Global Path Planning for Optimal Robot Navigation
Implementation of global path planning for optimal robot navigation using MATLAB, featuring a direction-weighted binary tree algorithm.
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.
Global Path Planning in Optimal Robot Path Planning
Global Path Planning in Optimal Robot Path Planning