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Author’s Note
🔊 Author Introduction: Graduate from a top university, researcher and developer in the field of Matlab;
🚅SeatRightInscription:WalkHundredsOf MilesIn HalfOfNineTen.
✅Business Scope: Complete Code, Paper Reproduction, Program Customization, Journal Writing, Research Cooperation
🏆Code Retrieval Method: Matlab Poseidon Code Retrieval Method
🏫 For more Matlab path planning simulation content, click 👇
Matlab Path Planning (Advanced Version)
Introduction
📋📋📋 The table of contents is as follows: 🎁🎁🎁
Table of Contents
💥1 Overview
📚2 Partial Code and Running Steps
🎉3 Running Results
🌈4 Matlab Version and References
🔎5 Code Retrieval Method
🍅6 Simulation Consultation
1 Overview
Kalman Filter SLAM Path Planning
Kalman Filter SLAM (Simultaneous Localization and Mapping) path planning principles and processes are as follows: 1 Data Acquisition and Processing: Acquire environmental data through sensors (such as LiDAR, cameras, etc.) and preprocess the data, such as noise removal, filtering, etc. 2 Map Creation: Use the acquired environmental data to create a map, which can be represented in different ways such as grid maps, topological maps, 3D point cloud maps, etc. The map can be static (pre-built) or dynamic (real-time constructed). 3 Localization: Based on sensor data and the map, localize the robot. Localization can utilize various methods such as odometry, inertial navigation, visual SLAM, etc. Kalman filtering is a commonly used localization method that can fuse sensor data and map information to achieve precise localization results for the robot. 4 Path Planning: Based on the robot’s current localization and target position, plan the robot’s movement path. Path planning algorithms can be graph search-based (like A* algorithm), sampling-based (like RRT algorithm), etc. 5 Path Execution: Convert the planned path into control commands that the robot can execute, controlling the robot to move along the path. Wheel odometry or other actuators can be used to control the robot’s movement. 6 Loop Update: Continuously repeat the previous steps, updating the robot’s localization and map information in real-time to adapt to environmental changes. The process of Kalman Filter SLAM path planning is: data acquisition and processing → map creation → localization → path planning → path execution → loop update. This process helps the robot simultaneously localize its position and construct a map in unknown environments, and on this basis, plan a suitable path for movement.
2 Partial Code and Running Steps
(1) Directly run main.m to generate the graph with one click
3Running Results
3Running Results
4Matlab Version and References
1 Matlab Version
2019b
2 References
[1] Sun Haibo, Tong Ziyuan, Tang Shoufeng, Tong Mingming, Ji Yuming. Research Review on SLAM Based on Kalman Filtering and Particle Filtering [J]. Software Guide. 2018
3 Note
This section is extracted from the internet for reference only. If there is any infringement, please contact for deletion.
5 Code Retrieval Method
CodeRetrievalMethod: Matlab Poseidon Code Retrieval Method
6 Simulation Consultation
**🍅 Simulation Consultation
1 Various intelligent optimization algorithm improvements and applications**
1.1 PID Optimization
1.2 VMD Optimization
1.3 Distribution Network Reconstruction
1.4 Three-Dimensional Packing
1.5 Microgrid Optimization
1.6 Layout Optimization
1.7 Parameter Optimization
1.8 Cost Optimization
1.9 Charging Optimization
1.10 Scheduling Optimization
1.11 Price Optimization
1.12 Departure Optimization
1.13 Distribution Optimization
1.14 Coverage Optimization
1.15 Control Optimization
1.16 Inventory Optimization
1.17 Routing Optimization
1.18 Design Optimization
1.19 Location Optimization
1.20 Absorption Optimization
1.21 Site Selection Optimization
1.22 Operation Optimization
1.23 Assignment Optimization
1.24 Combination Optimization
1.25 Workshop Scheduling
1.26 Production Scheduling
1.27 Economic Scheduling
1.28 Assembly Line Scheduling
1.29 Reservoir Scheduling
1.30 Inventory Position Optimization
1.31 Bus Schedule Optimization
1.32 Container Ship Loading Optimization
1.33 Pump Combination Optimization
1.34 Medical Resource Allocation Optimization
1.35 Visual Base Station and Drone Site Selection Optimization
**2 Machine Learning and Deep Learning Classification and Prediction**
**2.1 Machine Learning and Deep Learning Classification**
2.1.1 BiLSTM Bidirectional Long Short-Term Memory Neural Network Classification
2.1.2 BP Neural Network Classification
2.1.3 CNN Convolutional Neural Network Classification
2.1.4 DBN Deep Belief Network Classification
2.1.5 DELM Deep Learning Extreme Learning Machine Classification
2.1.6 ELMAN Recurrent Neural Network Classification
2.1.7 ELM Extreme Learning Machine Classification
2.1.8 GRNN General Regression Neural Network Classification
2.1.9 GRU Gated Recurrent Unit Classification
2.1.10 KELM Mixed Kernel Extreme Learning Machine Classification
2.1.11 KNN Classification
2.1.12 LSSVM Least Squares Support Vector Machine Classification
2.1.13 LSTM Long Short-Term Memory Network Classification
2.1.14 MLP Fully Connected Neural Network Classification
2.1.15 PNN Probabilistic Neural Network Classification
2.1.16 RELM Robust Extreme Learning Machine Classification
2.1.17 RF Random Forest Classification
2.1.18 SCN Stochastic Configuration Network Model Classification
2.1.19 SVM Support Vector Machine Classification
2.1.20 XGBOOST Classification
**2.2 Machine Learning and Deep Learning Prediction**
2.2.1 ANFIS Adaptive Neuro-Fuzzy Inference System Prediction
2.2.2 ANN Artificial Neural Network Prediction
2.2.3 ARMA Autoregressive Moving Average Model Prediction
2.2.4 BF Particle Filter Prediction
2.2.5 BiLSTM Bidirectional Long Short-Term Memory Neural Network Prediction
2.2.6 BLS Broad Learning System Neural Network Prediction
2.2.7 BP Neural Network Prediction
2.2.8 CNN Convolutional Neural Network Prediction
2.2.9 DBN Deep Belief Network Prediction
2.2.10 DELM Deep Learning Extreme Learning Machine Prediction
2.2.11 DKELM Regression Prediction
2.2.12 ELMAN Recurrent Neural Network Prediction
2.2.13 ELM Extreme Learning Machine Prediction
2.2.14 ESN Echo State Network Prediction
2.2.15 FNN Feedforward Neural Network Prediction
2.2.16 GMDN Prediction
2.2.17 GMM Gaussian Mixture Model Prediction
2.2.18 GRNN General Regression Neural Network Prediction
2.2.19 GRU Gated Recurrent Unit Prediction
2.2.20 KELM Mixed Kernel Extreme Learning Machine Prediction
2.2.21 LMS Least Mean Squares Algorithm Prediction
2.2.22 LSSVM Least Squares Support Vector Machine Prediction
2.2.23 LSTM Long Short-Term Memory Network Prediction
2.2.24 RBF Radial Basis Function Neural Network Prediction
2.2.25 RELM Robust Extreme Learning Machine Prediction
2.2.26 RF Random Forest Prediction
2.2.27 RNN Recurrent Neural Network Prediction
2.2.28 RVM Relevance Vector Machine Prediction
2.2.29 SVM Support Vector Machine Prediction
2.2.30 TCN Temporal Convolutional Network Prediction
2.2.31 XGBoost Regression Prediction
2.2.32 Fuzzy Prediction
2.2.33 Singular Spectrum Analysis Method SSA Time Series Prediction
**2.3 Machine Learning and Deep Learning Practical Application Prediction**
CPI Index Prediction, PM2.5 Concentration Prediction, SOC Prediction, Financial Warning Prediction, Yield Prediction, Parking Space Prediction, Pest Prediction, Steel Thickness Prediction, Battery Health Status Prediction, Power Load Prediction, Housing Price Prediction, Corrosion Rate Prediction, Fault Diagnosis Prediction, Photovoltaic Power Prediction, Trajectory Prediction, Aircraft Engine Life Prediction, Exchange Rate Prediction, Concrete Strength Prediction, Heating Furnace Temperature Prediction, Price Prediction, Traffic Flow Prediction, Resident Consumption Index Prediction, Air Quality Prediction, Grain Temperature Prediction, Temperature Prediction, Clear Water Value Prediction, Unemployment Rate Prediction, Electricity Consumption Prediction, Transportation Volume Prediction, Manufacturing Purchasing Managers Index Prediction
**3 Image Processing**
**3.1 Image Edge Detection**
**3.2 Image Processing**
**3.3 Image Segmentation**
**3.4 Image Classification**
**3.5 Image Tracking**
**3.6 Image Encryption and Decryption**
**3.7 Image Retrieval**
**3.8 Image Registration**
**3.9 Image Stitching**
**3.10 Image Evaluation**
**3.11 Image Denoising**
**3.12 Image Fusion**
**3.13 Image Recognition**
3.13.1 Dial Recognition
3.13.2 Lane Line Recognition
3.13.3 Vehicle Counting
3.13.4 Vehicle Recognition
3.13.5 License Plate Recognition
3.13.6 Parking Space Recognition
3.13.7 Dimension Detection
3.13.8 Answer Sheet Recognition
3.13.9 Appliance Recognition
3.13.10 Fall Detection
3.13.11 Animal Recognition
3.13.12 QR Code Recognition
3.13.13 Invoice Recognition
3.13.14 Clothing Recognition
3.13.15 Chinese Character Recognition
3.13.16 Traffic Light Recognition
3.13.17 Iris Recognition
3.13.18 Fire Detection
3.13.19 Disease Classification
3.13.20 Traffic Sign Recognition
3.13.21 Card Number Recognition
3.13.22 Mask Recognition
3.13.23 Crack Detection
3.13.24 Target Tracking
3.13.25 Fatigue Detection
3.13.26 Flag Recognition
3.13.27 Grass Recognition
3.13.28 Face Recognition
3.13.29 Renminbi Recognition
3.13.30 ID Card Recognition
3.13.31 Gesture Recognition
3.13.32 Digit and Letter Recognition
3.13.33 Palm Recognition
3.13.34 Leaf Recognition
3.13.35 Fruit Recognition
3.13.36 Barcode Recognition
3.13.37 Temperature Detection
3.13.38 Defect Detection
3.13.39 Chip Detection
3.13.40 Behavior Recognition
3.13.41 Verification Code Recognition
3.13.42 Medicinal Material Recognition
3.13.43 Coin Recognition
3.13.44 Postal Code Recognition
3.13.45 Playing Card Recognition
3.13.46 Fingerprint Recognition
**3.14 Image Restoration**
**3.15 Image Compression**
**3.16 Image Steganography**
**3.17 Image Enhancement**
**3.18 Image Reconstruction**
**4 Path Planning**
**4.1 Traveling Salesman Problem (TSP)**
4.1.1 Single Traveling Salesman Problem (TSP)
4.1.2 Multiple Traveling Salesman Problem (MTSP)
**4.2 Vehicle Routing Problem (VRP)**
4.2.1 Vehicle Routing Problem (VRP)
4.2.2 Capacity-Constrained Vehicle Routing Problem (CVRP)
4.2.3 Capacity + Time Window + Distance Vehicle Routing Problem (DCTWVRP)
4.2.4 Capacity + Distance Vehicle Routing Problem (DCVRP)
4.2.5 Distance-Constrained Vehicle Routing Problem (DVRP)
4.2.6 Vehicle Routing Problem with Charging Stations + Time Windows (ETWVRP)
4.2.3 Vehicle Routing Problem with Multiple Capacities (MCVRP)
4.2.4 Distance-Constrained Multiple Vehicle Routing Problem (MDVRP)
4.2.5 Vehicle Routing Problem with Simultaneous Pickup and Delivery (SDVRP)
4.2.6 Vehicle Routing Problem with Time Windows + Capacity (TWCVRP)
4.2.6 Vehicle Routing Problem with Time Windows (TWVRP)
**4.3 Multimodal Transport Problem**
**4.4 Robot Path Planning**
4.4.1 Obstacle Avoidance Path Planning
4.4.2 Maze Path Planning
4.4.3 Grid Map Path Planning
**4.5 Delivery Path Planning**
4.5.1 Cold Chain Delivery Path Planning
4.5.2 Takeout Delivery Path Planning
4.5.3 Mask Delivery Path Planning
4.5.4 Medicine Delivery Path Planning
4.5.5 Delivery Path Planning with Charging Stations
4.5.6 Chain Supermarket Delivery Path Planning
4.5.7 Vehicle Collaborative Drone Delivery Path Planning
**4.6 Drone Path Planning**
4.6.1 Aircraft Simulation
4.6.2 Drone Flight Operations
4.6.3 Drone Trajectory Tracking
4.6.4 Drone Swarm Simulation
4.6.5 Three-Dimensional Drone Path Planning
4.6.6 Drone Formation
4.6.7 Drone Collaborative Tasks
4.6.8 Drone Task Allocation
**5 Speech Processing**
**5.1 Speech Emotion Recognition**
**5.2 Sound Source Localization**
**5.3 Feature Extraction**
**5.4 Speech Coding**
**5.5 Speech Processing**
**5.6 Speech Separation**
**5.7 Speech Analysis**
**5.8 Speech Synthesis**
**5.9 Speech Encryption**
**5.10 Speech Denoising**
**5.11 Speech Recognition**
**5.12 Speech Compression**
**5.13 Speech Hiding**
**6 Cellular Automata**
**6.1 Cellular Automata Virus Simulation**
**6.2 Cellular Automata Urban Planning**
**6.3 Cellular Automata Traffic Flow**
**6.4 Cellular Automata Gas**
**6.5 Cellular Automata Crowd Evacuation**
**6.6 Cellular Automata Forest Fire**
**6.7 Cellular Automata Game of Life**
**7 Signal Processing**
**7.1 Fault Signal Diagnosis Analysis**
7.1.1 Gear Damage Identification
7.1.2 Asynchronous Motor Rotor Broken Bar Fault Diagnosis
7.1.3 Rolling Element Inner and Outer Ring Fault Diagnosis Analysis
7.1.4 Motor Fault Diagnosis Analysis
7.1.5 Bearing Fault Diagnosis Analysis
7.1.6 Gearbox Fault Diagnosis Analysis
7.1.7 Three-Phase Inverter Fault Diagnosis Analysis
7.1.8 Diesel Engine Fault Diagnosis
**7.2 Radar Communication**
7.2.1 FMCW Simulation
7.2.2 GPS Anti-Jamming
7.2.3 Radar LFM
7.2.4 Radar MIMO
7.2.5 Radar Angle Measurement
7.2.6 Radar Imaging
7.2.7 Radar Positioning
7.2.8 Radar Echo
7.2.9 Radar Detection
7.2.10 Radar Digital Signal Processing
7.2.11 Radar Communication
7.2.12 Radar Phased Array
7.2.13 Radar Signal Analysis
7.2.14 Radar Early Warning
7.2.15 Radar Pulse Compression
7.2.16 Antenna Pattern
7.2.17 Radar Clutter Simulation
**7.3 Biological Electrical Signals**
7.3.1 Electromyography EMG
7.3.2 Electroencephalography EEG
7.3.3 Electrocardiography ECG
7.3.4 Heart Simulation
**7.4 Communication Systems**
7.4.1 DOA Estimation
7.4.2 LEACH Protocol
7.4.3 Coding and Decoding
7.4.4 Variational Mode Decomposition
7.4.5 Ultra-Wideband Simulation
7.4.6 Multipath Fading Simulation
7.4.7 Cellular Networks
7.4.8 Pipeline Leakage
7.4.9 Empirical Mode Decomposition
7.4.10 Filter Design
7.4.11 Analog Signal Transmission
7.4.12 Analog Signal Modulation
7.4.13 Digital Baseband Signals
7.4.14 Digital Channels
7.4.15 Digital Signal Processing
7.4.16 Digital Signal Transmission
7.4.17 Digital Signal Denoising
7.4.18 Underwater Acoustic Communication
7.4.19 Communication Simulation
7.4.20 Wireless Transmission
7.4.21 Bit Error Rate Simulation
7.4.22 Modern Communication
7.4.23 Channel Estimation
7.4.24 Signal Detection
7.4.25 Signal Fusion
7.4.26 Signal Recognition
7.4.27 Compressed Sensing
7.4.28 Noise Simulation
7.4.29 Noise Interference
**7.5 Drone Communication**
**7.6 Wireless Sensor Positioning and Layout**
7.6.1 WSN Positioning
7.6.2 Height Estimation
7.6.3 Filtering and Tracking
7.6.4 Target Positioning
7.6.4.1 Dv-Hop Positioning
7.6.4.2 RSSI Positioning
7.6.4.3 Intelligent Algorithm Optimization Positioning
7.6.5 Integrated Navigation
**8 Power System**
Microgrid Optimization, Reactive Power Optimization, Distribution Network Reconstruction, Energy Storage Configuration