이동 로봇 알고리즘 설계

맵 작성, 경로 계획, 경로 추종, 상태 추정

다음 Robotics System Toolbox™ 알고리즘은 이동 로봇이나 지상 이동체의 응용 사례에 중점을 둡니다. 이러한 알고리즘은 맵 작성부터 계획, 제어에 이르는 전체적인 이동 로봇 워크플로를 지원합니다. 점유 그리드를 사용하여 주변 환경의 맵을 작성하고, 주어진 환경에 따라 로봇의 경로 계획 알고리즘을 개발하고, 일련의 웨이포인트를 추종하도록 제어기를 조정할 수 있습니다. 로봇의 라이다 센서 데이터를 기반으로 상태 추정을 수행합니다.

함수

모두 확장

맵 작성

`binaryOccupancyMap`	이진 값으로 점유 그리드 생성
`getOccupancy`	Get occupancy value of locations
`inflate`	각 점유 위치 확장
`move`	Move map in world frame
`occupancyMatrix`	점유 그리드를 행렬로 변환
`raycast`	Compute cell indices along a ray

레이저 스캔

`lidarScan`	Create object for storing 2-D lidar scan
`plot`	Display laser or lidar scan readings
`removeInvalidData`	Remove invalid range and angle data
`transformScan`	Transform laser scan based on relative pose

위치추정

`stateEstimatorPF`	Create particle filter state estimator
`initialize`	Initialize the state of the particle filter
`predict`	Predict state of robot in next time step
`correct`	Adjust state estimate based on sensor measurement
`getStateEstimate`	Extract best state estimate and covariance from particles

경로 계획과 경로 추종

`mobileRobotPRM`	확률적 로드맵 경로 플래너 생성
`findpath`	로드맵에서 출발 지점과 목표 지점 사이의 경로 찾기
`controllerPurePursuit`	일련의 웨이포인트를 따라가도록 제어기 생성

기구학적 모션 모델

`ackermannKinematics`	Car-like steering vehicle model
`bicycleKinematics`	자전거 이동체 모델
`differentialDriveKinematics`	차동 구동 이동체 모델
`unicycleKinematics`	외륜 이동체 모델

블록

Ackermann Kinematic Model	Ackermann 기구학 모델을 사용한 차륜 이동체의 모션
Bicycle Kinematic Model	자전거 기구학 모델을 사용하여 차륜 이동체의 모션 계산
Differential Drive Kinematic Model	차동 구동 기구학 모델을 사용하여 이동체 모션 계산
Unicycle Kinematic Model	외륜 기구학 모델을 사용하여 이동체 모션 계산
Pure Pursuit	선속도 제어 명령과 각속도 제어 명령

도움말 항목

맵 작성과 경로 계획

Occupancy Grids
Details of occupancy grid functionality and map structure.
Probabilistic Roadmaps (PRM)
How the PRM algorithm works and specific tuning parameters.
Path Planning in Environments of Different Complexity
This example demonstrates how to compute an obstacle-free path between two locations on a given map using the Probabilistic Roadmap (PRM) path planner.
Mapping with Known Poses
This example shows how to create a map of an environment using range sensor readings and robot poses for a differential drive robot.
Simulink에서 차동 구동 로봇의 경로 계획하기
이 예제는 Simulink®에서 주어진 맵의 두 위치 사이에 장애물 없는 경로를 실행하는 방법을 보여줍니다.

모션 모델링

Mobile Robot Kinematics Equations
Learn details about mobile robot kinematics equations including unicycle, bicycle, differential drive, and Ackermann models.
Simulate Different Kinematic Models for Mobile Robots
This example shows how to model different robot kinematics models in an environment and compare them.

로봇 제어

Pure Pursuit 제어기
Pure Pursuit 제어기 기능 및 알고리즘 세부 정보
차동 구동 로봇의 경로 추종
이 예제는 로봇 시뮬레이터를 사용하여 로봇이 목표 경로를 추종하도록 제어하는 방법을 보여줍니다.
Control Differential Drive Robot in Gazebo with Simulink
This example shows how to control a differential drive robot in Gazebo co-simulation using Simulink.
Multi-Robot Control with Resource Allocation and Conflict Management
This example shows how to control multiple robots working in a distribution center, as well as how to easily configure a distribution system.

상태 추정

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.

추천 예제

Mapping with Known Poses

Create a map of an environment using range sensor readings and robot poses for a differential drive robot. You create a map from range sensor readings that are simulated using the rangeSensor object. The differentialDriveKinematics motion model simulates driving the robot around the room based on velocity commands. The rangeSensor gives range readings based on the pose of the robot as it follows the path.

라이브 스크립트 열기

Path Planning in Environments of Different Complexity

Demonstrates how to compute an obstacle-free path between two locations on a given map using the Probabilistic Roadmap (PRM) path planner. PRM path planner constructs a roadmap in the free space of a given map using randomly sampled nodes in the free space and connecting them with each other. Once the roadmap has been constructed, you can query for a path from a given start location to a given end location on the map.

라이브 스크립트 열기

Execute Tasks for a Warehouse Robot

Demonstrates how to execute an obstacle-free path for a mobile robot between three locations on a given map. The robot is expected to visit the three locations in a warehouse: a charging station, loading station, and unloading location. The sequence in which these locations are visited is dictated by a scheduler. The scheduler gives each robot a goal pose to navigate to. The robot plans a path and uses a Pure Pursuit controller to follow the waypoints based on the current pose of the robot. The Differential Drive Kinematic Model block models the simplified kinematics, which takes the linear and angular velocities from the Pure Pursuit Controller. This example builds on top of the Simulink에서 차동 구동 로봇의 경로 계획하기 example.

라이브 스크립트 열기

Control and Simulate Multiple Warehouse Robots

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

라이브 스크립트 열기

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.

라이브 스크립트 열기

A* Path Planning and Obstacle Avoidance in a Warehouse

An extension to the Simulate a Mobile Robot in a Warehouse Using Gazebo example. The example shows to change the PRM path planner with an A* planner, and add a vector field histogram (VFH) algorithm to avoid obstacles.

라이브 스크립트 열기

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 DDPG(심층 결정적 정책 경사법) 에이전트 (Reinforcement Learning Toolbox).

라이브 스크립트 열기