Biomimetic Adaptive Pure Pursuit Control for Robot Path Tracking Inspired by Natural Motion Constraints
Abstract
:1. Introduction
2. Kinematic Model
2.1. Kinematic Analysis
- (1)
- Four wheels of the FWDDR are symmetrically distributed on the same plane;
- (2)
- None of the FWDDR’s wheels idle;
- (3)
- The FWDDR does not exhibit longitudinal skidding during steering;
- (4)
- The FWDDR possesses an ample turning radius;
- (5)
- The FWDDR’s center of mass is situated on the robot’s x-axis.
2.2. Equation of Motion
3. Path-Tracking Controller Design
3.1. Traditional PP Control
3.2. Quadratic Polynomial-Based A-PP Control
3.3. Road Curvature Calculation Method
4. Path Planning and Tracking
5. Experiment
5.1. Experimental Setup
- (1)
- Control commands are sent through the PC Matlab/Simulink control interface via the RS485 bus;
- (2)
- On the PC side, E32-DTU converts commands transmitted via the RS485 bus into LoRa RF signals;
- (3)
- On the FWDDR side, E32-DTU converts LoRa signals into RS485 signals;
- (4)
- The motion controller interprets RS485 signals based on the four-wheel differential model and generates corresponding servo drive commands. These commands are then transmitted to the four servo drives through the CAN bus;
- (5)
- The motion controller transmits motor status, battery voltage, and other parameters back to the upper computer through a reverse path.
5.2. Experiment Process
5.3. Analysis of Experimental Results
6. Conclusions
- (1)
- The quadratic polynomial is enhanced in both lateral and longitudinal dimensions to facilitate adaptive dynamic adjustment of the forward-looking distance. This enhancement reduces the lateral deviation of the FWDDR during path tracking and enhances both tracking accuracy and operational stability.
- (2)
- The A-PP algorithm is simulated and verified by Matlab/Simulink, and the results indicate that the A-PP algorithm achieves a mean lateral error of 0.00694 m, a variance of 0.004663 m, and a maximum lateral error of 0.012837 m during path tracking, which represent a significant enhancement in stability and accuracy when compared to the traditional PP and MPC algorithms.
- (3)
- Experimental tests further validated the A-PP algorithm. The results showed a mean lateral error of 0.01070 m, a variance of 0.006663 m, and a maximum lateral error of 0.019443 m. In comparison with the PP algorithm, the A-PP algorithm achieved faster convergence of deviations, enhanced lateral errors in turning sections, and heightened driving smoothness, all while maintaining low computational time.
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Conflicts of Interest
References
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Method | Average Value of Lateral Error (m) | Variance (m) | Maximum Lateral Error (m) |
---|---|---|---|
= 0.1 m | 0.01007 | 0.010075 | 0.094842 |
= 0.2 m | 0.01688 | 0.010333 | 0.029417 |
= 0.3 m | 0.02462 | 0.015404 | 0.046663 |
MPC | 0.01467 | 0.010247 | 0.021854 |
A-PP | 0.00694 | 0.004663 | 0.012837 |
Parameters | Value | Units |
---|---|---|
Length | 770 | mm |
Width | 658 | mm |
Wheelbase | 470 | mm |
Wheel tread | 573 | mm |
Minimum turning | 1015 | mm |
Tire size (diameter) | 260 | mm |
Maximum motor speed | 3600 | rpm |
Maximum driving torque | 47.5 | N·m |
Maximum load | ≥50 | kg |
Method | Average Value of Lateral Error (m) | Variance (m) | Maximum Lateral Error (m) |
---|---|---|---|
Pure pursuit | 0.02372 | 0.016588 | 0.036705 |
MPC | 0.02069 | 0.014416 | 0.031628 |
A-PP | 0.01070 | 0.006663 | 0.019443 |
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Zhao, S.; Zhao, G.; He, Y.; Diao, Z.; He, Z.; Cui, Y.; Jiang, L.; Shen, Y.; Cheng, C. Biomimetic Adaptive Pure Pursuit Control for Robot Path Tracking Inspired by Natural Motion Constraints. Biomimetics 2024, 9, 41. https://doi.org/10.3390/biomimetics9010041
Zhao S, Zhao G, He Y, Diao Z, He Z, Cui Y, Jiang L, Shen Y, Cheng C. Biomimetic Adaptive Pure Pursuit Control for Robot Path Tracking Inspired by Natural Motion Constraints. Biomimetics. 2024; 9(1):41. https://doi.org/10.3390/biomimetics9010041
Chicago/Turabian StyleZhao, Suna, Guangxin Zhao, Yan He, Zhihua Diao, Zhendong He, Yingxue Cui, Liying Jiang, Yongpeng Shen, and Chao Cheng. 2024. "Biomimetic Adaptive Pure Pursuit Control for Robot Path Tracking Inspired by Natural Motion Constraints" Biomimetics 9, no. 1: 41. https://doi.org/10.3390/biomimetics9010041