In the above project, A* and Djikstra Algorithm are used to compute the shortest path from start to goal point in a discretized search place full of obstacles.
The Following Libraries are used:
- Numpy
- copy
- matplotlib
- heapq
- math
- sys
Motion planning for both rigid and point robot is devised. In case of a rigid robot, both radius and clearance is considered while creating the free C space of the robot.
In case of A* search, the estimated total cost is calculated as the sum of past cost and optimistic cost to go. A* search is guaranteed to return a minimum cost path efficiently as it uses the optimistic cost go to guide the algorithm to the optimum path. Optimistic cost to go is taken as the manhattan distance.
In dijkstra Algorithm the estimated total cost is taken as zero. It is also guaranteed to find a minimum-cost path but it runs slowly than A* owing to the lack of an optimistic cost-ahead function to help guide the search
