Torque Curve Optimization of Ankle Push-Off in Walking Bipedal Robots Using Genetic Algorithm
Abstract
:1. Introduction
2. Methods
2.1. Simulation Model
2.2. The Joint Trajectories
2.2.1. Ankle Positions of Swing Leg
2.2.2. Target Positions of Hip and Knee Joints
2.2.3. Ankle Torque during Push-Off Phase
3. Controller
3.1. Controller Framework of the Ankle Joint
3.2. Overall Framework of the Bipedal Robot
3.3. Optimization Method
4. Results
4.1. Optimal Results
4.2. Joints Kinematics at Different Speeds
4.3. Torque and Power of the Ankle Joint
4.4. Walking Gaits
4.5. Mechanical Work of Joins
4.6. Energy Efficiency
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Parameters | Mass/kg | Length/m | Parameters | Values |
---|---|---|---|---|
Torso | 54.2 | 0.66 | Coefficient of contact stiffness (N/m) | 75,000 |
Thigh | 6.6 | 0.45 | Coefficient of contact damping (N/(m/s)) | 4600 |
Shank | 2.7 | 0.37 | Coefficient of static friction | 0.8 |
Foot | 0.8 | 0.24 | Coefficient of kinetic friction | 0.6 |
Foot—toe | - | 0.192 | Contact sphere radius(m) | 0.01 |
Foot—height | - | 0.09 |
Parameters | Lower Limit | Upper Limit |
---|---|---|
t1 | 2 | 8 |
Ratio1* | 2 | 8 |
Ratio2* | 2 | 8 |
b/° | 10 | 20 |
a/° | 40 | 60 |
Peak Time t1 | Ratio1* | Ratio2* | b/° | a/° | Walking Speed (m/s) |
---|---|---|---|---|---|
7 | 7 | 5 | 13 | 46 | 0.5 |
7 | 5 | 2 | 12 | 46 | 0.8 |
5 | 2 | 7 | 15 | 48 | 1.0 |
8 | 2 | 8 | 15 | 53 | 1.3 |
Mechanical Work (J) | Walking Speed (m/s) | Positive (Stride) | Negative (Stride) | Net (Stride) | Early Stance Phase | Ankle Push-Off Phase | Swing Phase |
---|---|---|---|---|---|---|---|
Hip | 0.5 | 99.30 | −43.97 | 55.33 | 20.11 | 0.05 | 35.27 |
0.8 | 124.77 | −62.14 | 62.63 | 20.38 | −0.03 | 42.28 | |
1.0 | 155.44 | −71.69 | 83.75 | 14.58 | −0.01 | 69.19 | |
1.3 | 166.16 | −76.42 | 89.74 | 19.06 | 0.05 | 70.64 | |
Knee | 0.5 | 127.54 | −43.75 | 83.79 | −1.03 | 0.00 | 84.82 |
0.8 | 134.67 | −37.30 | 97.37 | −1.05 | 0.00 | 98.42 | |
1.0 | 168.35 | −54.43 | 113.92 | −0.68 | −0.39 | 114.98 | |
1.3 | 199.03 | −44.54 | 154.49 | −0.67 | −0.30 | 155.45 | |
Ankle | 0.5 | 17.94 | −12.81 | 5.13 | −9.28 | 1.79 | 12.24 |
0.8 | 14.37 | −9.42 | 4.95 | −7.03 | 2.20 | 9.79 | |
1.0 | 24.00 | −6.42 | 17.59 | −4.77 | 9.50 | 12.86 | |
1.3 | 21.44 | −13.09 | 8.36 | −11.46 | 8.63 | 11.19 |
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Ji, Q.; Qian, Z.; Ren, L.; Ren, L. Torque Curve Optimization of Ankle Push-Off in Walking Bipedal Robots Using Genetic Algorithm. Sensors 2021, 21, 3435. https://doi.org/10.3390/s21103435
Ji Q, Qian Z, Ren L, Ren L. Torque Curve Optimization of Ankle Push-Off in Walking Bipedal Robots Using Genetic Algorithm. Sensors. 2021; 21(10):3435. https://doi.org/10.3390/s21103435
Chicago/Turabian StyleJi, Qiaoli, Zhihui Qian, Lei Ren, and Luquan Ren. 2021. "Torque Curve Optimization of Ankle Push-Off in Walking Bipedal Robots Using Genetic Algorithm" Sensors 21, no. 10: 3435. https://doi.org/10.3390/s21103435
APA StyleJi, Q., Qian, Z., Ren, L., & Ren, L. (2021). Torque Curve Optimization of Ankle Push-Off in Walking Bipedal Robots Using Genetic Algorithm. Sensors, 21(10), 3435. https://doi.org/10.3390/s21103435