Path planning simulation of a differential wheel robot based on the RRT Algorithm

This research aims to overcome the constraints of current image-processing techniques and path-planning algorithms that hinder traditional navigation systems from reliably recognizing obstacles and designing effective paths. The study examines the progress of sophisticated path planning and tracking algorithms for differential wheel robots that operate in interior situations. The study utilizes MATLAB-based image processing techniques to turn real-world photos into binary maps, enabling efficient obstacle identification. The rapid-exploring random tree (RRT) technique is used to plan paths by exploring random nodes and avoiding barriers to find the most efficient pathways. Moreover, the implementation of the pursuit-prediction (PP) tracking algorithm enhances the robot's capacity to accurately follow the intended path by dynamically modifying its trajectory using real-time data. The results indicate that the suggested methods greatly enhance the precision of path planning and navigation, allowing the robot to effectively navigate through intricate surroundings containing diverse impediments, such as tables and chairs. This work provides useful insights into autonomous navigation systems, specifically for differential wheel robots, expanding their potential uses in indoor environments such as restaurants and improving their overall operational efficiency and dependability.