Motor velocity based multi-objective genetic algorithm controlled navigation method for two-wheeled pioneer P3-DX robot in V-REP scenario

This paper proposes the right and left motor velocity based multi-objective genetic algorithm controlled navigation method for Two-Wheeled Pioneer P3-DX Robot (TWPR) in Virtual Robot Experimentation Platform (V-REP) software scenarios. The first objective function is made by taking front, left, and right ultrasonic sensors data and right motor velocity. Similarly, the second objective function is designed using front, left, and right ultrasonic sensors data and left motor velocity. Next, the sensor data are considered independent variables or inputs. The velocities of the motors are chosen as dependent variables or outputs for making multi-objective fitness functions for genetic algorithm (GA). This multi-objective GA makes a sensor-actuator control architecture and helps the TWPR to avoid the obstacles in the simulated scenarios. Further, the successful simulation test results show that the multi-objective GA provided a collision-free smooth, and shortest path for TWPR compared to the previously developed benchmark single objective GA.