🤖 Fuzzy Logic Control for Robot Speed Based on Obstacle Distance

This project implements a fuzzy logic controller using Python and scikit-fuzzy to determine a robot's movement speed based on the distance to an obstacle. The system simulates how a robot should slow down or stop as it approaches an object, mimicking human-like reasoning under uncertainty.

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📌 Assignment Objective

The goal is to understand and implement a fuzzy logic system to solve a real-world control problem using:

• Fuzzy sets and membership functions
• Fuzzy rules
• A fuzzy inference engine
• Defuzzification to produce actionable outputs

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🧠 Problem Statement

In uncertain environments, robots need to make decisions based on incomplete or imprecise information. This system models a robot that adjusts its speed based on how far it is from an obstacle:

• Close obstacle → Stop
• Medium distance → Slow down
• Far distance → Move fast

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⚙️ Fuzzy Logic System Design

🔹 Input Variable

• Distance (cm): 0 to 100
  • Fuzzy sets: Close, Medium, Far

🔹 Output Variable

• Speed (0 to 10 units):
  • Fuzzy sets: Stop, Slow, Fast

🔹 Fuzzy Rules

1. If distance is close, then speed is stop
2. If distance is medium, then speed is slow
3. If distance is far, then speed is fast
4. If distance is close AND medium, then speed is stop
5. If distance is medium AND far, then speed is slow

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🖥️ Implementation

• Language: Python 3.x
• Library: scikit-fuzzy
• Visualization: matplotlib

Setup Instructions

pip install scikit-fuzzy matplotlib numpy

Run the Code

1. Open the Jupyter notebook and execute the cells sequentially.
2. Fuzzy Sets and Membership Functions: Define input and output fuzzy sets and their corresponding membership functions.
3. Fuzzy Rules and Inference: Design fuzzy rules and implement the inference engine.
4. Testing: Test the system with sample input values (e.g., distances) and visualize the results.
5. Visualizations: View graphs of membership functions and output results.

The script:

• Defines fuzzy sets and membership functions
• Creates fuzzy rules
• Runs the fuzzy inference system
• Tests with various distances
• Outputs results and plots

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📊 Sample Output

Distance (cm) → Speed (0–10)
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10            →   0.75
30            →   2.42
50            →   4.84
70            →   7.25
90            →   9.12

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📈 Visualizations

• Membership functions for distance and speed
• Inferred output graphs for each input scenario

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✅ Conclusion

The fuzzy logic controller successfully models gradual and safe decision-making for robot navigation based on obstacle distance. The system demonstrates smooth transitions, robustness to uncertain input, and ease of rule modification.