Enhancing Robots Navigation in Internet of Things Indoor Systems

In this study, an effective local minima detection and definition algorithm is introduced for a mobile robot navigating through unknown static environments. Furthermore, five approaches are presented compared with the popular approach wall-following to pull out of enclosure namely; Random Virtual Target, Reflected Global Path Backtracking, Half Local Backtracking. The proposed mainly depend on changing target location temporarily avoid original target’s attraction force effect robot. Moreover, getting trapped in same location, virtual obstacle placed cover enclosure. To include most common shapes deadlock situations, were evaluated four different environments; V-shaped, double U-shaped, C-shaped, cluttered results reveal that robot, using any approaches, requires fewer steps reach destination, ranging from 59 73 m average, as opposed strategy, which average 732 m. On constant speed reflected takes 103 s, whereas identical 907 s complete tasks. Using fuzzy-speed duration greatly reduced 507 while may only need up 20% time. More detailed comparisons embedded subsequent sections.