Fuzzy control for cylindrical grasp in anthropomorphic hand

Rodriguez, Eyder; Romero, Daniel; Sarmiento, Fredy Hernán Martínez

doi:10.12988/ces.2017.711161

Cited by 1 publication

(1 citation statement)

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“…We use a Nao robot from SoftBank Group at the top for direct interaction with people, and we use our TurtleBot 1 robot at the bottom to solve navigation and path planning problems. As applied research the research group has studied the problem of safe grasping of cylindrical objects with anthropomorphic hands [25], considering also the delicate interaction with humans. We have also developed many autonomous navigation schemes for locally observable dynamic environments [26][27][28][29], and analyzed the problem of dynamic bipedal walking [30].…”

Section: Introductionmentioning

confidence: 99%

Identifier of human emotions based on convolutional neural network for assistant robot

2020

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This paper proposes a solution for the problem of continuous prediction in real-time of the emotional state of a human user from the identification of characteristics in facial expressions. In robots whose main task is the care of people (children, sick or elderly people) is important to maintain a close relationship man-machine, anld a rapid response of the robot to the actions of the person under care. We propose to increase the level of intimacy of the robot, and its response to specific situations of the user, identifying in real time the emotion reflected by the person's face. This solution is integrated with algorithms of the research group related to the tracking of people for use on an assistant robot. The strategy used involves two stages of processing, the first involves the detection of faces using HOG and linear SVM, while the second identifies the emotion in the face using a CNN. The strategy was completely tested in the laboratory on our robotic platform, demonstrating high performance with low resource consumption. Through various controlled laboratory tests with different people, which forced a certain emotion on their faces, the scheme was able to identify the emotions with a success rate of 92%.

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