map-A*: A Goal Oriented Optimum Path Finding Algorithm for Differential Drive Movement

Cognitive intelligent path searching procedures are evolving as significant perspective in both research and application level of WRN (Wheeled Robot Navigation). The superimposition of Machine Vision and Augmented Reality have provided clarity, precision and optimality to PTP (Point To Point) robot locomotion strategies. Performance of various existing graph theoretic as well as bio inspired path planning techniques have been computationally evaluated and improvised in certain research works. This concerned work primarily focuses on achievement of optimum Goal-oriented robot navigation with proposed map-A * algorithm. This structured algorithm is inspired from the characteristic features of RGB-D SLAM (Red, Green Blue Depth Simultaneous Localization And Mapping) for real-time trajectory scan of the explored route by the considered differential drive design. It has been followed by a simultaneous intelligent Grid-based path finding mechanism for desired goal completion. The interposition of path mapping has made the mobile agent comparative easier and quicker to get a snap of already traversed non-optimum route and decide its further movement. Input 3D data format has been captured by RGB-D sensor for its superiority in obstacle detection using its colour gradient feature. The algorithmic construction of the proposed procedure has been included with experimental analysis. Performance of the algorithm is bench-marked with conventionally used technique which cites its efficiency and reliability in the concerned working domain.