Motion Planning
Use motion planning to plan a path through an environment. You can use common sampling-based planners like RRT, RRT*, and Hybrid A*, deep-learning-based planner, or specify your own customizable path-planning interfaces. Use path metrics, state space sampling, and state validation to ensure your path is valid and has proper obstacle clearance or smoothness. Follow your path and avoid obstacles using pure pursuit, vector field histogram (VFH), and timed elastic band (TEB) algorithms.
Functions
Blocks
Pure Pursuit | Linear and angular velocity control commands |
Timed Elastic Band | Plan path to avoid obstacles and generate time-optimal trajectories (Since R2025a) |
Vector Field Histogram | Avoid obstacles using vector field histogram |
Topics
- Get Started with Motion Planning Networks
Motion Planning Networks for state space sampling and path planning.
- Choose Path Planning Algorithms for Navigation
Details about the benefits of different path and motion planning algorithms.
- Optimal Trajectory Generation for Urban Driving
This example shows how to perform dynamic replanning in an urban scenario using
trajectoryOptimalFrenet
. - Motion Planning in Urban Environments Using Dynamic Occupancy Grid Map
This example shows you how to perform dynamic replanning in an urban driving scene using a Frenet reference path.
- Path Following with Obstacle Avoidance in Simulink
Use Simulink® to avoid obstacles while following a path for a differential drive robot.
- Obstacle Avoidance with TurtleBot and VFH
This example shows how to use ROS Toolbox and a TurtleBot® with vector field histograms (VFH) to perform obstacle avoidance when driving a robot in an environment.
- Vector Field Histogram
VFH algorithm details and tunable properties.
- Pure Pursuit Controller
Pure Pursuit Controller functionality and algorithm details.