Implementation of Grid-Based Path Planning for Robots
MATLAB implementation of grid-based robotic path planning with practical routines, algorithm comparisons, and obstacle avoidance capabilities
Path Planning for Robotics in Two-Dimensional Planes
MATLAB-based implementation of robot path planning on a 2D plane using grid-based methods for environment and obstacle processing, featuring algorithm demonstrations and simulation capabilities.
Particle Swarm Optimization with Grid Method for Mobile Robot Shortest Path Planning
Implementing PSO-based Grid Approach for Optimal Path Planning in Mobile Robotics with Code Integration
Robot Path Planning Using Ant Colony Optimization Algorithm
This research focuses on global path planning for robots in static environments. The methodology involves environment abstraction using grid-based modeling to construct the robot workspace, followed by implementation of Ant Colony Optimization (ACO) to simulate ant foraging behavior for identifying optimal paths from start to terminal points. MATLAB simulation includes graphical output of optimized paths, with parameter selection validated through three distinct static environment scenarios. Comparative analysis with Genetic Algorithm-based path planning demonstrates ACO's superior performance in both time and space complexity.