Towards sensor based coverage with robot teams

Latimer, DeWitt; Srinivasa, Siddhartha S.; Lee-Shue, Vincent; Sonne, Samuel; Choset, Howie; Hurst, Aaron P.

doi:10.1109/robot.2002.1013480

Cited by 65 publications

(28 citation statements)

References 5 publications

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“…The agents map is updated with this sub map and is broadcast to comrade agent in less time. Presented in [11], Formation based coordination agents methodically sweep the unknown space by advancing in close lines of formation. The formation is broken and restored as necessary whenever new obstacles are encountered.…”

Section: Coordination Strategiesmentioning

confidence: 99%

Optimization of route planning and exploration using multi agent system

Zafar

Baig

2010

Multimed Tools Appl

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This research presents an optimization technique for route planning and exploration in unknown environments. It employs the hybrid architecture that implements detection, avoidance and planning using autonomous agents with coordination capabilities. When these agents work for a common objective, they require a robust information interchange module for coordination. They cannot achieve the goal when working independently. The coordination module enhances their performance and efficiency. The multi agent systems can be employed for searching items in unknown environments. The searching of unexploded ordinance such as the land mines is an important application where multi agent systems can be best employed. The hybrid architecture incorporates learning real time A* algorithm for route planning and compares it with A* searching algorithm. Learning real time A* shows better results for multi agent environment and proved to be efficient and robust algorithm. A simulated ant agent system is presented for route planning and optimization and proved to be efficient and robust for large and complex environments.

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Section: Coordination Strategiesmentioning

confidence: 99%

Optimization of route planning and exploration using multi agent system

Zafar

Baig

2010

Multimed Tools Appl

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“…al. [4] presented an approach which can provably cover an entire region with minimal repeated coverage, but requires a high degree of coordination between the robots. The robots sweep the space together in a parallel line formation until they reach an obstacle boundary, at which point the team splits up at the obstacle and can opportunistically rejoin at some later point.…”

Section: Related Workmentioning

confidence: 99%

“…al. [4] have the disadvantage of keeping the robots in close proximity and require close coordination which can increase the time required for exploration if full, detailed coverage is not the primary objective. This also inhibits the reliability of the system in the event of full or partial communication problems or single robot failures.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Market-Driven Multi-Robot Exploration

Zlot

Stentz²,

Dias³

et al. 2002

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Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. AbstractFor many real-world applications, autonomous robots must execute complex tasks in unknown or partially known unstructured environments. This work presents a novel approach to efficient multi-robot mapping and exploration which exploits a market architecture in order to maximize information gain while minimizing incurred costs. This system is reliable and robust in that it can accommodate dynamic introduction and loss of team members in addition to communication interruptions and failures. Results showing the capabilities of our system on a team of exploring autonomous robots are also given.I

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“…A significant body of work has looked at multi-agent deployment as a motion planning problem. A survey of this work can be found in [6], and some significant contributions can be found in, for example, [5,18] and the citations therein. Other authors have proposed information theoretic algorithms for sensor networks which consider placing static sensors sequentially rather than driving robots with sensors using a distributed controller.…”

Section: Introductionmentioning

confidence: 99%

Unifying Geometric, Probabilistic, and Potential Field Approaches to Multi-robot Coverage Control

Schwager

Slotine

Rus

2011

Springer Tracts in Advanced Robotics

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This paper unifies and extends several different existing strategies for deploying groups of robots in an environment. A cost function is proposed that can be specialized to represent widely different multi-robot deployment tasks. It is shown that geometric and probabilistic deployment strategies that were previously seen as distinct are in fact related through this cost function, and differ only in the value of a single parameter. These strategies are also related to potential field based controllers through the same cost function, though the relationship is not as simple. Distributed controllers are then obtained from the gradient of the cost function and are proved to converge to a local minimum of the cost function. Three special cases are derived as examples: a Voronoi based coverage control task, a probabilistic minimum variance task, and a task using artificial potential fields. The performance of the three different controllers are compared in simulation. A result is also proved linking multi-robot deployment to nonconvex optimization problems, and multi-robot consensus (i.e. all robots moving to the same point) to convex optimization problems, which implies that multi-robot deployment is inherently more difficult than multi-robot consensus.

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Towards sensor based coverage with robot teams

Cited by 65 publications

References 5 publications

Optimization of route planning and exploration using multi agent system

Optimization of route planning and exploration using multi agent system

Market-Driven Multi-Robot Exploration

Unifying Geometric, Probabilistic, and Potential Field Approaches to Multi-robot Coverage Control

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