Navigation Systems Using A*

Abstract

Shortest path searching is very important in some special cases such as medical emergencies, fire brigade, etc. An optimal path may be defined on a number of factors such as least distance/time or traffic density in stochastic road networks. Hence, there is no proper definition of an optimal path within the underlying constraints. One approach taken as an answer to this question is to find a path with the minimum expected travel time. For this, the approach taken is to construct a map of a city/town. This map is created using graphs where the nodes and edges represent the landmarks/locations and roads respectively. Traversal of the graph denotes traversal among the city/town. There are multiple graph traversal algorithms such as Best First Search, Bellman-Ford, and Dijkstra; but an optimal algorithm that finds the shortest path with the least time complexity should be chosen and implemented. Hence the A* algorithm is ideal. The benefit of using the A* algorithm is that it provides the optimum path while adhering to the underlying constraints. While A* algorithm is an optimum path-finding algorithm, it works ideally when the graph created has accurate measurements. Scaling is also not an issue as space complexity increases with the size of the graph. A small part of The city of Bengaluru is taken as the map consisting of 83 locations in which 24 locations consist of the possible destinations and an ambulance is considered as the vehicle of traversal to eliminate underlying constraints such as traffic density and variable speed.