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Vision-based predictive assist control on master-slave systems

Akio Namiki, Yosuke Matsumoto, Tomohiro Maruyama, Yang Liu

Year
2017
Citations
13

Abstract

In this paper, we propose a method for improving the maneuverability of master-slave systems. We aim at reproducing human skillfulness and dynamic performance in master-slave robots by using assist control for human operators. In this paper, we tackle a reaching task performed by a master-slave robot and propose an operation assist algorithm based on visual feedback control. The algorithm consists of visual object recognition for a slave robot, prediction of the operator's motion by a particle filter, estimation of the operator's intention, and operation assistance of the reaching motion. We verified the validity of the proposed system in experiments.

Keywords

Master/slaveRobotTask (project management)Computer scienceParticle filterOperator (biology)Object (grammar)Motion (physics)Artificial intelligenceControl (management)

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