Adaptive relocation of environment-attached storage devices for effective knowledge-sharing among multiple robots

Umetani, Tomohiro; Mae, Y.; Inoue, Kousuke; Arai, Tatsuo

doi:10.1109/aim.2001.936449

Cited by 1 publication

(2 citation statements)

References 6 publications

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“…The result is then assigned some utility value which the robot uses to determine whether the action taken was "good" or "bad". "Good" results reinforce a given action or behavior, while "bad" results do just the opposite [1], [2]. A typical autonomous robot must manage a large array of sensory information to determine its environment.…”

Section: Introductionmentioning

confidence: 99%

“…Advanced data mining techniques [12] have been shown to be very successful in determining predictive behavioral models as well as finding hidden patterns and associations in data. Prior research [1], [7], demonstrates the effectiveness of multiple robots working together and sharing knowledge. By combing the intrinsic power of data mining with the cooperative capabilities of clusters of exploring robots a very powerful system for data gathering, analysis and real-time behavioral adaptations is possible.…”

Section: Introductionmentioning

confidence: 99%

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Adaptive behavioral control of collaborative robots in hazardous environments

McCarty

Manic

2009

2009 2nd Conference on Human System Interactions

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Terrain exploration carries with it significant hazards. Robots attempting to map a piece of unknown terrain must be able to make decisions and react appropriately to dynamic and potentially hostile conditions. However, because of constraints on size and cost, robots may have limited ability to store and process necessary information. In addition, knowledge discovered by others may be difficult to share. This paper proposes a system using a powerful master controller, operating from a safe environment, directing the movements of numerous robots exploring a piece of terrain. The master controller processes the information from the robots, updates the decision process and distributes these updates back to the robots. This process allows for a cooperative, effective search environment while also maintaining a small processing footprint. It also allows the robot to employ adaptive, subsumptive behavioral modification as new information is made available. A test simulation of a hazardous environment demonstrates that even robots with little intrinsic intelligence can learn complex behaviors in order to reach their goal.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%