An ANFIS-Based Strategy for Autonomous Robot Collision-Free Navigation in Dynamic Environments
Abstract
:1. Introduction
- Development of a novel control strategy that employs the ANFIS model’s capabilities to simplify and facilitate the decision-making process, thereby enhancing the system’s computational efficiency.
- Application of four Adaptive Neuro-Fuzzy Inference Systems (ANFIS) that are expertly optimized to minimize the rule set of fuzzy controllers, while maintaining system efficiency.
- Enabling efficient navigation in environments cluttered with obstacles through a simplified rule structure.
- Integration of a path-planning algorithm that adds a layer of sophistication, enhancing the determination of optimal trajectories and significantly improving the efficiency and effectiveness of autonomous robot navigation.
2. Kinematics of a Two-Wheel Differential Drive Robot
3. Strategy for Path Planning
3.1. Path Planning Concepts
3.2. The Bump-Surface Concept
4. Basic Concepts on the Adaptive Neuro-Fuzzy Inference System (ANFIS)
5. Designing the ANFIS Controllers for the Automated Guided Vehicle
5.1. ANFIS Tracking Controllers
5.1.1. Data Set for ANFIS Tracking Controllers
- ‘small’ represents values from 0 to 0.13, indicating that the robot is very close to the target.
- ‘medium’ ranges from 0.05 to 0.3, suggesting a moderate distance from the target.
- ‘large’ encompasses values from 0.15 to 1, indicating that the robot is far from the target.
- ‘negative big’ represents values ranging from −1 to −0.3, indicating a significant deviation in the negative direction.
- ‘negative small’ spans values from −0.55 to −0.07, suggesting a moderate negative deviation.
- ‘zero’ encompasses values from −0.15 to 0.15, indicating no deviation from the desired heading.
- ‘positive small’ extends from 0.07 to 0.55, representing a moderate positive deviation.
- ‘positive big’ ranges from 0.3 to 1, indicating a significant deviation in the positive direction.
- ‘slow’: covering values from 0 to 0.4, representing slower motor speeds.
- ‘medium’: ranging from 0.27 to 0.6, indicating intermediate motor speeds.
- ‘fast’: extending from 0.47 to 1, denoting higher motor speeds.
5.1.2. Training ANFIS for Tracking Controllers (Left and Right)
5.2. ANFIS Avoidance Controllers
5.2.1. Data Set for ANFIS Avoidance Controllers
5.2.2. Training ANFIS for Avoidance Controllers (Left and Right)
6. Simulation Experiments
6.1. Robot System and Sensor Arrangement
6.2. Simulation Results
7. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Rule | Position Error | Heading Error | Right Motor Velocity | Left Motor Velocity |
---|---|---|---|---|
1 | large | negative big | medium | fast |
2 | large | negative small | slow | medium |
3 | large | zero | fast | fast |
4 | large | positive small | medium | slow |
5 | large | positive big | fast | medium |
6 | medium | negative big | medium | fast |
7 | medium | negative small | slow | medium |
8 | medium | zero | fast | fast |
9 | medium | positive small | medium | slow |
10 | medium | positive big | fast | medium |
11 | small | negative big | slow | fast |
12 | small | negative small | slow | fast |
13 | small | zero | slow | slow |
14 | small | positive small | fast | slow |
15 | small | positive big | fast | slow |
Position Error | Heading Error | Expected Right Motor Velocity | Expected Left Motor Velocity |
---|---|---|---|
0.9 | 0 | 0.7578 | 0.7578 |
0.8 | 0 | 0.7578 | 0.7578 |
0.7 | 0 | 0.7578 | 0.7578 |
0.6 | 0 | 0.7578 | 0.7578 |
0.5 | 0 | 0.7578 | 0.7578 |
0.4 | 0 | 0.7578 | 0.7578 |
0.3 | 0 | 0.7578 | 0.7578 |
0.2 | 0 | 0.7572 | 0.7572 |
0.1 | 0 | 0.6055 | 0.6055 |
0 | 0.9 | 0.7578 | 0.1674 |
0 | 0.8 | 0.7578 | 0.1674 |
0 | 0.7 | 0.7578 | 0.1674 |
0 | 0.6 | 0.7578 | 0.1674 |
0 | 0.5 | 0.7578 | 0.1674 |
0 | 0.4 | 0.7546 | 0.1713 |
Rule | Left Sensor | Right Sensor | Right Motor Velocity | Left Motor Velocity |
---|---|---|---|---|
1 | very close | very close | slow | slow |
2 | very close | close | slow | fast |
3 | very close | medium | slow | fast |
4 | very close | far | slow | fast |
5 | very close | very far | slow | fast |
6 | close | very close | fast | slow |
7 | close | close | slow | slow |
8 | close | medium | slow | fast |
9 | close | far | slow | fast |
10 | close | very far | slow | fast |
11 | medium | very close | fast | slow |
12 | medium | close | fast | slow |
13 | medium | medium | slow | slow |
14 | medium | far | slow | fast |
15 | medium | very far | slow | fast |
16 | far | very close | fast | slow |
17 | far | close | fast | slow |
18 | far | medium | fast | slow |
19 | far | far | fast | fast |
20 | far | very far | slow | fast |
21 | very far | very close | fast | slow |
22 | very far | close | fast | slow |
23 | very far | medium | fast | slow |
24 | very far | far | fast | slow |
25 | very far | very far | fast | fast |
Left Sensor | Right Sensor | Expected Right Motor Velocity | Expected Left Motor Velocity |
---|---|---|---|
0 | 1 | 0.6186 | 0.1276 |
0.1 | 0.9 | 0.6186 | 0.1276 |
0.2 | 0.8 | 0.6186 | 0.1276 |
0.3 | 0.7 | 0.6175 | 0.1292 |
0.4 | 0.6 | 0.5377 | 0.1364 |
0.5 | 0.5 | 0.1276 | 0.1276 |
0.6 | 0.4 | 0.1363 | 0.5377 |
0.7 | 0.3 | 0.1292 | 0.6175 |
0.8 | 0.2 | 0.1276 | 0.6186 |
0.9 | 0.1 | 0.1276 | 0.6186 |
1 | 0 | 0.1276 | 0.6186 |
Strategy | Time to Reach the End Point (s) | Number of Turns | Number of Rules for Tracking Controller | Number of Rules for Avoidance Controller |
---|---|---|---|---|
ANFIS | 71.4 | 13 | 9 | 16 |
Fuzzy system | 73.2 | 18 | 15 | 25 |
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Stavrinidis, S.; Zacharia, P. An ANFIS-Based Strategy for Autonomous Robot Collision-Free Navigation in Dynamic Environments. Robotics 2024, 13, 124. https://doi.org/10.3390/robotics13080124
Stavrinidis S, Zacharia P. An ANFIS-Based Strategy for Autonomous Robot Collision-Free Navigation in Dynamic Environments. Robotics. 2024; 13(8):124. https://doi.org/10.3390/robotics13080124
Chicago/Turabian StyleStavrinidis, Stavros, and Paraskevi Zacharia. 2024. "An ANFIS-Based Strategy for Autonomous Robot Collision-Free Navigation in Dynamic Environments" Robotics 13, no. 8: 124. https://doi.org/10.3390/robotics13080124