Autonomous Learning in Vertical Environments

Cusack, Matthew; Fogarty, Terence C.

doi:10.22260/isarc1993/0026

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The Wall Climbing Robot1

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1995

1996

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“…2,3]. The robot is built on a framework of steel tube and measures approximately 400mm x 400mm x 150mm.…”

Section: The Wall Climbing Robotmentioning

confidence: 99%

Adaptive Gait Acquisition Using Multi-Agent Learning for Wall Climbing Robots

Bull¹,

Fogarty²,

Mikami³

et al. 1995

Proceedings of the 12th International Symposium on Automation and Robotics in Construction (ISARC)

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333In this paper we present work in progress to examine the use of two machine learning techniques to determine the gait of a wall climbing robot. We describe the use of the genetic algorithm and then that of the reinforcement learning technique Q-learning, within a multiple-agent framework, for this task. We assert that there is one agent responsible for the control of each leg of the robot, where each agent is represented by a rule-based controller. It is shown that it is possible to use these techniques to control the gait of the basic robot.

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“…2,3]. The robot is built on a framework of steel tube and measures approximately 400mm x 400mm x 150mm.…”

Section: The Wall Climbing Robotmentioning

confidence: 99%