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Diseño de algoritmos para robots móviles

Aplicación, planificación y seguimiento de rutas, y estimación de estados

Estos algoritmos de Robotics System Toolbox™ se centran en aplicaciones robóticas móviles o en aplicaciones de vehículos terrestres. Este tipo de algoritmos proporcionan asistencia a lo largo de todo el flujo de trabajo de robótica móvil, desde la aplicación a la planificación y el control. Puede crear mapas de entornos con cuadrículas de ocupación, desarrollar algoritmos de planificación de rutas para robots en un determinado entorno, y ajustar controladores para seguir un conjunto de waypoints. Realice una estimación de estados en base a los datos del sensor de LIDAR desde el robot.

Funciones

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Aplicación

`binaryOccupancyMap`	Create occupancy grid with binary values
`getOccupancy`	Get occupancy value of locations
`inflate`	Inflate each occupied grid location
`move`	Move map in world frame
`occupancyMatrix`	Convert occupancy grid to matrix
`raycast`	Compute cell indices along a ray

Escaneo láser

`lidarScan`	Create object for storing 2-D lidar scan
`plot`	Display laser or lidar scan readings
`removeInvalidData`	Remove invalid range and angle data
`transformScan`	Transform laser scan based on relative pose

Localización

`stateEstimatorPF`	Create particle filter state estimator
`initialize`	Initialize the state of the particle filter
`predict`	Predict state of robot in next time step
`correct`	Adjust state estimate based on sensor measurement
`getStateEstimate`	Extract best state estimate and covariance from particles

Planificación y seguimiento de rutas

`mobileRobotPRM`	Create probabilistic roadmap path planner
`findpath`	Encontrar rutas entre el punto inicial y el punto objetivo en la hoja de ruta
`controllerPurePursuit`	Create controller to follow set of waypoints

Modelos de movimiento cinemático

`ackermannKinematics`	Car-like steering vehicle model
`bicycleKinematics`	Bicycle vehicle model
`differentialDriveKinematics`	Modelo de vehículo con tracción diferencial
`unicycleKinematics`	Unicycle vehicle model

Bloques

Ackermann Kinematic Model	Car-like vehicle motion using Ackermann kinematic model
Bicycle Kinematic Model	Compute car-like vehicle motion using bicycle kinematic model
Differential Drive Kinematic Model	Compute vehicle motion using differential drive kinematic model
Unicycle Kinematic Model	Compute vehicle motion using unicycle kinematic model
Pure Pursuit	Linear and angular velocity control commands

Temas

Aplicación y planificación de rutas

Occupancy Grids
Details of occupancy grid functionality and map structure.
Probabilistic Roadmaps (PRM)
How the PRM algorithm works and specific tuning parameters.
Path Planning in Environments of Different Complexity
This example demonstrates how to compute an obstacle-free path between two locations on a given map using the Probabilistic Roadmap (PRM) path planner.
Mapping with Known Poses
This example shows how to create a map of an environment using range sensor readings and robot poses for a differential drive robot.
Planificar una ruta para un robot de tracción diferencial en Simulink
Este ejemplo muestra cómo ejecutar una ruta libre de obstáculos entre dos ubicaciones de un determinado mapa en Simulink®.

Modelado de movimientos

Mobile Robot Kinematics Equations
Learn details about mobile robot kinematics equations including unicycle, bicycle, differential drive, and Ackermann models.
Simulate Different Kinematic Models for Mobile Robots
This example shows how to model different robot kinematics models in an environment and compare them.

Control de robots

Controlador de Pure Pursuit
Funcionalidad del controlador de Pure Pursuit y detalles del algoritmo.
Seguimiento de rutas para un robot de tracción diferencial
Este ejemplo muestra cómo controlar un robot para que siga la ruta deseada utilizando un simulador de robot.
Control a Differential Drive Robot in Gazebo with Simulink
This example shows how to control a differential drive robot in Gazebo co-simulation using Simulink.

Estimación de estados

Particle Filter Parameters
To use the stateEstimatorPF particle filter, you must specify parameters such as the number of particles, the initial particle location, and the state estimation method.
Particle Filter Workflow
A particle filter is a recursive, Bayesian state estimator that uses discrete particles to approximate the posterior distribution of the estimated state.
Track a Car-Like Robot Using Particle Filter
Particle filter is a sampling-based recursive Bayesian estimation algorithm, which is implemented in the stateEstimatorPF object.

Ejemplos destacados

Mapping with Known Poses

Create a map of an environment using range sensor readings and robot poses for a differential drive robot. You create a map from range sensor readings that are simulated using the rangeSensor object. The differentialDriveKinematics motion model simulates driving the robot around the room based on velocity commands. The rangeSensor gives range readings based on the pose of the robot as it follows the path.

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Path Planning in Environments of Different Complexity

Demonstrates how to compute an obstacle-free path between two locations on a given map using the Probabilistic Roadmap (PRM) path planner. PRM path planner constructs a roadmap in the free space of a given map using randomly sampled nodes in the free space and connecting them with each other. Once the roadmap has been constructed, you can query for a path from a given start location to a given end location on the map.

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Execute Tasks for a Warehouse Robot

Demonstrates how to execute an obstacle-free path for a mobile robot between three locations on a given map. The robot is expected to visit the three locations in a warehouse: a charging station, loading station, and unloading location. The sequence in which these locations are visited is dictated by a scheduler. The scheduler gives each robot a goal pose to navigate to. The robot plans a path and uses a Pure Pursuit controller to follow the waypoints based on the current pose of the robot. The Differential Drive Kinematic Model block models the simplified kinematics, which takes the linear and angular velocities from the Pure Pursuit Controller. This example builds on top of the Planificar una ruta para un robot de tracción diferencial en Simulink example.

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Control and Simulate Multiple Warehouse Robots

Control and simulate multiple robots working in a warehouse facility or distribution center. The robots drive around the facility picking up packages and delivering them to stations for storing or processing. This example builds on top of the Execute Tasks for a Warehouse Robot example, which drives a single robot around the same facility.

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Simulate a Mobile Robot in a Warehouse Using Gazebo

Simulate a warehouse robot in Gazebo. Gazebo enabled you to simulate a mobile robot that uses a range sensor, while executing certain tasks in a simulated environment. This example details how to use a simulator to apply the Execute Tasks for a Warehouse Robot example, where a robot delivers packages in a warehouse scenario. The robot makes use of the simulated range sensor in Gazebo to detect possible collisions with a dynamic environment and avoid them.

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A* Path Planning and Obstacle Avoidance in a Warehouse

An extension to the Simulate a Mobile Robot in a Warehouse Using Gazebo example. The example shows to change the PRM path planner with an A* planner, and add a vector field histogram (VFH) algorithm to avoid obstacles.

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Avoid Obstacles Using Reinforcement Learning for Mobile Robots

Uses Deep Deterministic Policy Gradient (DDPG) based reinforcement learning to develop a strategy for a mobile robot to avoid obstacles. For a brief summary of the DDPG algorithm, see Deep Deterministic Policy Gradient Agents (Reinforcement Learning Toolbox).

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