Multi-robot exploration controlled by a market economy

Zlot, Robert; Stentz, Anthony; Dias, M. Bernardine; Thayer, Scott

doi:10.1109/robot.2002.1013690

Cited by 361 publications

(331 citation statements)

References 8 publications

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“…A goal point is created for each such group, and is located at the arithmetic mean of all points in the group. Other approaches to goal identification that have been implemented include random point selection, greedy exploration, map segmentation [14] and quad-tree subdivision [16]. Once determined, a variety of approaches exist to assign these to robots.…”

Section: Related Workmentioning

confidence: 99%

“…Teams of robots can fan out and visit locations in parallel to make the overall discovery process more efficient, and a variety of approaches have been proposed to coordinate this process. One common approach to coordinating multiple robots is through the use of market-based schemes for task allocation [5,6,13,16]. When a new task is given to the team (or discovered by a team member, in the case of exploration), the robots bid on the right to take on that task.…”

Section: Introductionmentioning

confidence: 99%

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Hierarchical Distributed Task Allocation for Multi-robot Exploration

Hawley

Butler

2013

Springer Tracts in Advanced Robotics

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In order to more effectively explore a large unknown area, multiple robots may be employed to work cooperatively. When properly done, the group allocates specific portions of the overall exploration task to different robots such that the entire environment is explored with minimal excess effort. In this work, we present a new hierarchical market-based approach to this allocation problem. Our approach builds on standard auction approaches to provide agents with a mechanism to independently form coalitions and to divide a coalition into smaller coalitions in response to the progress of their cooperative exploration process. These coalitions allow a subset of the team to move together efficiently, especially in constrained environments when there are few avenues for exploration. We also present implementation and simulated experiments which show how this natural hierarchy forms and can lead to more efficient exploration than using a greedy allocation technique or without the use of coalitions.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hierarchical Distributed Task Allocation for Multi-robot Exploration

Hawley

Butler

2013

Springer Tracts in Advanced Robotics

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“…2). These frontiers, thus, represent locations that are reachable from within the partial map and provide opportunities for exploring unknown terrain, thereby allowing the robots to greedily maximize information gain [19]. However, these methods should have a strategy to avoid sending two robots toward the same frontier.…”

Section: Multi-robot Task Sharing and Cooperationmentioning

confidence: 99%

Multi-robot olfactory search in structured environments

Marjovi

Marques

2011

Robotics and Autonomous Systems

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This paper presents a cooperative distributed approach for searching odor sources in unknown structured environments with multiple mobile robots . While searching and exploring the environment, the robots independently generate on-line local topological maps and by sharing them with each other they construct a global map. The proposed method is a decentralized frontier based algorithm enhanced by a cost/utility evaluation function that considers the odor concentration and airflow at each frontier. Therefore, frontiers with higher probability of containing an odor source will be searched and explored first. The method also improves path planning of the robots for exploration process by presenting a priority policy. Since there is no global positioning system and each robot has its own coordinate reference system for its localization, this paper uses topological graph matching techniques for map merging. The proposed method was tested in both simulation and real world environments with different number of robots and different scenarios. The search time, exploration time, complexity of the environment and number of double-visited map nodes were investigated in the tests. The experimental results validate the functionality of the method in different configurations.

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“…Frontiers are the borders of the partial map, between explored free space and unexplored area. These borders, thus, represent locations that are reachable from within the partial map and provide opportunities for exploring unknown terrain, thereby allowing the robots to greedily maximize information gain [10]. However, methodologies should have a strategy to not send two robots toward the same frontier.…”

Section: Introductionmentioning

confidence: 99%

Multi-robot Topological Exploration Using Olfactory Cues

Marjovi

Marques

2013

Springer Tracts in Advanced Robotics

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Abstract. This paper presents a distributed multi-robot system to search for odor sources inside unknown environments. The robots cooperatively explore the whole environment and generate its topological map. The exploration method is a decentralized frontier based algorithm that is enhanced by considering odor concentration at each frontier inside its cost/gain function. The robots independently generate local topological maps and by transferring them to each other, they are able to integrate these maps and generate a whole global map. The proposed method was tested and validated in real reduced scale scenarios.

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Multi-robot exploration controlled by a market economy

Cited by 361 publications

References 8 publications

Hierarchical Distributed Task Allocation for Multi-robot Exploration

Hierarchical Distributed Task Allocation for Multi-robot Exploration

Multi-robot olfactory search in structured environments

Multi-robot Topological Exploration Using Olfactory Cues

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