motion planning -凯发k8网页登录

path metrics, rrt path planners, path following

use motion planning to plan a path through an environment. you can use common sampling-based planners like rrt, rrt*, and hybrid a*, or specify your own customizable path-planning interfaces. use path metrics and state validation to ensure your path is valid and has proper obstacle clearance or smoothness. follow your path and avoid obstacles using pure pursuit and vector field histogram algorithms.

functions

paths and path segments

	planned path
	path representing control-based kinematic trajectory
	dubins path connection type
	dubins path segment connecting two poses
	reeds-shepp path connection type
	reeds-shepp path segment connecting two poses

path metrics

	information for path metrics
	minimum clearance of path
	determine if planned path is obstacle free
	smoothness of path
	visualize path metrics in map environment

state spaces

	se(2) state space
	se(3) state space
	state space for dubins vehicles
	state space for reeds-shepp vehicles

state validation

	state validator based on 2-d grid map
	state validator based on 3-d grid map
	state validator based on 2-d costmap
	check if state is valid
	check if path between states is valid

state propagation

	state propagator for control-based planning
	state propagator for wheeled robotic systems
	estimate cost of propagating to target state
	propagate system without validation
	propagate system and return valid motion
	generate control command and duration
	set up the mobile robot state propagator

`navgraph`

	create `navgraph` object
	find ids of links
	find ids of states
	find state vectors of state indices
	find indices for queried state vectors
	find successive state indices and costs

motion planning

	create an rrt planner for geometric planning
	create an optimal rrt path planner (rrt*)
	create bidirectional rrt planner for geometric planning
	control-based rrt planner
	graph-based a* path planner
	a* path planner for grid map
	hybrid a* path planner
	create probabilistic roadmap path planner
	benchmark path planners using generated metrics

path optimization

	create optimization options for optimizepath function
	optimize path while maintaining safe distance from obstacle

frenet trajectory generation

	smooth reference path fit to waypoints
	find optimal trajectory along reference path
	find optimal trajectory along reference path

custom path planning interfaces

	create sample implementation for path planning interface
	create state space for path planning
	create state validator for path planning

path following

	avoid obstacles using vector field histogram
	create controller to follow set of waypoints
	avoid unseen obstacles with time-optimal trajectories
	compute heading angle from xy-points of path
	retrieve velocity command from time series of velocity commands

dynamic capsule list

	dynamic capsule-based obstacle list
	dynamic capsule-based obstacle list
	add ego bodies to capsule list
	add obstacles to 2-d capsule list
	check for collisions between ego bodies and obstacles
	geometric properties of ego bodies
	poses of ego bodies
	geometric properties of obstacles
	poses of obstacles

blocks

	linear and angular velocity control commands
	avoid obstacles using vector field histogram

topics

details about the benefits of different path and motion planning algorithms.
this example shows how to plan a path to move bulky furniture in a tight space avoiding poles.
plan a grasping motion for a kinova jaco assistive robotics arm using the rapidly-exploring random tree (rrt) algorithm.
this example shows how to perform dynamic replanning in an urban scenario using trajectoryoptimalfrenet.
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.
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.
vfh algorithm details and tunable properties.
pure pursuit controller
pure pursuit controller functionality and algorithm details.

featured examples

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.

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.