mobile robot algorithm design -凯发k8网页登录

mapping, path planning, path following, state estimation

these robotics system toolbox™ algorithms focus on mobile robotics or ground vehicle applications. these algorithms help you with the entire mobile robotics workflow from mapping to planning and control. you can create maps of environments using occupancy grids, develop path planning algorithms for robots in a given environment, and tune controllers to follow a set of waypoints. perform state estimation based on lidar sensor data from your robot.

functions

mapping

	create occupancy grid with binary values
	get occupancy value of locations
	inflate each occupied location
	move map in world frame
	convert occupancy grid to matrix
	compute cell indices along a ray

laser scans

	create object for storing 2-d lidar scan
	display laser or lidar scan readings
	remove invalid range and angle data
	transform laser scan based on relative pose

localization

	create particle filter state estimator
	initialize the state of the particle filter
	predict state of robot in next time step
	adjust state estimate based on sensor measurement
	extract best state estimate and covariance from particles

path planning and following

	create probabilistic roadmap path planner
	find path between start and goal points on roadmap
	create controller to follow set of waypoints

kinematic motion models

	car-like steering vehicle model
	bicycle vehicle model
	differential-drive vehicle model
	unicycle vehicle model

blocks

	car-like vehicle motion using ackermann kinematic model
	compute car-like vehicle motion using bicycle kinematic model
	compute vehicle motion using differential drive kinematic model
	compute vehicle motion using unicycle kinematic model
	linear and angular velocity control commands

topics

mapping and path planning

details of occupancy grid functionality and map structure.
how the prm algorithm works and specific tuning parameters.
this example demonstrates how to compute an obstacle-free path between two locations on a given map using the probabilistic roadmap (prm) path planner.
this example shows how to create a map of an environment using range sensor readings and robot poses for a differential drive robot.
this example demonstrates how to execute an obstacle-free path between two locations on a given map in simulink®.

motion modeling

learn details about mobile robot kinematics equations including unicycle, bicycle, differential drive, and ackermann models.
this example shows how to model different robot kinematics models in an environment and compare them.

robot control

pure pursuit controller functionality and algorithm details.
this example demonstrates how to control a robot to follow a desired path using a robot simulator.
this example shows how to control a differential drive robot in gazebo co-simulation using simulink.

state estimation

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.
a particle filter is a recursive, bayesian state estimator that uses discrete particles to approximate the posterior distribution of the estimated state.
particle filter is a sampling-based recursive bayesian estimation algorithm, which is implemented in the stateestimatorpf object.

featured examples

control and simulate multiple warehouse robots

control and simulate multiple robots working in a warehouse facility or distribution center.

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.

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.