代做program、代写Python编程设计 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 代写Particle Filter-Based Robot Localization 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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