gonzaloruizdevilla

npm install
npm run asbuild

gonzaloruizdevilla

#include <math.h>
#include <algorithm>
#include <iostream>
#include <vector>
#include "hybrid_breadth_first.h"

// Initializes HBF
HBF::HBF() {}

HBF::~HBF() {}

gonzaloruizdevilla

def expand(state, goal):
    next_states = []
    for delta in range(-35, 40, 5): 
        # Create a trajectory with delta as the steering angle using the bicycle model:

        # ---Begin bicycle model---
        delta_rad = deg_to_rad(delta)
        omega = SPEED/LENGTH * tan(delta_rad)
        next_x = state.x + SPEED * cos(theta)
        next_y = state.y + SPEED * sin(theta)

gonzaloruizdevilla

#include "classifier.h"
#include <math.h>
#include <string>
#include <vector>
#include <iostream>

using Eigen::ArrayXd;
using std::string;
using std::vector;

gonzaloruizdevilla

# ----------
# User Instructions:
# 
# Implement the function optimum_policy2D below.
#
# You are given a car in grid with initial state
# init. Your task is to compute and return the car's 
# optimal path to the position specified in goal; 
# the costs for each motion are as defined in cost.
#

gonzaloruizdevilla

#include <iostream>
#include "multiv_gauss.h"

int main() {
  // define inputs
  double sig_x, sig_y, x_obs, y_obs, mu_x, mu_y;
  // define outputs for observations
  double weight1, weight2, weight3;
  // final weight
  double final_weight;

gonzaloruizdevilla

# Now we want to simulate robot
# motion with our particles.
# Each particle should turn by 0.1
# and then move by 5. 
#
#
# Don't modify the code below. Please enter
# your code at the bottom.

from math import *

gonzaloruizdevilla

# Now add noise to your robot as follows:
# forward_noise = 5.0, turn_noise = 0.1,
# sense_noise = 5.0.
#
# Once again, your robot starts at 30, 50,
# heading north (pi/2), then turns clockwise
# by pi/2, moves 15 meters, senses,
# then turns clockwise by pi/2 again, moves
# 10 m, then senses again.

gonzaloruizdevilla

#include <algorithm>
#include <iostream>
#include <vector>

#include "helpers.h"

using std::vector;
using std::cout;
using std::endl;

gonzaloruizdevilla

#include <algorithm>
#include <iostream>
#include <vector>

#include "helpers.h"

using std::vector;

// function to get pseudo ranges
vector<float> pseudo_range_estimator(vector<float> landmark_positions,