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T1e-expl - Mobile robot exploration: Frontier detection

The main task is to implement a frontier detector, which can be used to detect waypoints in autonomous spatial exploration.

Deadline November 2nd 2019 23:59 PST
Points 3
Label in BRUTE t1e-expl
Files to submit archive with ExplorationMap.py
Resources T1e-expl package


In ExplorationMap.py implement the function is_frontier to distinguish between frontier cells and non-frontiers cells.

    def is_frontier(self, x, y):
        Method to determine whether a cell is a frontier.
        x : int
            discrete cell coordinate
        y : int
            discrete cell coordinate
            True if the cell is a frontier, False otherwise 
        # TODO t1e-expl
        if (x + y) % 2 == 0:
            return False
        return True


Frontiers are borders between free and unknown space, which may used to detect navigational goals to steer the robot towards the unknown environment, and thus to support mobile robot exploration. In grid maps, the cells that correspond to the frontiers are defined as follows

A frontier cell is a free cell which borders
  1. at least one other free cell
  2. at least one unknown cell
  3. no occupied cells.

Note, cells that border occupied cells are not considered frontiers in order to reduce false positives induced by the localization and mapping uncertainty.

The 8-neighborhood should be considered when implementing this task.


The code can be evaluated using t1e-expl-eval.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
import sys
from scipy import misc
import glob
import numpy as np
import os.path
from matplotlib import pyplot as plt
INSTANCES = ['maps/map0','maps/map1','maps/map2']
import GridMap as gmap
import ExplorationMap as emap
class Evaluator:
	# loat trajectory as array of tuples
	def load_map_image(self, filename):
		ret = []
		with open(filename, "r+") as f:
			data = f.readlines() # read the text file
			for line in data:
				numArr = line.strip().split(" ") # numbers on a one line
				if len(numArr) >1:
		return ret
	# compute point count difference
	def point_count_diff(self, gt, est):
		return abs( len(gt) -len(est) )
	def dist_to_closest(self, gt, point):
		minDist = 9999999.99
		for x in range(len(gt)):
			dist = np.sqrt( ((gt[x][0] - point[0])**2) + ((gt[x][1] - point[1])**2) )
			if dist < minDist:
				minDist = dist
			if minDist is 0.0:
		return minDist
	# compute max distance between points of gt and estimate	
	def max_dist(self, gt, est):
		maxDist = -100
		for x in range(len(est)):
			dist = self.dist_to_closest( gt, est[x] )
			if dist > maxDist:
				maxDist = dist
		return maxDist
def eval_map(mappath):
    imgpath = mappath + '_in.png'
    gtpath = mappath + '_frontiers_gt.txt'
    mapE = emap.ExplorationMap()
    mapE.load_map_image( imgpath, 1)
    evaluate = Evaluator()
    est = mapE.list_frontiers()
    gt = np.loadtxt(gtpath,delimiter=' ')
    gt = [(gt[i,0],gt[i,1]) for i in range(gt.shape[0])]
    # evaluate 
    if len(est) is 0:
        print("Evaluation failed! -> no point were generated")
    elif evaluate.point_count_diff( gt, est) > MAX_POINT_COUNT_DIFFERENCE:
        print("Evaluation failed! -> point count difference is too high\n")
        print("point count difference: %d" % evaluate.point_count_diff( gt, est) )
    elif evaluate.max_dist( gt, est) > MAX_DISTANCE_TOLERANCE:
        print("Evaluation failed! -> some points are too far from their correct locations\n")
        print("max point difference: %f" % evaluate.max_dist( gt, est) )
        print("Evaluation success!")
if __name__=="__main__" :
    for instance in INSTANCES:
        eval_map( instance )

Expected output


Required dependencies

sudo apt install python-pip
pip install pillow
pip install scipy

FAQ and Known Issues
  1. The Gridmap.neighbors8 and Gridmap.neighbors4 functions filter our unpassable cells. Therefore, they cannot be directly used to check for obstacles in a cell's neighborhood.
  2. For some users scipy.misc.imread might be deprecated. If so, use imageio.imread instead. However, use scipy.misc.imread when uploading the task in BRUTE.
  3. The expected output refers to the provided testing script, which tests 3 input maps. However, the evaluation script in Brute tests 4 inputs in total.
courses/b4m36uir/hw/t1e-expl.txt · Last modified: 2019/10/29 10:55 by pragrmi1