Chapter 5. Experimental Result and Discussion
5.4 Comparison Performance Before and After Optimization
We evaluate the optimization result by comparing the performance of optimized and non-optimized gait in the robot. We used hand-tuned parameters from trial and error experiments for non-optimized gait and got the best parameter in Table 5-2 for optimized gait. In this experiment, we used a statistical approach with four different types of omnidirectional walking, such as forward, backward, sideways, and turning.
Each walking type is applied to the robot with 50 trials on the thin carpet with the starting and finish line. The robot is controlled manually by joystick to start walking from the starting line to the finish line. The distance of the starting and finish line for walking forward and backward was 2 meters, and the distance for walking sideways was 1 meter.
In the turning motion, we run the robot to walk on a circular path with a diameter of 0.74 m. During the experiment, the robot is given a constant motion command. The command for walking forward and backward cmd =x
0.03, 0.03−
. For the walking sideways, we run with both walking to side left and walking in the right direction by giving the command cmd =y
0.01, 0.01−
. For walking on a circular path, we gave the motion command cmd =x 0.03 and cmd =
5, 5−
degrees to walk on clockwise (CW) and counter-clockwise (CCW) direction. We recorded the voltage, current, and IMU sensor data during walking to compare the gait performance as presented in Table 5-4.37
Table 5-4. Comparison before and after optimization.
Walking Type
Before Optimization After Optimization Saving energy
Table 5-4 delivered the performance comparison before and after optimization. The success rate represented the percentage of the successful trial, where the robot reached the finish line without falling. The energy represented by the total consumed electric power of the leg actuator per second.
As described in Table 5-4, before optimization, the average success rate was about 61.5 % that indicated unstable gait and made the robot high chance to fall. The non-optimized gait yielded a high success rate in walking forward only that indicated the parameters were not generalized for other types of walking. From the speed and energy perspective, before optimization, the average energy was about 12.985 W/s, and the average speed about 0.075 m/s. After optimization, the gait performance improved significantly. The success rate maintained a constant of 100 % for a different type of walking that indicated the gait parameters yielded a stable gait that made the robot never falling.
On the other hand, the average consumed energy of 10.4 W/s, which was lower compared to non-optimized gait. However, the average speed reached 0.037 m/s, which was slower compared to non-optimized gait. By comparing the consumed energy before and after optimization, the optimized gait was able to save energy about 19.905 %. As the conclusion of the result visualized in Table 5-4, after optimization, the walking gait was slower, more stable, and less consumed energy compared to non-optimized gait.
38 5.5 Straight Walk with Variable Step Length
We extend the experiment in Section 5.4 by varying step lengths when the robot walked forward and backward to study the effect of changing step length to gait performance. We used step length from range 1 cm to 5 cm with an interval of 1 cm.
The result of the walking forward experiment shown in Table 5-5, and walking backward experiment summarized in Table 5-6.
Table 5-5. Comparison walking forward with variable step length.
Step Length
Based on data in Table 5-5, before optimization, the changing of step length affected the reduction in success rate. The higher step length applied to the robot produced a high chance for the robot to fall. However, after we optimized the gait, the changing of step length did not affect the stability. The robot can walk with minimum to maximum step length with a constant success rate. With the optimized gait, we can reach a maximum speed of 0.072 m/s with the highest step length of 7 cm. On the other hand, the consumed energy maintained stable at around 10.021 – 11.004 W/s, even though the step length was varying.
Table 5-6. Comparison walking backward with variable step length.
Step Length
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The backward walking result, illustrated in Table 5-6, has a similar result with walking forward. While the variable step length applied to non-optimized gait, it affected the reduction in success rate. By using an optimized gait, the robot able to walk backward from step length 1 cm – 7 cm without falling. However, compared to forward walking, the consumed energy was quite higher, about 10.426 W/s – 11.301 W/s. In contrast, the maximum speed was equal to forward walking about 0.072 m/s.
Table 5-5 and Table 5-6 conclude that the yielded walking parameter from optimization successfully applied to walk with variable step length, which means that the robot can walk stably with low energy at a different speed.
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Chapter 6. Conclusion and Future Work
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.