HW 01 - Grid-based Path Planning

The main task of the homework is to implement a grid based path planning approach that will guide the simulated robot through the maze. The homework focus on integration of individual essential phases of map preprocessing, path planning and plan execution.

Deadline	6. November 2017, 23:59 PDT Deadline moved
Label in BRUTE	HW01
Files to submit	archive with `.py` files except the `eval.py` and `eval_functions.py` scripts, and the `hexapod_vrep` directory
Mandatory tasks	10 Points
Resources	lab04_resource_files maze1.ttt

Mandatory tasks - Modify the `planner.py` script to implement the following phases of grid-based path planning:
2 points	Map preprocessing - address the problem of robot embodiment (the robot can safely navigate at the distance of 0.3m from obstacles). Consider all the provided maps to have cell size of 0.1m
5 points	Path planning and trajectory smoothing - address the problem of effective path planning by selecting suitable grid-based path planning approach (select one):
	DFS	2 points
	BFS	2 points
	DT	3 points
	Dijkstra	3 points
	A*	3 points
	Theta*	4 points
	JPS	5 points
	D* lite	5 points
3 points	Plan execution - address the problem of plan execution with a simulated robot in V-REP simulator. Demonstrate the functionality of your solution on the `maze1.ttt` scenario.

Evaluation - how will be the homeworks evaluated

The solution will be evaluated with a modified version of the eval.py function. The minimum evaluation function example is:

    import sys
    import map as mp
    import planner as pl
    import numpy as np
 
    #instantiation of map and planner
    map = mp.Map()
    planner = pl.Planner()
 
    ################################################
    # Testing
    ################################################
    #maze name, (size_x, size_y) in vrep, (offset_x, offset_y) for coordinates transformation, voxel_size, start_position in the map, goal_position in the map, execution flag
    test = ("./mazes/maze1.png",(10,8.6),(5,4.3),0.1,(92,75),(5,20),True)
    map.from_file(test[0],test[1],test[2],test[3])
    start = test[4]
    goal = test[5]
    execute = test[6]
 
    ######################################
    #path planning
    ######################################
    path = planner.plan(map, start, goal)
 
    ######################################
    #path execution
    ######################################
    ret = planner.execute(map, start, goal)

The planner method plan(map, start, goal) shall return a feasible path to given grid map with a voxel size of 0.1 m. The planner method execute(map, start, goal) shall guide the simulated robot through the environment.