This work, focuses on path planning for autonomous robots using different algorithms. Path planning is a crucial aspect of autonomous navigation, allowing robots to find the shortest and most obstacle-free path from a starting point to a goal state. This, along with perception and control systems, forms the foundation of autonomous navigation.
The document demonstrates model development in MATLAB using various algorithms, including:
Dijkstra’s Algorithm: A popular pathfinding algorithm that finds the shortest path between two points on a graph. The MATLAB program developed in this section implements Dijkstra’s algorithm to plan a path from the start location to the goal location in a grid world map.
D* Algorithm: A dynamic pathfinding algorithm that allows for more efficient updates when the environment or the destination changes. This MATLAB program uses the D* algorithm for path planning from the start position to the goal position in the map.
Probabilistic Roadmap Method (PRM): A popular sampling-based motion planning algorithm that creates a graph by randomly sampling points in the environment and connecting them. The MATLAB program in this section uses PRM for path planning from the start position to the goal position in the map.
Lattice Planner Algorithm: A motion planning algorithm that generates a lattice structure in the state space, enabling the robot to search for a feasible path. The MATLAB program developed in this section uses the lattice planner algorithm for path planning from the start position to the goal position in the map.
Rapidly-Exploring Random Tree (RRT): A sampling-based motion planning algorithm that quickly explores the state space by randomly sampling points and connecting them to the nearest vertex in the tree. The MATLAB program in this section uses the RRT algorithm for path planning from the start position to the goal position in the map.
The work’s document provides detailed explanations, MATLAB code snippets, and visualizations for each of these algorithms, making it an excellent resource for anyone interested in learning about different path planning methods for autonomous robots. The reference section lists the MATLAB help documentation as the primary source of information.