Neural Network Control of Humanoid Robot

휴머노이드 로봇의 뉴럴네트워크 제어

  • 김동원 (인하공업전문대학 디지털 전자과) ;
  • 김낙현 (현대모비스 기술연구소 플랫폼설계 2팀) ;
  • 박귀태 (고려대학교 공과대학 전기공학과)
  • Received : 2010.06.10
  • Accepted : 2010.07.20
  • Published : 2010.10.01


This paper handles ZMP based control that is inspired by neural networks for humanoid robot walking on varying sloped surfaces. Humanoid robots are currently one of the most exciting research topics in the field of robotics, and maintaining stability while they are standing, walking or moving is a key concern. To ensure a steady and smooth walking gait of such robots, a feedforward type of neural network architecture, trained by the back propagation algorithm is employed. The inputs and outputs of the neural network architecture are the ZMPx and ZMPy errors of the robot, and the x, y positions of the robot, respectively. The neural network developed allows the controller to generate the desired balance of the robot positions, resulting in a steady gait for the robot as it moves around on a flat floor, and when it is descending slope. In this paper, experiments of humanoid robot walking are carried out, in which the actual position data from a prototype robot are measured in real time situations, and fed into a neural network inspired controller designed for stable bipedal walking.


Supported by : 인하공업전문대학


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