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Mobile Robot Algorithm Design

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

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binaryOccupancyMapCreate occupancy grid with binary values
getOccupancyGet occupancy value of locations
inflateInflate each occupied grid location
moveMove map in world frame
occupancyMatrixConvert occupancy grid to matrix
raycastCompute cell indices along a ray
lidarScanCreate object for storing 2-D lidar scan
plotDisplay laser or lidar scan readings
removeInvalidDataRemove invalid range and angle data
transformScanTransform laser scan based on relative pose
stateEstimatorPFCreate particle filter state estimator
initializeInitialize the state of the particle filter
predictPredict state of robot in next time step
correctAdjust state estimate based on sensor measurement
getStateEstimateExtract best state estimate and covariance from particles
mobileRobotPRMCreate probabilistic roadmap path planner
findpathFind path between start and goal points on roadmap
controllerPurePursuitCreate controller to follow set of waypoints
ackermannKinematicsCar-like steering vehicle model
bicycleKinematicsBicycle vehicle model
differentialDriveKinematicsDifferential-drive vehicle model
unicycleKinematicsUnicycle vehicle model

Blocks

Ackermann Kinematic ModelCar-like vehicle motion using Ackermann kinematic model
Bicycle Kinematic ModelCompute car-like vehicle motion using bicycle kinematic model
Differential Drive Kinematic ModelCompute vehicle motion using differential drive kinematic model
Unicycle Kinematic ModelCompute vehicle motion using unicycle kinematic model
Pure PursuitLinear and angular velocity control commands

Topics

Mapping and Path Planning

Motion Modeling

Robot Control

State Estimation

  • Particle Filter Parameters
    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.
  • Particle Filter Workflow
    A particle filter is a recursive, Bayesian state estimator that uses discrete particles to approximate the posterior distribution of the estimated state.
  • Track a Car-Like Robot Using Particle Filter
    Particle filter is a sampling-based recursive Bayesian estimation algorithm, which is implemented in the stateEstimatorPF object.