Planification du mouvement
Utilisez la planification de mouvements pour planifier un chemin à travers un environnement. Vous pouvez utiliser des planificateurs courants basés sur l'échantillonnage tels que RRT, RRT* et Hybrid A*, ou spécifier vos propres interfaces de planification de chemin personnalisables. Utilisez les métriques de chemin, l'échantillonnage de l'espace d'état et la validation d'état pour garantir que votre chemin est valide et présente un franchissement d'obstacles ou une fluidité appropriés. Suivez votre chemin et évitez les obstacles à l'aide des algorithmes de poursuite pure, d'histogramme de champ vectoriel (VFH) et de bande élastique chronométrée (TEB).
Fonctions
Blocs
| Pure Pursuit | Commandes de contrôle de vitesse linéaire et angulaire |
| Vector Field Histogram | Évitez les obstacles à l'aide de l'histogramme de champ vectoriel |
Concepts
- Get Started with Motion Planning Networks
Motion Planning Networks for state space sampling and path planning.
Exemples et procédures
- Choose Path Planning Algorithms for Navigation
Details about the benefits of different path and motion planning algorithms.
- Moving Furniture in a Cluttered Room with RRT
This example shows how to plan a path to move bulky furniture in a tight space avoiding poles.
- 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.
Sélection d՚exemples
Plan Mobile Robot Paths Using RRT
Use the rapidly exploring random tree (RRT) algorithm to plan a path for a vehicle through a known map. Special vehicle constraints are also applied with a custom state space. You can tune your own planner with custom state space and path validation objects for any navigation application.
Dynamic Replanning on an Indoor Map
Perform dynamic replanning on a warehouse map with a range finder and an A* path planner.
Highway Lane Change
Perceive surround-view information and use it to design an automated lane change maneuver system for highway driving scenarios.
Highway Trajectory Planning Using Frenet Reference Path
Demonstrates how to plan a local trajectory in a highway driving scenario. This example uses a reference path and dynamic list of obstacles to generate alternative trajectories for an ego vehicle. The ego vehicle navigates through traffic defined in a provided driving scenario from a drivingScenario object. The vehicle alternates between adaptive cruise control, lane changing, and vehicle following maneuvers based on cost, feasibility, and collision-free motion.
Reverse-Capable Motion Planning for Tractor-Trailer Model Using plannerControlRRT
Find global path-planning solutions for systems with complex kinematics using the kinematics-based planner, plannerControlRRT. The example is organized into three primary sections:
Object Tracking and Motion Planning Using Frenet Reference Path
Dynamically replan the motion of an autonomous vehicle based on the estimate of the surrounding environment. You use a Frenet reference path and a joint probabilistic data association (JPDA) tracker to estimate and predict the motion of other vehicles on the highway. Compared to the Highway Trajectory Planning Using Frenet Reference Path example, you use these estimated trajectories from the multi-object tracker in this example instead of ground truth for motion planning.
Optimization Based Path Smoothing for Autonomous Vehicles
Optimize the path for a car-like robot by maintaining a smooth curvature and a safe distance from the obstacles in a parking lot.
Benchmark Path Planners for Differential Drive Robots in Warehouse Map
Choose the best 2-D path planner for a differential drive robot in a warehouse environment from the available path planners. Use the plannerBenchmark object to benchmark the path planners plannerRRT, plannerRRTStar, plannerBiRRT, plannerPRM, and plannerHybridAstar on the warehouse environment with the randomly chosen start and goal poses. Compare the path planners based on their ability to find a valid path, clearance from the obstacles, time taken to initialize a planner, time taken to find a path, length of the path, and smoothness of the path. A suitable planner is chosen based on the performance of each path planner on the above mentioned metrics.
Offroad Planning with Digital Elevation Models
Process and store 2.5-D information, and presents various techniques for using it for an offroad path planner.
Enable Vehicle Collision Checking for Path Planning Using Hybrid A*
Use a Hybrid A* planner to plan a path to a narrow parking space, while accounting for the shape of a car-like robot.
Plan Path to Custom Goal Region for Mobile Robot
Plan a path for a mobile robot to a goal region using a rapidly exploring random tree (RRT) path planner. In this example, you can define a custom goal region as a 2-D polygon, and then plan a path to it.
Hybrid Sampling Method for Motion Planning in Warehouse Environment
Combine uniform sampling and Gaussian sampling approaches for motion planning in narrow passages and wide spaces.
Train Deep Learning-Based Sampler for Motion Planning
Create a deep learning-based sampler using Motion Planning Networks to speed up path planning using sampling-based planners like RRT (rapidly-exploring random tree) and RRT*. For information about Motion Planning Networks (MPNet) for state space sampling, see Get Started with Motion Planning Networks.
Accelerate Motion Planning with Deep-Learning-Based Sampler
The example shows how to use sampling-based planners such as RRT (rapidly-exploring random tree) and RRT* with Motion Planning Networks (MPNet), deep-learning-based sampler to find optimal paths efficiently.
Route Planning in Uneven Terrain Based on Vehicle Requirements
Use navGraph and plannerAStar to find a path through rough terrain while accounting for vehicle-based requirements and constraints.
