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Project: Particle Filter - Based Robot Localization November 1 2 , 2 0 2 3 Abstract Based on the information from the Particle Filter slides,

Project: Particle Filter-Based Robot Localization
November 12,2023
Abstract
Based on the information from the Particle Filter slides, here is a project idea for students
involving the implementation of a Particle Filter for localization and navigation using Python. The
project is designed to be straightforward enough for students with some programming experience,
yet challenging enough to provide a comprehensive understanding of Particle Filters in a practical
scenario.
1 Project Description
In this project, students will implement a Particle Filter to estimate the position of a robot moving in
a two-dimensional space. The robots environment will be represented as a grid, where each cell can
be either an obstacle or free space. The robot will have access to a simple sensor that provides noisy
measurements of its distance to the nearest obstacle in its front, left, right, and back directions.
1.1 Objectives
Implement a Particle Filter: Students will develop a Particle Filter to estimate the robots
location based on sensor readings and a map of the environment.
Simulate Robot Movement: Create a simulation where the robot moves a certain number of
steps in the environment, making random turns and moves.
Sensor Data Simulation: Generate simulated sensor data based on the robots actual position
and the map.
Visualization: Implement real-time visualization of the particle cloud and the estimated position of the robot in comparison to its actual position.
1.2 Implementation Approaches
Basic Python Implementation: - Use standard Python libraries (numpy,matplotlib for visualization).- Represent the map as a 2D array, the robots position as coordinates, and particles as
objects with position and weight attributes. - Implement particle resampling, motion update, and
measurement update functions.
Object-Oriented Approach: - Define classes for the Robot, Particle, and Map. - Implement
methods for movement, sensing, and updating in each class. - Use inheritance to showcase different
types of particles or robots, if desired.
Advanced Visualization with Pygame: - Utilize the pygame library for more interactive
and sophisticated visualization. - Allow real-time interaction, e.g., manually controlling the robots
movement or altering the environment.
2 Example Template
Import Necessary Libraries
1 import numpy as np
2 import matplotlib . pyplot as plt
3 from matplotlib . animation import FuncAnimation
1
Define the Robot and Particle Classes
1 class Robot :
2 def __init__( self , x , y , orientation ) :
3 self .x = x
4 self .y = y
5 self . orientation = orientation # in degrees
6
7 def move ( self , delta_x , delta_y , delta_orientation ) :
8 self .x += delta_x
9 self .y += delta_y
10 self . orientation =( self . orientation + delta_orientation )%360
11
12 # Simulate sensor reading based on robot s position
13 def sense ( self , environment_map ):
14 # Implement sensor reading logic here
15 pass
16
17 class Particle :
18 def __init__( self , x , y , orientation , weight ) :
19 self .x = x
20 self .y = y
21 self . orientation = orientation
22 self . weight = weight
23
24 def move ( self , delta_x , delta_y , delta_orientation ) :
25 # Add noise to movement
26 self .x += delta_x + np . random . normal (0,0.1)
27 self .y += delta_y + np . random . normal (0,0.1)
28 self . orientation =( self . orientation + delta_orientation )%360+ np . random .
normal (0,5)
29
30 # Update weight based on measurement
31 def update_weight ( self , measurement , robot_measurement ):
32 # Implement weight updating logic here
33 pass
Initialize Robot and Particles
1 robot = Robot (50,50,0)
2 particles =[ Particle ( np . random . randint (100), np . random . randint (100), np . random .
randint (360),1.0) for _ in range (1000)]
Particle Filter Algorithm
1 def particle_filter ( particles , robot , environment_map , move_command ):
2 # Move the robot and particles
3 robot . move (* move_command )
4 for particle in particles :
5 particle . move (* move_command )
6
7 # Update particles weights based on sensor reading
8 robot_measurement = robot . sense ( environment_map )
9 for particle in particles :
10 particle_measurement = particle . sense ( environment_map ) # Particle s sense
method not shown
11 particle . update_weight ( particle_measurement , robot_measurement )
12
13 # Resampling
14 weights = np . array ([ particle . weight for particle in particles ])
15 weights /= np .sum( weights ) # Normalize weights
16 indices = np . random . choice ( range (len( particles )), size =len( particles ), p= weights )
17 resampled_particles =[ particles [i] for i in indices ]
18
19 return resampled_particles
Visualization using Matplotlib
1 def update ( frame_number ):
2 global particles , robot
3 move_command =(1,0,10) # Example move command
4 particles = particle_filter ( particles , robot , environment

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