代做program、代写Python编程设计

日期：2024-12-16 09:42

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 robot’s 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 robot’s

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 robot’s 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 robot’s position as coordinates, and particles as

objects with position and weight attributes. - Implement particle resampling, motion update, and

measurement update functions.

Object-Oriented Approach: - Deffne 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 robot’s

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

1Deffne 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_map , move_command )

5

26 # Clear current plot

7 plt . cla ()

8

9 # Plot particles

10 xs , ys = zip (*[( particle .x, particle .y) for particle in particles ])

11 plt . scatter (xs , ys , color =’blue ’, s=1)

12

13 # Plot robot

14 plt . scatter ( robot .x, robot .y, color =’red ’, s =10)

15

16 plt . xlim (0, 100)

17 plt . ylim (0, 100)

18 plt . title (" Particle Filter Robot Localization ")

19

20 fig = plt . figure ()

21 ani = FuncAnimation (fig , update , frames =10 , interval =1000)

22 plt . show ()

Note:

• This code provides a basic framework and requires further development to fully simulate the

environment, sensor readings, and particle weight updates.

• The move and sense methods for the Robot and Particle classes should be tailored to the speciffc

problem and sensor model.

• The visualization updates the particles and robot position at each step, illustrating the working

of the particle fflter.

This implementation serves as a foundational guideline, and students are encouraged to build upon it,

reffning and adding complexity as needed for their speciffc project requirements.

3 Expected Outcomes

• - Understand the concept and application of Particle Filters in localization.

• - Gain experience in simulating robot movement and sensor readings.

• - Develop skills in probabilistic reasoning and algorithm implementation.

4 Evaluation Criteria

• - Accuracy of the localization (how close the estimated position is to the actual position).

• - Efffciency of the implementation (number of particles used vs. accuracy).

• - Quality of the visualization and ease of understanding the Particle Filter process.

This project provides a balance of theoretical understanding and practical application, making it

an excellent exercise for students to grasp the fundamentals of Particle Filters in robotics.

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