Motivations and Goals |
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Become familiar with advanced methods of grid based path planning and dynamic replanning |
Be able to dynamically replan the motion based on the robot feedback from the environment |
Tasks (teacher) |
Implement and verify the functionality of the D* Lite algorithm (4 points) |
Lab resources |
Lab sripts: lab05 resource files |
The pseudocode and visualization of the algorithm is in Lecture 4. Grid and Graph-based path planning methods slides
The task is to implement a D* Lite algorithm with 4 neighborhood search within the provided d_star_lite_impelmentation. The visualization shall show the map, the g(s) and rhs(s) values for each s in the grid map for each step. Implement the following functions to cope with the algorithm:
def compute_shortest_path(grid_map, U, start, goal) def update_vertex(grid_map, U, u, start, goal)and extend the main method to implement map update after obstacle detection
if not grid_map.passable(start_prime): #the path is blocked, it is necessary to update weights """ put your code for weights update here """ else: #move towards the target start = start_prime
Your solution submit to the upload system under the Lab05 assignment for evaluation.