Reinforcement Optimization Algorithm for Mobile Robot Sensor Networks Drive Motion Improvement

This paper proposed four optimization algorithms for mobile robot sensor networks that improve the kinematics drive motion in a reference map environment. The standard procedure followed in mobile robot sensor measurements considers a problem statement for relating the sensor measurements with a reference map. The initial path shows that the existing methods lack consideration of more sensor points without considering the boundary constraints and obstacles. The probabilistic path map can be rearranged according to the current location to improve the better drive motion, as well as to obey the fundamental kinematics equations. he obstacle crossing led to the development of new algorithms. Implementation of schemes is achieved in different map environments, and the accuracy of results outperforms conventional methods by 84.21 % to 96.94 %.