Trajectory planning and obstacle negotiation rate are two fundamental problems in mobile robot locomotion. The proposed work addresses the problem of locomotion in mobile robots using Radial Basis Function Neural Network (RBFNN). The RBFNN controller initializes if the sensors detected obstacles inside the environment. The proposed RBFNN navigational control algorithm provides smooth and continuous steering angle commands to the robot using sensory reading. Distance received from the sensors for obstacle negotiation and the target-seeking rate is taken as the input parameter of the proposed control algorithm. The output of the control algorithm is the steering angle and optimum trajectory length toward the target. To show the results in terms of simulation, MATLAB and CoppeliaSim GUI platform has been used. To validate the simulation results, a real-time experiment has been proposed in the same environment. The error for path length and navigational time is less than 5% and has been recorded in terms of both environments (simulation and real-time experiment). The simulation and experimental results established the stability check for the proposed control algorithm.