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Planificación de trayectorias de manipuladores

Planificación de rutas mediante RRT y árboles de cuerpo rígido

El proceso de planificación de trayectorias del manipulador implica planificar rutas en un espacio dimensional alto en función de los grados de libertad (DOF) del robot y las restricciones cinemáticas del modelo de robot. Las restricciones cinemáticas del modelo de robot se especifican como un objeto rigidBodyTree. Utilice manipulatorRRT para planificar rutas en el espacio articular utilizando el algoritmo de árbol aleatorio de exploración rápida (RRT).

Objetos

`manipulatorRRT`	Plan motion for rigid body tree using bidirectional RRT
`manipulatorStateSpace`	State space for rigid body tree robot models
`manipulatorCollisionBodyValidator`	Validate states for collision bodies of rigid body tree
`workspaceGoalRegion`	Define workspace region of end-effector goal poses

Funciones

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RRT

`plan`	Plan path using RRT for manipulators
`interpolate`	Interpolate states along path from RRT
`shorten`	Trim edges to shorten path from RRT

Espacio de estados y validador

`isStateValid`	Check if state is valid
`isMotionValid`	Check if path between states is valid

Región objetivo del área de trabajo

`sample`	Sample end-effector poses in world frame
`show`	Visualize workspace bounds, reference frame, and offset frame

Temas

Pick and Place Using RRT for Manipulators
Using manipulators to pick and place objects in an environment may require path planning algorithms like the rapidly-exploring random tree planner.
Pick-and-Place Workflow Using RRT Planner and Stateflow for MATLAB
This example shows how to setup an end-to-end pick-and-place workflow for a robotic manipulator like the KINOVA® Gen3.
Pick-and-Place Workflow in Gazebo Using Point-Cloud Processing and RRT Path Planning
Set up an end-to-end, pick-and-place workflow for a robotic manipulator like the KINOVA® Gen3.
Plan Paths With End-Effector Constraints Using State Spaces For Manipulators
Plan a manipulator robot path using sampling-based planners like the rapidly-exploring random trees (RRT) algorithm.
Motion Planning for Backhoe Using RRT
This example shows how to plan a path for a backhoe, in an environment that contains obstacles, by using a motion planner.
Reduce Motion Planning Times Using Capsule Approximation
This example shows how to approximate the collision geometries of a rigid body tree using a capsuleApproximation object and then perform motion planning with a manipulatorRRT object.

Ejemplos destacados

Pick and Place Using RRT for Manipulators

Using manipulators to pick and place objects in an environment may require path planning algorithms like the rapidly-exploring random tree planner. The planner explores in the joint-configuration space and searches for a collision-free path between different robot configurations. This example shows how to use the manipulatorRRT object to tune the planner parameters and plan a path between two joint configurations based on a rigidBodyTree robot model of the Franka Emika™ Panda robot. After tuning the planner parameters, the robot manipulator plans a path to move a can from one place to another.

Abrir script en vivo

Pick-and-Place Workflow Using RRT Planner and Stateflow for MATLAB

Setup an end-to-end pick-and-place workflow for a robotic manipulator like the KINOVA® Gen3.

Abrir script en vivo

Pick-and-Place Workflow in Gazebo Using Point-Cloud Processing and RRT Path Planning

Set up an end-to-end, pick-and-place workflow for a robotic manipulator like the KINOVA® Gen3.

Abrir script en vivo