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ROSMASTER X3 Plus📖
1.Preparation and First Trial
  • 1.1 Wiring Introduction
  • 1.2 Precautions for battery
  • 1.3 First Trial
  • 1.4 Precautions for DIY Robotic Arm
2. Assembly steps
  • Installation Video
3.Remote control course
  • 3.1 APP remote control
  • 3.2 Model aircraft remote control
  • 3.3 Handle remote control
4.Hardware course
  • 4.1 About expansion board_V3.0
  • 4.1 About expansion board_V1.0
  • 4.2 STM32 Development
  • 4.3 Button to control the buzzer
  • 4.4 FreeRTOS application
  • 4.5 Serial communication
  • 4.6 SBUS model aircraft remote control
  • 4.7 CAN bus communication
  • 4.8 RGB colorful light bar
  • 4.9 Timer interrupt to control
  • 4.10 Control serial port servos
  • 4.11 Nine-axis attitude sensor
  • 4.12 Control motor
  • 4.13 Timer captures the encoder data
  • 4.14 Kinematic Analysis
  • 4.15 PID control robot movement
5.Basic course
  • 5.1 Update the firmware
  • 5.2 Turn off the self-starting process
  • 5.3 Install the Rosmaster driver
  • 5.4 Buzzer whistle
  • 5.5 Control PWM servo
  • 5.6 RGB colorful light bar
  • 5.7 Control motor
  • 5.8 Control robot movement
  • 5.9 Control Serial Servo
6.Linux operating system
  • 6.1 Virtual machine installation
  • 6.2 Linux basics
  • 6.3 Remote control
  • 6.4 Multi-machine communication
  • 6.5 Static IP and hotspot mode
  • 6.6 Bind the device ID
  • 6.7 Web page real-time monitor
  • 6.8 Expansion tutorial
  • 6.9.1 Jetson Nano write sytem
  • 6.9.2 Jetson Xavier NX write system
  • 6.9.3 Jetson TX2 NX write system
  • 6.9.4 Raspberry Pi write system
7.Docker-Just for pi5
  • 7.1 Docker overview and docker installation
  • 7.2 Common commands for docker image containers
  • 7.3 Docker images deeply understand and publish images
  • 7.4 Docker hardware interaction and data processing
  • 7.5 Enter the bot's docker container
  • 7.6 Robot development environment construction in Docker
  • 7.7 How customers can update docker images in the future
8.ROS basic course
  • 8.1 ROS introduction
  • 8.2 ROS installation
  • 8.3 ROS common command tools
  • 8.4 ROS workspace
  • 8.5 ROS function package
  • 8.6 ROS node
  • 8.7 ROS topic publisher
  • 8.8 ROS topic subscribers
  • 8.9 ROS service client
  • 8.10 ROS service server
  • 8.11 ROS action client
  • 8.12 ROS action server
  • 8.13 ROS custom message reception
  • 8.14 ROS-launch file
  • 8.15 ROS-TF transformation
  • 8.16 ROS parameter service
  • 8.17 ROS-rviz use
  • 8.18 ROS-rqt tool usage
  • 8.19 Topic message recording and playback
  • 8.20 urdf model introduction
  • 8.21 gazebo introduction
  • 8.22 ROS distributed communication
9.OpenCV courses
  • 9.1 Getting started with Open Source CV
  • 9.2 Geometric transformation
  • 9.3 Image processing and draw
  • 9.4 Image beautification
  • 9.5 Opencv application
  • 9.6 AR vision
  • 9.7 AR QR code
  • 9.8 ROS+Opencv foundation
  • 9.9 ROS+Opencv application
  • 9.10 Data transformation
  • 9.11 MediaPipe development
10.Robot control course
  • 10.1 PID algorithm theory
  • 10.2 Robot PID debugging
  • 10.3 Robot information release
  • 10.4 Robot keyboard control
  • 10.5 Robot handle control
  • 10.6 Robot state estimation
  • 10.7 Robot calibration
  • 10.8 Robot URDF model
11.Depth camera course
  • 11.1 How to use the Astra camera
  • 11.2 Astra camera calibration
  • 11.3 Astra Color Tracking
  • 11.4 Astra Object Tracking
  • 11.5 Visual tracking autopilot
  • 11.6 Pure visual 2D mapping
  • 11.7 RTAB-Map 3D mapping
  • 11.8 ORB_SLAM2 basics
  • 11.9 ORB_SLAM2_Octomap
12.Lidar course
  • 12.1 Lidar basic-SLAM
  • 12.1 Lidar basic-EAI
  • 12.2 Lidar avoiding
  • 12.3 Lidar guard
  • 12.4 Lidar follow
  • 12.5 Patrol game
  • 12.6 gmapping mapping algoritm
  • 12.7 hector mapping algorithm
  • 12.8 karto mapping algorithm
  • 12.9 cartographer mapping
  • 12.10 rrt_exploration mapping
  • 12.11 AMCL
  • 12.12 Navigation and avoiding
  • 12.13 APP mapping and navigation
13.Multi-robot control course
  • 13.1 Multi-machine handle control
  • 13.2 Multi-machine navigation
  • 13.3 Multi-machine formation
  • 13.4 Multi-machine surround
14.Deep learning courses
  • 14.1 KNN recognizes handwritten
  • 14.2 Basic use of TensorFlow
  • 14.3 Basic use of PyTorch(jetson)
  • 14.4 yolov5 model training(jetson)
  • 14.5 yolov5+tensorrt acceleration
  • 14.6 yolov4-tiny
15.Voice control courses
  • 0. How to use old voice module in new image
  • 15.1 Introduction of voice control-new
  • 15.2 Voice control module port-new
  • 15.3.Introduction of voice module-old
  • 15.4.Voice control module port binding-old
  • 15.5 Voice control car movement
  • 15.6 Voice control Autopilot
  • 15.7 Voice control color recogzition
  • 15.8 Voice control color tracking
  • 15.9 Voice control multi-point nagavation
16.Robotic arm control course
  • 16.1 MoveIt configuration
  • 16.2 MoveIt control the real machine
  • 16.3 MoveIt moves randomly
  • 16.4 MoveIt kinematics design
  • 16.5 MoveIt cartesian path
  • 16.6 MoveIt avoiding
  • 16.7 MoveIt scene design
  • 16.8 MoveIt trajectory planning
  • 16.9 Mediapipe palm control car
  • 16.10 Mediapipe gesture control robotic arm
  • 16.11 Mediapipe gesture control car
  • 16.12 Mediapipe arm attitude control robotic arm
  • 16.13 Mediapipe recognizes and tracks palm control robotic arm
  • 16.14 Recognizes color block and handling
  • 16.15 Autopilot to clear obstacles
17.Voice control robotic arm course
  • 17.1 Voice control robotic arm movement
  • 17.2 Voice control robotic arm learning
  • 17.3 Voice control color block transport
  • 17.4 Voice control robotic arm grip objects
  • 17.5 Voice control robotic arm garbage sorting
18.Model training Autopilot
  • 18.0 ReadMe
  • 18.1 yolov5 to train traffic signs
  • 18.2 TensorRt acceleration identification mark
  • 18.3 Road sign indication function description
  • 18.4 Collect data
  • 18.5 Training model
  • 18.6 Model transformation and application
  • 18.7 AI Autopilot
19.For Orin-Remote control course
1. APP control
  • 1. APP control
  • 2. Model aircraft control
  • 3. Handle control
2. Model aircraft control
3. Handle control
20.Orin-Hardware course
  • 1. About expansion board_V1.0
  • 1. About expansion board_V2.0
  • 2. STM32 Development environment
  • 3. Button control
  • 4. FreeRTOS application
  • 5. Serial communication
  • 6. SBUS model aircraft remote control
  • 7. CAN bus communication
  • 8. RGB colorful light bar
  • 9. Timer interrupt to control PWM servo
  • 10. Control serial port servos
  • 11. Nine-axis attitude sensor to obtain data
  • 12. Control motor
  • 13. Timer captures the encoder data
  • 14. Robot kinematics analysis theory
  • 15. PID control robot movement
21.Orin-Basic course
  • 1. Update the firmware
  • 2. Turn off the self-starting process at startup
  • 3. Install the Rosmaster driver library
  • 4. Buzzer whistle
  • 5. Control PWM servo
  • 6. RGB colorful light bar special effects display
  • 7. Control motor
  • 8. Control robot movement
  • 9. Control Serial Servo
22.Orin-Linux operating systemnew
  • 1. Virtual machine installation and use
  • 2. Linux basics
  • 3. Remote control
  • 4. Multi-machine communication configuration
  • 5. Static IP and hotspot mode
  • 6. Bind the device ID
  • 7. Web page real-time monitoring
  • 8. Expansion tutorial
  • 9.1 Jetson Nano write sytem
  • 9.2 Jetson Xavier NX write system
  • 9.3 Jetson TX2 NX-Orin series write system
  • 9.4 Raspberry Pi write system
  • 10.Jetpack old system upgraded to SUPER system
23.Orin-Dockernew
SUPER image uses ros environment in docker
  • 1、Docker overview and docker installationnew
  • 2、Common commands for docker image containersnew
  • 3、Docker images deeply understand and publish imagesnew
  • 4、Docker hardware interaction and data processingnew
  • 5、Enter the bot's docker containernew
  • 6、Robot development environment construction in Dockernew
23.Orin-ROS basic course
  • 1.ROS introduction
  • 2.Project file structure
  • 3.Common commands and tools
  • 4.Publisher
  • 5.Subscribers
  • 6.Customize topic messages and use
  • 7.Client
  • 8.Server
  • 9.Custom service messages and usage
  • 10.TF release and monitoring
24.Orin-OpenCV courses
  • 1. Getting started with Open Source CV
  • 2. Open Source CV geometric transformation
  • 3. Open Source CV image processing and drawing text line segments
  • 4. Open Source CV image beautification
  • 5. Opencv application
  • 6. AR vision
  • 7. AR QR code
  • 8. ROS+Opencv foundation
  • 9. ROS+Opencv application
  • 10. Data transformation and point cloud
  • 11. MediaPipe development
25.Orin-Robot control course
  • 1.PID algorithm theory
  • 2.Robot PID debugging
  • 3.Robot information release
  • 4.Robot keyboard control
  • 5.Robot handle control
  • 6.Robot state estimation
  • 7.Robot calibration
  • 8.Robot URDF model
26.Orin-Depth camera course
  • 01.How to use the Astra camera
  • 02.Astra camera calibration
  • 03.Astra Color Tracking
  • 04.Astra Object Tracking
  • 05.Visual tracking autopilot
  • 06.Pure visual 2D mapping and navigation
  • 07.RTAB-Map 3D mapping navigation
  • 08.ORB_SLAM2 basics
  • 09.ORB_SLAM2_Octomap
27.Orin-Lidar course
  • 1.Lidar basic-4ROS-EAI
  • 1.Lidar basic-SLAM
  • 2.Lidar avoiding
  • 3.Lidar guard
  • 4.Lidar follow
  • 5.Patrol game
  • 6.gmapping mapping algorithm
  • 7.hector mapping algorithm
  • 8.karto mapping algorithm
  • 9.cartographer mapping algorithm
  • 10.rrt_exploration mapping algorithm
  • 11.AMCL
  • 12.Navigation and avoiding
  • 13.APP mapping and navigation
28.Orin-Multi-robot control course
  • 1.Multi-machine handle control
  • 2.Multi-machine navigation
  • 3.Multi-machine formation
  • 4.Multi-machine surround
29.Orin-Deep learning courses
  • 1.KNN recognizes handwritten digits
  • 2.Basic use of TensorFlow
  • 3.Basic use of PyTorch(jetson)
  • 4.YOLOv11Model conversion(SUPER)new
  • 5.YOLOv11Dataset annotation(SUPER)new
  • 6.YOLOv11Model conversion + tensorrt acceleration(SUPER)new
  • 7.YOLOv11Object recognition(SUPER)new
30.Orin-Voice control courses
  • 0. How to use old voice module in new image
  • 1.1 Introduction of voice module-new
  • 1.2 Introduction of voice module-old
  • 2.1 Voice control module port binding-new
  • 2.2 Voice control module port binding-old
  • 3. Voice control car movement
  • 4. Voice control Autopilot
  • 5. Voice control color recognition
  • 6. Voice control color tracking
  • 7. Voice control multi-point navigation
31.Orin-Robotic arm control course
  • 1.MoveIt configuration(VM)
  • 2.MoveIt control the real machine(VM)
  • 3.MoveIt moves randomly(VM)
  • 4.MoveIt kinematics design(VM)
  • 5.MoveIt cartesian path(VM)
  • 6.MoveIt avoiding(VM)
  • 7.MoveIt scene design(VM)
  • 8.MoveIt trajectory planning(VM)
  • 9.Mediapipe palm control car
  • 10.Mediapipe gesture control robotic arm
  • 11.Mediapipe gesture control car
  • 12.Mediapipe arm attitude control robotic arm
  • 13.Mediapipe recognizes and tracks palm control robotic arm
  • 14.Recognizes color block and handling
  • 15.Autopilot to clear obstacles
32.Orin-Voice control robotic arm course
  • 1.Voice control robotic arm movement
  • 2.Voice control robotic arm learning
  • 3.Voice control color block transport
  • 4.Voice control robotic arm grip objects
  • 5.Voice control robotic arm garbage sorting
LLM tutorial
1.Development environment
  • 0. Model usage instructions
  • 1.Environments deployment
  • 2.Dialogue platform installation
2.Jetson Nano B01
  • 1.Qwen2 model
  • 2.TinyLlama model
  • 3.Phi-3 model
  • 4.Gemma model
  • 5.Orca Mini model
  • 6.StarCoder2 model
  • 7.DeepSeek-R1 model
  • 8.Expand LLaVA-Phi3
3.Jetson Orin NXNANO
  • 1.Llama3 model
  • 2.Qwen2 model
  • 3.Phi-3 model
  • 4.Gemma model
  • 5.WizardLM2 model
  • 6.Codellama model
  • 7.DeepSeek-R1 model
  • 8.Orca Mini model
  • 9.StarCoder2 model
  • 10.TinyLlama model
  • 11.Expand LLaVA
  • 12.Expand LLaVA-Phi3
4.Raspberry Pi 5B
  • 1.Meta AI Llama3 model
  • 2.Qwen2 model
  • 3.Phi-3 model
  • 4.Gemma model
  • 5.WizardLM2 model
  • 6.Meta AI Codellama model
  • 7.DeepSeek-R1 model
  • 8.Expand LLaVA
FAQ
  • Using your own Jetson board, car cannot start normally
Download
  • RemoteCtrl_APP(iOS/Android)
  • Mapping_APP(iOS_Android)
  • SystemFile_OrinNANOSUPER
  • SystemFile_OrinNXSUPER
  • SystemFile_RPi_NANO
  • Instruction Manual
  • Code
  • Autopilot Annex
  • VM software
  • Jetson_Boot_USB.tar.gz
  • Other_Annex_File
Download ZIP
FAQ
  • 1、How to connect OLED?
  • 2、Precautions for DIY Robotic Arm

Welcome to ROSMASTER X3 Plus repository

12.10 rrt_exploration mapping

10. rrt_exploration mapping algorithm

10. rrt_exploration mapping algorithm10.1. Introduction10.2. Use10.2.1. Start10.2.2. Mapping10.2.3. Map saving10.2.4. Node View10.2.5. View tf tree10.3, global_rrt_frontier_detector10.3.1. Introduction10.3.2, Topics and Services10.3.3. Configuration parameters10.4, local_rrt_frontier_detector10.4.1. Introduction10.4.2, Topics and Services10.4.3. Configuration parameters10.5, frontier_opencv_detector10.5.1. Introduction10.5.2, Topics and Services10.5.3. Configuration parameters10.6, filter10.6.1. Introduction10.6.2, Topics and Services10.6.3. Configuration parameters10.7、Assigner10.7.1. Introduction10.7.2, Topics and Services10.7.3. Configuration parameters

rrt_exploration: http://wiki.ros.org/rrt_exploration

rrt_exploration/Tutorials: http://wiki.ros.org/rrt_exploration/Tutorials

map_server: https://wiki.ros.org/map_server

10.1. Introduction

RRT exploration is a search algorithm implemented based on the RRT path planning algorithm. The reason why the RRT algorithm is used is that RRT has a strong tendency for unknown areas. In RRT exploration, RRT is mainly used to generate boundary points, which is very beneficial for exploring boundary points. The so-called boundary point is the intersection point between the explored and unknown areas. Here is a definition: for all areas, if they have been explored, the area without obstacles is recorded as 0, and the area with obstacles is recorded as 1. , the unknown area is recorded as -1, and at the beginning, the entire area is recorded as -1.

The framework of the Rapid Exploration Random Tree (RRT) algorithm is as follows:

  • Global RRT frontier point detector node.

  • Local RRT frontier point detector node.

  • OpenCV-based frontier detector node.

  • Filter node.

  • Assigner node.

image-20220224153344477

There are 3 types of nodes: nodes for detecting boundary points in the occupied raster map, nodes for filtering detected points, and nodes for assigning points to robots.

10.2. Use

Note: When building a map, the slower the speed, the better the effect (note that the rotation speed should be slower). If the speed is too fast, the effect will be poor.

According to different models, you only need to set the purchased model in [.bashrc], X1 (normal four-wheel drive) X3 (Mailun) Take X3 as an example

#Raspberry Pi 5 master needs to enter docker first, please perform this step
#If running the script into docker fails, please refer to ROS/07, Docker tutorial
~/run_docker.sh

Open the [.bashrc] file

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sudo vim .bashrc

Find the [ROBOT_TYPE] parameters and modify the corresponding car model

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export ROBOT_TYPE=X3 # ROBOT_TYPE: X1 X3 X3plus R2 X7

10.2.1. Start

Start the command (robot side). For the convenience of operation, this section takes [mono + laser + yahboomcar] as an example.

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roslaunch yahboomcar_nav laser_bringup.launch # laser + yahboomcar
roslaunch yahboomcar_nav laser_usb_bringup.launch # mono + laser + yahboomcar
roslaunch yahboomcar_nav laser_astrapro_bringup.launch # Astra + laser + yahboomcar

Mapping command (robot side)

<PI5 needs to open another terminal to enter the same docker container

image-20240408144126098

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roslaunch yahboomcar_nav rrt_exploration.launch use_rviz:=false
  • [use_rviz] parameter: whether to enable rviz visualization.

Turn on the visual interface (virtual machine side)

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roslaunch yahboomcar_nav view_rrt_map.launch

image-20220224154939172

The gap at the back of the robot is due to the obstruction caused by the installation position of the display screen, so a certain range of radar data is blocked. The shielding range can be adjusted, or it can not be blocked according to the actual situation. For specific operations, see [01. Radar Basic Course].

10.2.2. Mapping

image-20220519120657718

Click [Publish Point] on the [rviz] interface, and then click on the map. Every time before clicking a point on the map, you must first select [Publish Point]. Click four points clockwise or counterclockwise. The last point is near the car. After selecting five points as required, the robot begins to explore and build the map.

10.2.3. Map saving

After the map construction is completed, the map will be automatically saved and returned to zero point. The map is saved to the ~/yahboomcar_ws/src/yahboomcar_nav/maps folder, the name is rrt_map, and can be modified in the [simple.launch] file.

rrt_map.yaml

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image: map.pgm
resolution: 0.05
origin: [-15.4,-12.2,0.0]
Negate: 0
occupied_thresh: 0.65
free_thresh: 0.196

Parameter analysis:

  • image: The path of the map file, which can be an absolute path or a relative path.

  • resolution: resolution of the map, meters/pixel

  • Origin: 2D pose (x, y, yaw) in the lower left corner of the map. The yaw here is rotated counterclockwise (yaw=0 means no rotation). Many parts of the current system ignore the yaw value.

  • negate: whether to reverse the meaning of white/black and free/occupied (the interpretation of the threshold is not affected)

  • occupied_thresh: Pixels with an occupation probability greater than this threshold will be considered fully occupied.

  • free_thresh: Pixels with occupancy probability less than this threshold will be considered completely free.

10.2.4. Node View

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rqt_graph

image-20220224154939172

10.2.5. View tf tree

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rosrun rqt_tf_tree rqt_tf_tree

image-20220224154939172

10.3, global_rrt_frontier_detector

10.3.1. Introduction

The global_rrt_frontier_detector node takes the occupancy grid and finds boundary points (i.e. exploration targets) in it. It publishes detected points so that filter nodes can process them. In a multi-bot configuration, only one instance of this node is run. If needed, running additional instances of the global boundary detector can increase the speed of boundary point detection.

10.3.2, Topics and Services

Subscription topicTypeDescription
mapnav_msgs/OccupancyGridThe topic name is defined by the ~map_Topic parameter. It is the topic name that this node receives.
clicked_pointgeometry_msgs/PointStampedglobal_rrt_frontier_detector The area to be detected by the node. This topic is where nodes receive five points that define an area. The first four points are the four points that define the square area to be explored, and the last point is the starting point of the tree. After announcing these five points, RRT will start detecting border points.
Post TopicTypeDescription
detected_pointsgeometry_msgs/PointStampedTopic for publishing detected boundary points.
shapesvisualization_msgs/MarkerPublish RRT imaginary line shapes for viewing with Rviz.

10.3.3. Configuration parameters

ParametersTypeDefault valueDescription
~map_topicstring"/robot_1/map"This node receives the map topic mapping name
~etafloat5.0This parameter controls the growth rate of the RRT used to detect boundary points, in meters. This parameter should be set according to the map size, very large values will cause the tree to grow faster and thus detect boundary points faster, but a large growth rate will also mean that the tree will lose small corner points in the map

10.4, local_rrt_frontier_detector

10.4.1. Introduction

This node is similar to global_rrt_frontier_detector. However, it works differently because the tree here is constantly reset every time a boundary point is detected. This node will run along the global boundary detector node, which is responsible for quickly detecting boundary points located near the robot.

In a multi-robot configuration, each robot runs a local detector instance. So, for a team of 3 robots, there will be 4 nodes for detecting boundary points; 3 local detectors and 1 global detector. If needed, running additional instances of the local boundary detector can increase the speed of boundary point detection. All detectors will publish detected boundary points ("/detected_points") on the same topic.

10.4.2, Topics and Services

Subscription topicTypeDescription
mapnav_msgs/OccupancyGridThe name of the map topic subscribed to by this node.
clicked_pointgeometry_msgs/PointStampedSimilar to the global_rrt_frontier_detector node
Post TopicTypeDescription
detected_pointsgeometry_msgs/PointStampedRelease detectionto the topic of boundary points.
shapesvisualization_msgs/MarkerPublish RRT imaginary line shapes for viewing with Rviz.

10.4.3. Configuration parameters

ParametersTypeDefault valueDescription
~/robot_1/base_linkstring"/robot_1/base_link"The frame to connect to the robot. Each time the RRT tree is reset, it will start from the current robot position obtained in this coordinate system.
~map_topicstring"/robot_1/map"This node receives the map topic mapping name
~etafloat5.0This parameter controls the growth rate of the RRT used to detect boundary points, in meters. This parameter should be set according to the map size, very large values will cause the tree to grow faster and thus detect boundary points faster, but a large growth rate will also mean that the tree will lose small corner points in the map

10.5, frontier_opencv_detector

10.5.1. Introduction

This node is another boundary detector, but it is not based on RRT. This node uses OpenCV tools to detect boundary points. It is designed to run standalone, and in a multi-bot configuration, only one instance should be run (running additional instances of this node will not make any difference).

Initially, this node was implemented for comparison with RRT-based boundary detectors. Running this node along RRT detectors (local and global) improves the speed of boundary point detection.

Note: You can run any type and number of detectors, all detectors will be published on the same topic to which the filter node (explained in the next section) is subscribed. The filter, on the other hand, passes the filtered boundary points to the allocator in order to command the robot to explore these points.

10.5.2, Topics and Services

Subscription topicTypeDescription
mapnav_msgs/OccupancyGridThe name of the map topic subscribed to by this node.
Post TopicTypeDescription
detected_pointsgeometry_msgs/PointStampedTopic for publishing detected boundary points.
shapesvisualization_msgs/MarkerPublish RRT imaginary line shapes for viewing with Rviz.

10.5.3. Configuration parameters

ParametersTypeDefault valueDescription
~map_topicstring"/robot_1/map"This node receives the map topic mapping name

10.6, filter

10.6.1. Introduction

This Node node receives detected boundary points from all detectors, filters these points, and passes them to the distribution node to command the robot. Filtering includes deleting old and invalid points, as well as deleting redundant points.

10.6.2, Topics and Services

| Subscription topic | Type | Description | | -------------------------------------------------- -- | -------------------------- | -------------------- ---------------------------------------- | | map | nav_msgs/OccupancyGrid | The topic name is defined by the ~map_Topic parameter. It is the topic name that this node receives. | | robot_x/move_base_node
/global_costmap/costmap | nav_msgs/OccupancyGrid | x is the number of the robot. This node subscribes to the topic of all costmaps for all robots and therefore requires a costmap. Normally, the cost map should be published by navigation (after turning navigation on the robot, each robot will have a cost map). For example, if n_robots=2, the nodes will subscribe to: robot_1/move_base_node/global_costmap/costmap and robot_2/move_base_node/global_costmap/costmap. The cost map is used to remove invalid points.
Note: The namespace of all nodes corresponding to robots should start with robot_x. | | detected_points | geometry_msgs/PointStamped | Topic name defined by ~goals_topic. It is the topic where the filter node receives the boundary detection point. | | Post Topic | Type | Description | | frontiers | visualization_msgs/Marker | The filter node publishes only the topics of filtered boundary points. | | centroids | visualization_msgs/Marker | The filter node publishes the topic of the received boundary points. | | filtered_points | MsgLink(msg/type) | All filtered points are sent to the assigner node of this topic as a point array. |

10.6.3. Configuration parameters

ParametersTypeDefault valueDescription
~map_topicstring"/robot_1/map"This node receives the map topic mapping name
~costmap_clearing_thresholdfloat70.0Costmap clearing threshold
~info_radiusfloat1.0The information radius used to calculate the information gain of boundary points.
~goals_topicstring/detected_pointsDefines the topic for nodes to receive detected boundary points
~n_robotsfloat1.0Number of robots
~namespacestring namespace
~namespace_init_countfloat1.0Namespace index
~ratefloat100.0Node cycle rate in Hz.

10.7、Assigner

10.7.1. Introduction

This node receives target detection targets, i.e. filtered boundary points published by the filtering node, and commands the robot accordingly. The evaluator node commands the robot through move_base_node. This is why navigation is enabled on the robot.

10.7.2, Topics and Services

Subscription topicTypeDescription
mapnav_msgs/OccupancyGridThe topic name is defined by the ~map_Topic parameter. It is the topic name that this node receives.
frontiers_topicnav_msgs/OccupancyGridThe topic name is defined by the ~frontiers_topic parameter

10.7.3. Configuration parameters

ParametersTypeDefault valueDescription
~map_topicstring"/robot_1/map"This node receives the map topic mapping name
~info_radiusfloat1.0The information radius used to calculate the information gain of boundary points.
~info_multiplierfloat3.0Units are meters. This parameter is used to emphasize the importance of the information gain of a boundary point relative to the cost (expected travel distance to the boundary point).
~hysteresis_radiusfloat3.0Units are meters. This parameter defines the hysteresis radius.
~hysteresis_gainfloat2.0Units are meters. This parameter defines the hysteresis gain.
~frontiers_topicstring/filtered_pointsThis node receives the topic of the frontier points.
~n_robotsfloat1.0Number of robots
~namespacestring namespace
~namespace_init_countfloat1.0Starting indexing of robot names.
~delay_after_assignementfloat0.5The unit is seconds. It defines the amount of delay after each robot allocation.
~global_framestring"/map"For global coordinate system. In a single robot, it is the same as the "map_topic" parameter. In the case of multiple robots, the coordinate system name corresponds to the global coordinate system name.
cartographer mapping
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