A Formal Analysis and Taxonomy of Task Allocation in Multi-Robot Systems

Gerkey, Brian; Matarić, Maja J.

doi:10.1177/0278364904045564

Cited by 1,340 publications

(977 citation statements)

References 61 publications

(92 reference statements)

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“…They assist agents to achieve their objectives and to maximize the benefits of the system. There are works that address the task-scheduling problem in multi-agent systems [14,15], multi-robot systems [16], disaster-emergency teams [17], robocup rescue simulations [18], and strategic decision making for coordinating actions of a USAR team [19].…”

Section: Literature Reviewmentioning

confidence: 99%

Dynamic Assignment of Geospatial-Temporal Macro Tasks to Agents under Human Strategic Decisions for Centralized Scheduling in Multi-Agent Systems

Nourjou¹,

Smith²,

Hatayama³

et al. 2014

IJMLC

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Abstract-Problem: This paper addresses a centralized scheduling problem in multi-agent systems in which the incident commander (IC) of a disaster-response team aims to coordinate the actions of the field units (rational agents) to minimize the total operation time in uncertain, dynamic, and spatial environments.Objective: The purpose of this paper is to propose an intelligent software system that assists the IC in the dynamic assignment of geospatial-temporal macro tasks to agents under human strategic decisions. This system autonomously executes a heuristic algorithm to minimize the maximum total dependent duration according to human high-level strategies.Results: The result is a schedule composed of macro decisions, each comprised of seven types of information: 1) what task type is going to be accomplished, 2) who (a subset of agents) are assigned to this assignment, 3) where this task is to be performed (a road segment or zone as a macro geospatial object) containing a subset of tasks, 4) when operations start, 5) when operations finish, 6) how many tasks are estimated to be completed, 7) what task types and the estimated number to be revealed (identified" or "enabled) in this location to complete this job.Conclusion: This result, which is a feasible solution for the addressed problem, permits the IC to coordinate agents, partially specify the activities of the agents in time and space, minimize the overall execution time for all the tasks, calculate the correct time to revise the strategic decisions, evaluate the efficiency of the high-level strategy, and estimate the makespan.

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Section: Literature Reviewmentioning

confidence: 99%

Dynamic Assignment of Geospatial-Temporal Macro Tasks to Agents under Human Strategic Decisions for Centralized Scheduling in Multi-Agent Systems

Nourjou¹,

Smith²,

Hatayama³

et al. 2014

IJMLC

View full text Add to dashboard Cite

show abstract

“…The approach investigated in this paper for task modeling and distribution is based on models of the specific sense, plan and/or act abilities available in one or several robots which are required to achieve certain tasks. Therefore it differs from standard approaches and taxonomies for multi-robot task allocation like [11,14,25] which operate on more abstract levels that do not directly take into account specific sense, plan or act abilities of the robots.…”

Section: General Considerationsmentioning

confidence: 99%

“…e.g. [11,13,14,25]. However, unlike most previous approaches the utility function used in this paper is based on the specific sense/plan/act abilities of the robots required to achieve certain tasks (Sect.…”

Section: Task Modeling and Assignmentmentioning

confidence: 99%

Towards cooperation of heterogeneous, autonomous robots: A case study of humanoid and wheeled robots

Kiener

Stryk

2010

Robotics and Autonomous Systems

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In this paper a case study of cooperation of a strongly heterogeneous autonomous robot team, composed of a highly articulated humanoid robot and a wheeled robot with largely complementing and some redundant abilities is presented. By combining strongly heterogeneous robots the diversity of achievable tasks increases as the variety of sensing and motion abilities of the robot system is extended compared to a usually considered team of homogeneous robots. A number of methodologies and technologies required to achieve the long-term goal of cooperation of heterogeneous autonomous robots are discussed including modeling tasks and robot abilities, task assignment and redistribution, robot behavior modeling and programming, robot middleware and robot simulation. Example solutions and their application to the cooperation of autonomous wheeled and humanoid robots are presented in this case study. The scenario describes a tightly coupled cooperative task, where the humanoid robot and the wheeled robot track a moving ball, which is to be approached and kicked by the humanoid robot into a goal. The task can be fulfilled successfully by combining the abilities of both robots.

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“…In the area of cooperative robotics, many works address the problem of task allocation [25]. Task allocation typically deals with the question: "Who should do which task?".…”

Section: Related Workmentioning

confidence: 99%

Autonomous functional configuration of a network robot system

Lundh

Karlsson

Saffiotti

2008

Robotics and Autonomous Systems

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We consider distributed systems of networked robots in which: (1) each robot includes sensing, acting and/or processing modular functionalities; and (2) robots can help each other by offering those functionalities. A functional configuration is any way to allocate and connect functionalities among the robots. An interesting feature of a system of this type is the possibility to use different functional configurations to make the same set of robots perform different tasks, or to perform the same task under different conditions. In this paper, we propose an approach to automatically generate, at run time, a functional configuration of a network robot system to perform a given task in a given environment, and to dynamically change this configuration in response to failures. Our approach is based on artificial intelligence planning techniques, and it is provably sound, complete and optimal. Moreover, our configuration planner can be combined with an action planner to deal with tasks that require sequences of configurations. We illustrate our approach on a specific type of network robot system, called Peis-Ecology, and show experiments in which a sequence of configurations is automatically generated and executed on real robots. These experiments demonstrate that our self-configuration approach can help the system to achieve greater autonomy, flexibility and robustness.

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A Formal Analysis and Taxonomy of Task Allocation in Multi-Robot Systems

Abstract: Abstract

Cited by 1,340 publications

References 61 publications

Dynamic Assignment of Geospatial-Temporal Macro Tasks to Agents under Human Strategic Decisions for Centralized Scheduling in Multi-Agent Systems

Dynamic Assignment of Geospatial-Temporal Macro Tasks to Agents under Human Strategic Decisions for Centralized Scheduling in Multi-Agent Systems

Towards cooperation of heterogeneous, autonomous robots: A case study of humanoid and wheeled robots

Autonomous functional configuration of a network robot system

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