DocumentCode :
185038
Title :
Implementation of an adaptive, model free, learning controller on the Atlas robot
Author :
Atmeh, Ghassan M. ; Ranatunga, Isura ; Popa, Dan O. ; Subbarao, Kamesh ; Lewis, Frank ; Rowe, Philip
Author_Institution :
MAE Dept., Univ. of Texas at Arlington, Arlington, TX, USA
fYear :
2014
fDate :
4-6 June 2014
Firstpage :
2887
Lastpage :
2892
Abstract :
Recent events in natural and man-made disasters have highlighted the limitation in man´s ability to confine and mitigate damage in such scenarios. Therefore, there is an urgent need for robotic technology that can function in all environments and serve as a substitute to humans in disaster scenarios. This paper presents research efforts to advance walking technology of humanoid robots with application to the Boston Dynamics Atlas robot. The Atlas was designed as part of the DARPA Robotics Challenge (DRC). The paper contribution is in a model free, walking trajectory tracking controller that is tested using GAZEBO robotics simulator. Artificial neural networks are used to learn the robot´s nonlinear dynamics on the fly using a neuroadaptive control algorithm. The learned nonlinear dynamics are utilized along with a filtered error signal to generate input torques to control the system. Results show that the ability to approximate the robot nonlinear dynamics allows for full-body control without the need of modeling such a complex system. This ability is what makes the control scheme utilized appealing for complex, real-life, robotic applications that occur in a non-laboratory setting.
Keywords :
adaptive control; humanoid robots; learning systems; legged locomotion; neurocontrollers; nonlinear control systems; robot dynamics; trajectory control; Boston Dynamics Atlas robot; DARPA Robotics Challenge; DRC; GAZEBO robotics simulator; adaptive controller; advance walking technology; artificial neural networks; filtered error signal; full-body control; humanoid robots; input torque generation; learned nonlinear dynamics; learning controller; model free controller; neuroadaptive control algorithm; robot nonlinear dynamics; walking trajectory tracking controller; Artificial neural networks; Foot; Legged locomotion; Nonlinear dynamical systems; Robot sensing systems; Trajectory; Adaptive systems; Mechanical systems/robotics; Neural networks;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
American Control Conference (ACC), 2014
Conference_Location :
Portland, OR
ISSN :
0743-1619
Print_ISBN :
978-1-4799-3272-6
Type :
conf
DOI :
10.1109/ACC.2014.6859431
Filename :
6859431
Link To Document :
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