Normal Probability and Heuristics based Path Planning and Navigation System for Mapped Roads

In a hybrid road network with multiple paths to same location having prior geographical knowledge, successful navigation for mobile robots is one of the main challenges. Path planning is one of the most important issues in the navigation process which enables the selection and identification of a suitable path for the robot to traverse in the workspace area. Path-planning for mapped roads can be considered as the process of navigating a mobile robot around a configured road map, which provides optimized path by considering roughness of roads. In this paper, we propose a novel navigation algorithm for outdoor environments, which permits robots to travel from one static node to another along a planned path. It utilizes Normal probability weight distribution (NPWD) to assign weights between two nodes dynamically. Heuristics based shortest path (HSP) algorithm is employed to solve complex optimization problems concerned with real-world scenarios. The experiments performed on categorized road databases show significant improvement in timings and complexity of system. Our results justify the effectiveness for the implementation of driver-assist system.