multirobot teams deployed for executing multiple tasks, this article 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. Toward this end, the task prioritization and execution-through which the allocation of tasks to robots is effectively realized-are 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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ISSN:1552-3098
1941-0468
DOI:10.1109/TRO.2021.3102379