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

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. .

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.

Theoretical concept.

Theoretical concept.

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)

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

Design.

ENVIRONMENT Development V-REP simulator environment

discussion

Discussion

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.

conclusion Simulation video link https://www.youtube.com/watch?v=IC_9drR68DQ.