This thesis presented a stable, energy-efficient, and omnidirectional gait generation on the humanoid robot. The ZMP preview controller with Bezier function was used to generate a walking gait. Moreover, the CMA-ES algorithm was proposed for optimizing gait parameters in the simulation model. The yielded gait engine was verified in the real robot to measure the stability and consumed energy performance.
Based on an experimental result, the proposed gait generation achieved a stable and energy-efficient gait. The reduction in energy during training about 29.813 % in simulation. On the other hand, stability increases by 20 % in simulation. The optimized gait successfully reduced energy consumption by 19.905 % compared to non-optimized gait. Moreover, the optimized gait yielded a stable performance while it applied to variable-speed and omnidirectional walk.
Even though the gait engine is stable, but it can not guarantee to reject external disturbance cause the gait generation is open loop. In future work, a model-free reinforcement learning will be studied to improve the dynamic balance in the robot.
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Autobiography
Eko Rudiawan Jamzuri finished diploma at Politeknik Negeri Batam in 2011 and obtained a Bachelor of Applied Science from Bandung Institute of Technology in 2013.
Currently, he is a master student at the Department of Electrical Engineering, National Taiwan Normal University, and member of Educational Robotics Centre (ERC) National Taiwan Normal University.
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Academic Achievement
1. International Intelligent RoboSports Competition 2020 (Taiwan) - 1st Place HuroCup Kid-size Humanoid Sprint & Marathon.
2. IEEE/RSJ IROS 2019 (Macau) – 3rd Place Humanoid Robot Application Challenge.
3. Iran FIRA RoboWorldCup Open 2019 (Iran) - 1st Place all-round HuroCup Kid-size Humanoid.