Explore MATLAB source code curated for "机器人导航" with clean implementations, documentation, and examples.
This article presents an optical flow-based robot navigation and obstacle avoidance algorithm, implemented and validated through simulation on a virtual robotic platform with detailed code implementation insights.
Robot navigation program featuring implementation code for particle filter and extended Kalman filter algorithms.
The Kalman Filter is an "optimal recursive data processing algorithm" that provides the most efficient and effective solution for a wide range of problems. It has seen extensive applications for over 30 years in fields including robotic navigation, control systems, sensor data fusion, military radar systems, and missile tracking. In recent years, it has been increasingly applied to computer image processing tasks such as facial recognition, image segmentation, and edge detection. The filter operates through a two-step process: prediction (projecting state estimates forward) and update (correcting estimates with new measurements), typically implemented using matrix operations for state transition and covariance calculations.
Implementation of Path Planning Using Particle Swarm Optimization Algorithm
Implementation of Ant Colony Algorithm for Shortest Path Optimization in MATLAB with Code-Based Enhancements
Comprehensive Guide to Simultaneous Localization and Mapping (SLAM) using MATLAB's Robotics Toolbox with Code Examples and Implementation Strategies
Implementation of Artificial Neural Network Path Planning Using Energy Functions