Abstract
In this paper, a strategy for adapting the NAO robot to different floors with different slipperiness degrees is presented while following a desired human-like Zero Moment Point trajectory. The robot’s gait is generated based on the HRSP software package and it aims to be as human-like as possible. The gait parameters such as the step length and walking speed are optimized in order to generate gaits with adequate RCoF for the floor’s ACoF, which minimizes the slipping probability, and as such avoiding undesired falls. This choice of gait parameters is based on the analysis of the ground reaction forces and human behavior. Also, gait adaptation is further improved in order to follow a desired Zero Moment Point trajectory, through the use of a controller that offsets the hip and ankle joint angles. The novelty of this work lies on the fact that machine learning techniques are used to adapt the gait parameters and joint corrections to make the robot more resistant to both slipping and external disturbances.
Highlights
Humanoid robot development has been a field of study that has seen a lot of research in the last few years and is becoming more and more relevant as human-machine interaction becomes omnipresent, and robots fill increasingly important roles in society, such as rescue, industrial automation, therapy, and rehabilitation
The disadvantage bipedal/humanoid robots have compared to wheeled ones is that they are more complex and difficult to control due to their high center of gravity and multiple degrees of freedom, and despite various efforts to come up with efficient and robust locomotion strategies, there are still no definite solutions for humanoid gait and balance control
The proposed approach can be divided in four parts: 1) Use Humanoid Robot Simulation Platform (HRSP), a previously developed humanoid robot simulator at Mihajlo Pupin Institute that uses machine learning techniques, to generate human-like gait trajectories based on human motion capture
Summary
Humanoid robot development has been a field of study that has seen a lot of research in the last few years and is becoming more and more relevant as human-machine interaction becomes omnipresent, and robots fill increasingly important roles in society, such as rescue, industrial automation, therapy, and rehabilitation. The proposed approach can be divided in four parts: 1) Use HRSP, a previously developed humanoid robot simulator at Mihajlo Pupin Institute that uses machine learning techniques, to generate human-like gait trajectories based on human motion capture. 4) Develop a controller that offsets the hip and ankle joint angles in order to follow a ZMP reference trajectory, correcting sudden balance disturbances. Both traditional and machine learning methods will be developed. The simulation output is explained in more detail as well as a comparison between the factory default gait and the human-like gait that is generated by the HRSP system It is organized in the following manner: Chapter Introduction provides a brief introduction of the research project and justification for it. Chapter Conclusion and Future Steps discusses the preliminary results and plans the steps to take for the implementation of the final system
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