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運動の計画

パス メトリクス、RRT パス プランナー、パス追従

運動の計画を使用して、環境内を通るパスを計画します。RRT、RRT*、Hybrid A* などの一般的なサンプリングベースのプランナーを使用するか、独自のカスタマイズ可能なパス計画インターフェイスを指定することができます。パス メトリクスと状態検証を使用して、パスが有効であり障害物とのクリアランスまたは滑らかさが適切であることを確認します。単純追跡と Vector Field Histogram アルゴリズムを使用して、パスを追従し障害物を回避します。

関数

すべて展開する

navPathPlanned path
dubinsConnectionDubins path connection type
dubinsPathSegmentDubins path segment connecting two poses
reedsSheppConnectionReeds-Shepp path connection type
reedsSheppPathSegmentReeds-Shepp path segment connecting two poses
pathmetricsInformation for path metrics
clearanceMinimum clearance of path
isPathValidDetermine if planned path is obstacle free
smoothnessSmoothness of path
showVisualize path metrics in map environment
stateSpaceSE2SE(2) state space
stateSpaceSE3SE(3) state space
stateSpaceDubinsState space for Dubins vehicles
stateSpaceReedsSheppState space for Reeds-Shepp vehicles
validatorOccupancyMapState validator based on 2-D grid map
validatorOccupancyMap3DState validator based on 3-D grid map
validatorVehicleCostmapState validator based on 2-D costmap
isStateValidCheck if state is valid
isMotionValidCheck if path between states is valid
plannerRRTCreate an RRT planner for geometric planning
plannerRRTStarCreate an optimal RRT path planner (RRT*)
plannerBiRRTCreate bidirectional RRT planner for geometric planning
plannerAStarGridA* path planner for grid map
plannerHybridAStarHybrid A* path planner
referencePathFrenetSmooth reference path fit to waypoints
trajectoryGeneratorFrenetFind optimal trajectory along reference path
trajectoryOptimalFrenetFind optimal trajectory along reference path
createPlanningTemplateCreate sample implementation for path planning interface
nav.StateSpaceCreate state space for path planning
nav.StateValidatorCreate state validator for path planning
controllerVFHAvoid obstacles using vector field histogram
controllerPurePursuit一連の中間点に追従するコントローラーの作成
dynamicCapsuleListDynamic capsule-based obstacle list
dynamicCapsuleList3DDynamic capsule-based obstacle list
addEgoAdd ego bodies to capsule list
addObstacleAdd obstacles to 2-D capsule list
checkCollisionCheck for collisions between ego bodies and obstacles
egoGeometryGeometric properties of ego bodies
egoPosePoses of ego bodies
obstacleGeometryGeometric properties of obstacles
obstaclePosePoses of obstacles

ブロック

Pure PursuitLinear and angular velocity control commands
Vector Field HistogramAvoid obstacles using vector field histogram

トピック

Choose Path Planning Algorithms for Navigation

Details about the benefits of different path and motion planning algorithms.

Plan Mobile Robot Paths Using RRT

This example shows how to 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.

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. This example shows a workflow of the "Piano Mover's Problem", which is used for testing path planning algorithms with constrained state spaces. This example uses the plannerRRTStar object to implement a custom optimized rapidly-exploring tree (RRT*) algoirthm. Provided example helpers illustrate how to define custom state spaces and state valdiation for any motion planning application.

Motion Planning with RRT for a Robot Manipulator

Plan a grasping motion for a Kinova Jaco Assistive Robotics Arm using the rapidly-exploring random tree (RRT) algorithm. This example uses a plannerRRTStar object to sample states and plan the robot motion. Provided example helpers illustrate how to define custom state spaces and state validation for motion planning applications.

Dynamic Replanning on an Indoor Map

This example shows how to perform dynamic replanning on a warehouse map with a range finder and an A* path planner.

Highway Lane Change

This example shows how to simulate an automated lane change maneuver system for highway driving scenario.

Highway Trajectory Planning Using Frenet Reference Path

This example 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.

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. In this example, you use a dynamic occupancy grid map estimate of the local environment to find optimal local trajectories.

Path Following with Obstacle Avoidance in Simulink®

This example shows you how to use Simulink to avoid obstacles while following a path for a differential drive robot. This example uses ROS to send and receive information from a MATLAB®-based simulator. You can replace the simulator with other ROS-based simulators such as Gazebo®.

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. The robot wanders by driving forward until obstacles get in the way. The controllerVFH object computes steering directions to avoid objects while trying to drive forward.

Vector Field Histogram

VFH algorithm details and tunable properties.

単純追跡コントローラー

単純追跡コントローラーの機能とアルゴリズムの詳細。

注目の例