PathTrackFlow: A Predictive Framework for Advanced Path Planning in Autonomous Robotics

Navigation of mobile robots is one of the most crucial areas for the field of robotics. Transportation of a mobile robot from its origin to a specified destination without mishaps and other possibly harmful situations is known as navigation. In this paper, the path planning of an autonomous mobile robot in the dynamic environment is demonstrated based on the improved flow based prediction model (FPM) and the boosted occurrence bat algorithm (BOBA) optimization method. Estimation of the presence of environmental obstacles is the duty of the FPM. When there are no neighboring impediments, the BOBA defines the pathways through which the mobile robot can travel. If the mobile robot detects a barrier nearby, normal operation is interrupted and the obstacle avoidance (OA) mechanism is used. Simulation results showed that the BOBA performs better than existing prediction models and optimizers, due to the ability of generating a smooth, safer and shorter collision free trajectory.