A*, RRT and potential fields find collision-free paths.
Global planners find collision-free paths: Dijkstra, A*, D*, PRM, RRT/RRT*, potential fields; local planners (DWA, TEB, MPC) avoid dynamic obstacles. Consider kinematic constraints, costmaps, replanning and safety margins.
A* on an occupancy grid finds the shortest route around furniture.
Path planning finds a route that avoids obstacles, so a robot can move safely and efficiently in a changing environment.
A*, RRT and potential fields find collision-free paths.
Path planning finds a route that avoids obstacles, so a robot can move safely and efficiently in a changing environment.
It finds the lowest-cost path on a grid or graph using a heuristic that estimates the remaining distance to the goal.
A sampling-based method that grows a tree of collision-free states, which handles high dimensional spaces such as robot arms.
Typical applications include planning a route for a delivery robot around people and furniture, finding a collision-free path for a robotic arm in a crowded cell and routing a drone around buildings in a city.
Study "Localisation and mapping" first (topic 66 of 102 in Manufacturing, Robotics & Systems). It covers the groundwork this topic builds on.
Configuration space. Optimality vs completeness. Costmap inflation.
A* on an occupancy grid finds the shortest route around furniture.
The next topic is "Sensor fusion", which builds directly on this one.
It is classed as Intermediate in Manufacturing, Robotics & Systems (topic 67 of 102). It is an intermediate topic. Work through the beginner topics in Manufacturing, Robotics & Systems first.
Want to practise? Open the interactive course for quick checks, quizzes, simulations and progress tracking.