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マニピュレーターのモーション プランニング

RRT および剛体ツリーを使用したパス計画

マニピュレーターのモーション プランニングには、ロボットの自由度 (DOF) およびロボット モデルの運動学的拘束に基づいた高次元の空間でのパスの計画が含まれます。ロボット モデルの運動学的拘束は、rigidBodyTree オブジェクトとして指定されます。manipulatorRRT を使用して、Rapidly-Exploring Random Tree (RRT) アルゴリズムを使用したジョイント空間でパスを計画します。


manipulatorRRTPlan motion for rigid body tree using bidirectional RRT
manipulatorStateSpaceState space for rigid body tree robot models
manipulatorCollisionBodyValidatorValidate states for collision bodies of rigid body tree
workspaceGoalRegionDefine workspace region of end-effector goal poses



planPlan path using RRT for manipulators
interpolateInterpolate states along path from RRT
shortenTrim edges to shorten path from RRT
isStateValidCheck if state is valid
isMotionValidCheck if path between states is valid
sampleSample end-effector poses in world frame
showVisualize workspace bounds, reference frame, and offset frame


  • 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.

  • MATLAB 向けに RRT プランナーと Stateflow を使用したピックアンドプレースのワークフロー

    この例では、KINOVA® Gen3 などのロボット マニピュレーター用にエンドツーエンドのピックアンドプレース ワークフローを設定する方法を説明します。

  • 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. In this example, you model the backhoe in simplified form as a kinematic tree, then use a sampling-based motion planner to determine a viable path for the backhoe between two poses in the presence of obstacles. Simulate the outcome in a Simscape™ Multibody™ model.

  • 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. If you use capsule approximations, you can reduce your path-planning times.