1. AGGREY SHITSUKANE SHISIALI. TECHNICAL UNIVERSITY OF MOMBASA
fuzzy logic MODEL FOR Obstacles avoidance robotic CRANE in static unknown environment.
PRESENTER
AGGREY SHITSUKANE SHISIALI.
TECHNICAL UNIVERSITY OF MOMBASA

2. INTRODUCTION .
Autonomous mobile robotics has in recent times gained interests from many researchers.
Motivated by the current gap between the available technology and the new application demands
The biggest challenge in mobile robot is path finding and motion control. .

3. INTRODUCTION
Normally robots are fitted with transducers that have a large amount of uncertainty.
Fuzzy logic has been demonstrated to be a convenient tool for handling uncertainty
We present mamdani fuzzy controller model for effective steering.simulated using MATLAB and V-REP software.

4. Theoretical concept.

5. Theoretical concept.

6. Determination of input and output variables.
Inputs dl, df, dr are taken as the premise variables
three fuzzy linguistic sets labeled as "NEAR (N)", "MEDIUM (M)", and "FAR (F)“ discussion region of dl, df, dr belong to [0, 1000mm]
The output variables vl, vr
represented by three fuzzy linguistic sets labeled by "SMALL (S)", "MEDIUM (M)", and "BIG (B)" discussion regions for vl, vr are chosen during (0 to 100 cm/s)

7. Determination of input and output variables.
Membership functions for the fuzzy premise variables dl, df, dr
Membership functions for fuzzy output variables vl, vr.

8. Design.

9. ENVIRONMENT Development V-REP simulator environment

10. discussion

11. Discussion

12. Gaussian MF was poor in response in all instances.
Conclusion
Fusion of sensor improves performance and eliminates local minima
Triangular and trapezoidal MF are good in response in terms of time taken to reach the target.
Gaussian MF was poor in response in all instances.

13. conclusion
Simulation video link