AGLIEX ROBOTICS D435 SCOUT MINI Research and Development Kit Pro User Guide

User Guide for AGLIEX ROBOTICS models including: D435 SCOUT MINI Research and Development Kit Pro, D435, SCOUT MINI Research and Development Kit Pro, Research and Development Kit Pro, Development Kit Pro, Kit Pro

SCOUT MINI Research & Development Kit Pro

5 mars 2024 — The SCOUT MINI Research & Development Kit Pro is a ROS development kit designed and developed by Agliex Robotics for entry-level and advanced ROS developers ...70 pages

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scout-mini-pro-user-manual

SCOUT MINI Research & Development Kit Pro

0 Product Overview
The SCOUT MINI Research & Development Kit Pro is a ROS development kit designed and developed by Agliex Robotics for entry-level and advanced ROS developers in the field of scientific research and education. It comes equipped with high-performance industrial control, high-precision LiDAR, and multiple sensors based on the Agliex Robot ROS ecosystem. These features enable it to perform various functions such as mobile robot motion control, communication, navigation, and map building, among others. In addition to its technical capabilities, this kit is also lightweight, portable, and boasts a highly technological industrial design. It even includes an exclusive sensor customized holder. With complete developer documentation and DEMO resources, it serves as the ideal experimental platform for rapid secondary development of ROS for multi-directional applications like education and scientific research, product pre-research, subjects, and product demonstration.

1 Configuration List and Parameters 1.1 Shipping List

1.1.1 SCOUT MINI Pro
Name IPC
Laser Sensor Vision Sensor
Router Chassis Remote Controller Charger Voltage Stabilizer Voltage Stabilizer HD Display USB To CAN USB HUB

Quantity 1 1 1 1 1 1 1 1 1 1 1 1

Model Nvidia AGX Xavier
RS-Helios-16P Intel RealSense D435
GL.iNet GL-A1300 SCOUT MINI(off-road)
FS-I6S Agilex UY360 24V To 12V
12V To 5V

SCOUT MINI Pro Main configuration

1.2 SCOUT MINI Introduction
1.2.1 SCOUT MINI
The SCOUT MINI mobile chassis uses 4WD design with powerful off-road performance, compact design and truly 'smart like a swallow, galloping like a heart'. This innovative design inherits the advantages of the 4WD differential chassis series from SCOUT, which features independent suspension and zero turning radius. The hub motor has also been improved upon in this model.
With a minimum turning radius of 0m and a climbing angle almost reaching 30 degrees, SCOUT MINI performs exceptionally well off-road despite being 50% smaller than the original SCOUT. Its power control system offers precise, stable, and controllable power, allowing it to achieve high speeds of up to 10.8km/h.
The SCOUT MINI development platform comes equipped with its own control system that supports standard CAN protocol and can connect to various external devices. It also supports ROS/Autoware secondary development and advanced robotics development. Standard accessories include an activation plug, 24V@15Ah lithium battery, and an endurance mileage of up to 10KM.

1.2.2 Get started quickly with Scout Mini
1.2.2.1 SCOUT MINI Checking
Press power button and wait for a few seconds Check the display to see if the power is lower than 30%. If it is lower than 30%,
please charge it. If the battery level is less than 30%, please use the standard charger to charge
the vehicle. Turn off the power of the vehicle during charging. It takes about 1 and a half hours to charge from low battery to full power.
1.2.2.2 Remote Control
Check whether the battery of remote controller has been installed. Move the joysticks SWA, SWB, SWC, and SWD to the top. Press and hold the power switch buttons 1 and 2 at the same time until the
power is turned on.
1.2.2.3 Remote Control Operation
The remote control has preset switches default setting. Please do not change the switches setting. Any changing may cause control failure. SWB switch the control mode, the SWC is controlling the light on and off while SWD controls the speed mode. The left joystick controls the Scout mini move forward and backward while the right joystick controls the rotation. Please note that the internal mapping motion of the chassis is mapped based on percentage, so when the joysticks are at the same position, the speed is constant.
SWB is the control mode switching button. The remote controller joystick is at the top for command mode, at the middle for remote control mode, and at the bottom for constant speed mode.
SWC is the light controlling button. When it is at the bottom position, it is closed, when it is in the middle, the light is open, and the top position is the breathing light

mode. Please note that the lighting control option setting is only valid under the remote controller mode. SWD is the speed gear selection mode, the up position is the low gear mode (the fastest speed is about 10km/h), and bottom position is the high gear (the fastest speed is about 20km/h). Please note that the gear setting button is only valid under the remote control mode.
1.2.2.4 SCOUT MINI Preparation
Please use SCOUT MINI in a relatively open area for the first time to avoid any inappropriate operation and damage.
Press the SCOUT MINI power button and wait a few seconds. Set SWB to the middle position. Try to switch the light mode manually to make sure that the mode is selected
correctly. Try to gently push the left joystick forward, you can see that the car is moving
forward slowly. Try to gently push the left joystick back, you can see that the car is moving
backwards slowly. Release the left joystick and the vehicle will stop. Try to gently push the right joystick to the left, you can see that the car slowly
rotates to the left. Try to gently push the right joystick to the right, you can see that the car slowly
rotates to the right. Release the right joystick and the vehicle will stop Then try to control the vehicle in the relatively open area and be familiar with the
speed control of the vehicle.
1.2.2.5 Turn off SCOUT MINI
Press SCOUT MINI power button and then release.
1.2.2.6 Turn off Remote Controller
Hold the power button 1 and 2 at the same time for few seconds until turned off.

1.2.3 SCOUT MINI Parameters

SCOUT MINI Parameters Description

Size

627mm × 550mm × 252mm

Wheelbase

452mm

Track width

450mm

Weight

20kg

Minimum

107mm

Ground

Clearance

Standard Load 10kg(Tested on the ground, the friction coefficient is

0.5)

Maximum

20km/h

Speed

Minimum

0(Can rotate in place)

Turning Radius

Maximum

30°(With load)

climbing angle

Obstacle

70mm

avoiding

Maximum

10km(Without load)

mileage

Driver mode

4WD

Temperature

-20~60

Charger

AC 220

Charging time 1.5H

Voltage Output 24V

Battery

24V/15Ah

Code Wheel

1024 Photoelectric Incremental

Communication Standard CAN

interface

IP protection

IP22(IP64 for customization)

Suspension

Independent swing arm suspension

1.3 Nvidia Xavier Introduction
NVIDIA® Jetson AGX XavierTM sets a new standard for compute density, energy efficiency, and AI inference capabilities in edge devices. This cutting-edge technology represents the next evolution of intelligent machines with end-to-end autonomous capabilities. Despite being only 100 x 87 mm, the Jetson AGX Xavier delivers the same performance as a larger workstation while being one-tenth the size. This makes it the perfect solution for

autonomous machines such as delivery and logistics robots, factory systems, and large industrial UAVs.

As the first computer designed specifically for autonomous machines, Jetson AGX Xavier is powerful enough to perform complex tasks such as visual odometry, sensor fusion, localization and mapping, obstacle detection, and route planning algorithms that are crucial for next-generation robots. It offers GPU workstation-class performance in a compact form factor, featuring 32 teraflops of peak computing power and 750 Gbps of high-speed I/O performance.

Jetson AGX Xavier offers new levels of compute density, energy efficiency, and AI inference capabilities at the edge. Users can configure their applications for 10-watt, 15-watt, or 30-watt operating modes using the Jetson AGX Xavier module.

GPU CPU Memory Storage PCIe X16 RJ45
USB-C
Camera Interface M.2 Key M M.2 Key E 40 Pin Header HD Audio connector eSTATp + USB 3.0 Type A HDMI Type A µSD/UFS

Developer Kit Technical Specifications Tensor Core 512 -Volta GPU 8-core ARM v8.2 64-bit CPU, 8 MB L2 + 4 MB L3 32 GB 256-bit LPDDR4x | 137 GB/sec 32 GB eMMC 5.1 X8 PCIe Gen4/x8 SLVS-EC Gigabit Ethernet 2 USB 3.1 interfaces, DP interface (optional), PD interface (optional). Supports closed system debugging and programming through the same port. 16 *CSI-2 channels NVMe PCIe x1 + USB 2.0 + UART (for Wi-Fi/LTE) / I2S + DMIC + GPIOs UART + SPI + CAN + I2 C + I2 S + DMIC + GPIOs
HD Audio
SATA interface with PCIe x1 bridge + USB 3.0 Data for 2.5-inch SATA interface HDMI 2.0 SD/UFS

1.4 Intel RealSense D435
Binocular vision sensors play a critical role in various applications within the field of robot visual measurement, visual navigation, and other areas of the robotics industry. At present, we have carefully chosen visual sensors that are widely used in scientific research and education. Among them, the Intel RealSense Depth Camera D435 stands out with its global image shutter and expansive field of view, enabling efficient capture and streaming of depth data for moving objects. This camera delivers exceptionally precise depth perception, making it an excellent choice for mobile prototypes.

Features Depth Camera

Item Scenes Measure Distance
Type of Depth Shutter
IMU Support Depth Camera FOV Depth Frame Rate Depth Resolution Maximum Measurement Distance

Intel Realsense D435 Indoor/Outdoor
About 10 m
Global Shutter, 3um X 3um pixel size
No Active Infrared 86° x 57°±3° 0.105m 1280 x 720 About 10 m

RGB Others

Depth Frame Rate Resolution FOV Frame Size Interface Type

90 fps 1280 x 800 69.4° × 42.5°±3° 30fps 90mm x 25mm x 25mm USB-C 3.1

1.5 IMUOptional

The CH10X series is an attitude-sensing system that uses high-performance, small-volume MEMS inertial devices to sense object attitude information. It integrates an inertial measurement unit (IMU), a magnetometer, and a microcontroller equipped with an extended Kalman fusion algorithm (EKF) device. It can output three-dimensional orientation data based on local geographical coordinates calculated by the sensor fusion algorithm, including absolute reference heading angle, pitch angle, and roll angle. Calibrated raw sensor data can also be output. The IP68 waterproof shell-type package can be easily integrated into the user's system.

Attitude angle output accuracy Attitude angle Roll angle/pitch angle-static error Roll angle/pitch angle-dynamic error Heading and angle accuracy during movement

Typical Value 0.8° 2.8° 3°

Gyroscope Parameters Measuring range Bias stability Scale non-linearity Noise density Acceleration sensitivity

Value ±2000°/s 10°/h ±0.1% (full range) 0.08 0.001°/s/g

Accelerometer Parameters Measuring range Bias stability Non-linearity Noise density

Value ±8G (1G=1x gravity acceleration) 30mG ±0.5% (full range) 120

Magnetic sensor parameters Parameters Measuring range Non-linearity Resolution

Value ±8GGauss ±0.1% 0.25mG

1.6 RS-Helios-16P

RS-Helios-16P is a 16-line lidar mass-produced by RoboSense. It is the first domestic and world-leading small lidar. It is mainly used for autonomous driving vehicle environment perception, and robot environment perception, human-machine surveying, and mapping. RS-Helios-16P has 16 built-in laser components. It emits and receives high-frequency laser beams at the same time. Through 360 ° rotation, it performs real-time 3D imaging and provides accurate three-dimensional space point cloud data and object reflectivity, allowing the machine to obtain reliable Environmental information and provide a strong guarantee for positioning, navigation, obstacle avoidance, etc.

Item Range Range Accuracy Points Per Second
Field of View (Vertical) Vertical Resolution Frame Rate

Parameters 0.2m~150m(110m@10% NIST) ±2cm
288,000pts/s(Single Return) 576,000pts/s(Dual Return) -15°~15° 2° 10Hz/ 20 Hz

Laser Safety Classification Weight(Without cabling) Power Consumption Voltage Operating Temperature

Class 1 ~1.0kg 11W 9V~32V -30~60

2 Instructions for Hardware Installation and Electricity
2.1 SCOUT MINI Pro Kit Installation
2.1.2 SCOUT MINI Pro
The SCOUT MINI Pro scientific research and education kit bracket features a stacked design, providing a well-organized structure. The frame utilizes sheet metal for support, allowing customers to easily expand connections. A high-definition display is conveniently positioned at the rear for customer debugging purposes. The bottom section incorporates a hollow sheet metal bracket, which is assembled with the standard

aluminum profile bracket on top of the SCOUT MINI. The computing unit of the SCOUT MINI Pro is located on the first floor. The second layer primarily consists of the USB-HUB expansion interface, USB to CAN module, and Intel RealSense D435. The third layer houses the voltage stabilizing module, while the top layer currently accommodates a VLP 16 lidar. For specific installation locations and schematic diagrams, please refer to the figure provided below.
2.2 Electricity and Communication Connection
2.2.2 SCOUT MINI Pro
SCOUT MINI Pro provides power and communication interfaces for upper equipment through the aviation expansion interface of the chassis. The power supply of the SCOUT MINI chassis power expansion interface (maximum power output supports 24V@5A) comes from the battery of the chassis. Without voltage stabilization and voltage regulation modules, in order to power other expansion equipment, it is necessary to add voltage stabilization and voltage regulation equipment. To facilitate later use, we chose 19V and 12 V voltage stabilizing modules. The 19V voltage stabilizing module mainly provides power input for Nvidia AGX Xavier, and the 12V voltage stabilizing module provides power input for VLP-16 lidar, USB HUB, and wireless routers.

The external topological connection of Nvidia AGX Xavier, serving as the core computing unit, is as follows. The network port of Xavier is connected to the router's network port, enabling remote desktop connection, access, and debugging. This setup also allows for the expansion of other network devices. Additionally, an externally added USB-HUB with independent external power supply is incorporated. The USB-HUB facilitates the connection of D435 binocular camera, LCD screen, and other peripherals.
2.3 Sensor Expansion
External expansion mainly involves mechanical installation, power supply, and communication expansion. In terms of power supply expansion, we carefully considered this issue during the early stage of selecting the power supply voltage stabilizing module. As a result, the module has a certain margin reserved to accommodate potential power needs. For communication expansion, we added a USB-HUB and wireless gateway to the device to enable access to more devices.
3 Development Guide
3.1 ROS Development Introduction
3.1.1 ROS History
The rapid advancement of hardware technology has not only facilitated the progress and

complexity of the robotics field but also presented significant challenges to the software development of robotic systems. With an increasing abundance of robot platforms and hardware devices, there is a growing demand for software code reusability and modularity. However, existing robot systems often fail to meet these requirements adequately. In comparison to hardware development, software development in this field has been comparatively insufficient.
To address the significant challenges faced by robot software development, developers and research institutions worldwide have devoted resources to the creation of universal software frameworks for robots. In recent years, several remarkable robot software frameworks have emerged, greatly facilitating software development efforts. Among these frameworks, the Robot Operating System (ROS) stands out as one of the most exceptional.
ROS is a flexible framework designed for the creation of robot software. It seamlessly integrates numerous tools, libraries, and protocols, offering functionalities akin to those provided by operating systems. These functionalities include hardware abstraction description, management of underlying drivers, execution of shared functions, inter-program message passing, and program release package management. ROS effectively simplifies the creation of complex tasks and enables stable behavior control across diverse and intricate robot platforms.
3.1.2 ROS Concept
The concept of ROS includes three layers: the file system layer, computing layer, and community layer. The primary objective of ROS is to enhance software reusability in robot research and development. To achieve this goal, ROS adopts a distributed structure, enabling each functional module within the framework to be designed, compiled, and executed independently, while being loosely coupled with other modules.
ROS primarily offers functionalities and mechanisms such as hardware abstraction, underlying drivers, message passing, program management, and application prototypes for robot development. It also integrates various third-party tools and library files to assist users in swiftly establishing, writing, and integrating robot applications across multiple machines. Moreover, the functional modules within ROS are encapsulated within independent function packages (Packages) or meta-function packages (Meta Packages), facilitating sharing and distribution within the community.
3.1.3 ROS Node
A node represents a single process running the ROS graph. Every node has a name, which it registers with the ROS master before it can take any other actions. Multiple nodes with different names can exist under different namespaces, or a node can be defined as anonymous, in which case it will randomly generate an additional identifier to add to its

given name. Nodes are at the center of ROS programming, as most ROS client code is in the form of a ROS node which takes actions based on information received from other nodes, sends information to other nodes, or sends and receives requests for actions to and from other nodes.
3.1.4 ROS Message
The most important communication mechanism between nodes is message communication based on the publish/subscribe model. Each message is a strict data structure, supporting standard data types (integer, floating point, Boolean, etc.), and also supports nested structures and arrays (similar to the structure struct in C language). It can also be configured based on Requirements are defined by developers themselves. 3.1.4.1 ROS Topic Messages are delivered in a publish/subscribe manner. A node can publish messages for a given topic (called a publisher/Talker), or it can pay attention to a topic and subscribe to specific types of data (called a subscriber/Listener). Publishers and subscribers do not know each other's existence. There may be multiple nodes in the system publishing or subscribing to messages on the same topic at the same time.
3.1.4.2 ROS Service Although the topic-based publish/subscribe model is a very flexible communication model, it is not suitable for the two-way synchronous transmission model. In ROS, we call this

synchronous transmission mode Service, which is based on the Client/Server model and contains two parts of communication data types: one for requests and the other for responses. Similar to a web server. Unlike topics, only one node in ROS is allowed to provide a specified named service.
3.1.4.3 ROS File System
Similar to an operating system, ROS organizes all files according to certain rules, and files with different functions are placed in different folders. Function package (Package): Function package is the basic unit in ROS software, including ROS nodes, libraries, configuration files, etc. Package Manifest: Each function package contains a package manifest named package.xml, which is used to record the basic information of the function package, including author information, license information, dependency options, compilation flags, etc. Meta Package: In the new version of ROS, the concept of the original function package set (Stack) is upgraded to a "meta function package", whose main function is to organize multiple function packages for the same purpose. For example, a ROS navigation meta-function package will include multiple functional packages such as modeling, positioning, and navigation. Meta-function package list: Similar to the function package list, the difference is that the meta-function package list may include function packages that need to be depended on at runtime or declare some reference tags.
3.2 Start up and shut down
3.2.1 Installation
3.2.1.1 Tool
Set of hexagonal tools.
3.2.1.2 Sensors holder and mobile base
SCOUT MINI Pro
When the product is ready for shipping, the sensor holder is separated from the Scout MINI. User need to use tool to set the sensor holder on the Scout MINI. Firstly, put the four slider nuts into the slide way each side on the Scout MINI , and then use the hexagonal tool to screw corresponding four screws on the holder to the platform. Please refer to the figure shown below.

3.2.1.3 HD Display Installation
SCOUT MINI Pro
Put the HD display into the display holder horizontally, as shown in the figure below. Then settle the display holder with screws and nylon posts to the holes on both sides.

3.2.2 Checking before start up
Check whether any wiring harness connections are disconnected; Please make sure to operate in a relatively open area without large area of water .
The environment is relatively open and stable, there are no flammable, explosive and other dangerous goods around The whole machine is complete, the wiring harness is intact and there is no break, and the sensors are not damaged.
3.2.3 Mouse and keyboard
This Kit is not provided with mouse and keyboard, users can purchase it based on requirement, and can be connected with the USB interface of the computing unit or the USB HUB.
3.2.4 Power on
3.2.4.2 SCOUT MINI Pro Press the power button on the Scout MINI, as shown in the figure below.
3.2.5 Computing Unit Login
After pressing the power button of the Scout MINI , the computing unit will automatically log in and show the following interface, the root authority password and system login password are agx .

3.2.6 Shut Down
If you need to shut down he system, do not press the power button directly because thel computing unit is running. Enter the command $poweroff in the terminal or click shut down of the drop-down menu, as shown in the figure below, wait until the display shows no signal input, then press the power button to shut down the system.

3.3 SCOUT MINI Pro Development Environment

UbuntuROSGazeboRVIZ

Ubuntu

18.04

ROS

melodic

Gazebo

9.0

RVIZ

It has passed the test and can be used normally under the above environment.

3.4 Remote Desktop

3.4.1 Download the corresponding operating system remote

desktop software on the host computer
Open the wireless network settings of the host computer, it shows that there are two signals from the router GL-AR750(respectively), please connect to the 5G frequency band, the default password is goodlife

3.4.2 Xavier connection (make sure the computer unit is power on)
3.4.2.1 Choose connection object

3.4.2.2 Click Yes 3.4.2.3 Usernameagilex Passwordagx Choose to save the password. 3.4.2.4 Keep clicking the default OK

3.4.2.5 Enter the default password agx

3.4.2.6 Successful connection
3.4.3 Network port to connect to remote desktop
3.4.3.1 Enter the router, the default address is 192.168.8.1. Select the language and set the password Note: After resetting the router, the default IP address is 192.168.8.1. Enter, 192.168.8.1 in the browser, select the language, and set the password. After the test platform is produced, the router password is unified: 12345678, and the IP address is 192.168.1.1

3.4.3.2 Click USE as LAN port
3.4.3.3 Successfully set to LAN port mode, now you can connect the Kit with the host computer through a network cable for access

3.5 ROS Installation
Install Source
sudo sh -c 'echo "deb http://packages.ros.org/ros/ubuntu $(lsb_release -sc) main" > /etc/apt/sources.list.d/ros-latest.list'
or source from China
sudo sh -c '. /etc/lsb-release && echo "deb http://mirrors.ustc.edu.cn/ros/ubuntu/ $DISTRIB_CODENAME main" > /etc/apt/sources.list.d/ros-latest.list'
Set key
sudo apt-key adv --keyserver 'hkp://keyserver.ubuntu.com:80' --recv-key C1CF6E31E6B ADE8868B172B4F42ED6FBAB17C654
Update
sudo apt-get update
ROS Desktop-Full Installation Ubuntu16.04
sudo apt-get install ros-kinetic-desktop-full

Ubuntu18.04
sudo apt-get install ros-melodic-desktop-full
Resolve dependencies
sudo rosdep in rosdep update
Environment Setup Ubuntu16.04
echo "source /opt/ros/kinetic/setup.bash" >> ~/.bashrc source ~/.bashrc
Ubuntu18.04
echo "source /opt/ros/melodic/setup.bash" >> ~/.bashrc source ~/.bashrc
Install rosinstalla convenient tool.
sudo apt install python-rosinstall python-rosinstall-generator python-wstool buildessential
3.6 Sensors base node
3.6.1 USB To CAN Drive installation and testing
Enable gs _usb.
sudo modprobe gs_usb
Open CAN port and set the baud rate.
sudo ip link set can0 up type can bitrate 500000
If no errors occurred in the previous steps, you should now be able to view the CAN by using command.
ifconfig -a

Testing the hardware by using can-utils.
sudo apt install can-utils
Or run the script:
rosrun scout_bringup setup_can2usb.bash
Testing the commands.
$ candump can0
3.6.2 SCOUT MINI ROS Package
scout_bringup: Start up the mobile base node. scout_base: Control and monitor package based on ugv_sdk. scout_description: scout_min URDF model, available for simulation of customized robots equipped with sensors . scout_msgs: scout_mini message format definition.

3.6.3 IMU sensor(Optional)
imu_launch: launch imu function package
roslaunch imu_launch imu_msg.launch
To view IMU data, enter in the terminal:
rostopic echo /imu/data_raw

3.6.3 RealSense D435 node ROS Package and RVIZ visualization
Realsense2_camera: Set up and start the depth camera function package, and you can use the rviz visualization tool to view color maps, depth maps, dense point clouds, etc.
Get the rgb
roslaunch realsense2_camera rs_camera.launch
Enter rviz in the terminal to open rviz tool, click the add to add image component.

Fixed frame: choose camera_link.

Fills in the corresponding topic in image component to get the rgb picture.
Get the rgb + Depth map
roslaunch realsense2_camera rs_camera.launch
Open rviz, click the add to add DepthCloud component.

Fixed frame : choose camera_link, choose the corresponding topic in the Depth Map topic. The depth map is shown below:

3.6.5 RS-Helios-16P
rslidar_sdk: driver package for lidar. 3.6.5.1 Set the industrial computer IP Open the browser and enter in the browser search bar:
192.168.1.1
Enter the router management web page. The password is uniformly set to 12345678. Note: If the router has been reset, please enter 192.168.8.1 in the browser.
Enter the router management interface and click More Settings on the left.

Continue to select the LAN IP and change the LAN IP to 192.168.1.1
Then open computer settings and select Network.
Select the network to connect to, usually the third one, then click options to enter the network settings

Follow these steps Select IPv4; click the subscript on the right side of Method and select Manual; click add and enter 192.168.1.102 in Address. Enter 24 in Netmask, enter 192.168.1.1 in Gateway, and click save to save. The final effect is as follows:

3.6.5.2 Start RS-Helios-16P Start the lidar and publish the coordinate transformation of base_link-><laser_link>
roslaunch scout_bringup open_rslidar.launch
3.7 Navigation and positioning based on Gmapping open source architecture
3.7.1 Autonomous Navigation Practice of Lidar
The RS-Helios-16P radar is a 16-beam laser radar with a 2° increment, covering 15° in both the upward and downward directions. As the RS-Helios-16P directly outputs point cloud data, it includes laser data from all 16 beams. Gmapping mapping only requires data from one beam, so it is necessary to compress the point cloud data. At this point, you can use the ROS package provided by VLP to compress and output point cloud data to /scan. Alternatively, you can utilize the pointcloud_to_laserscan package that we provide for compressing point cloud data. Due to the scanning angles of 3D laser radar, the scanning blind spots are fewer compared to 2D radar. Once the scan data is obtained, it can be fed to Gmapping for mapping. Provide a relatively reliable odometer information and correct coordinate transformations, and Gmapping mapping can be performed. 3.7.1.1 Autonomous navigation practice of lidar Slam mapping practice:

(1) Start the lidar and publish the coordinate transformation of base_link-><laser_link>
roslaunch scout_bringup open_rslidar.launch
2Launch gmapping node Gmapping Algorithm:
roslaunch scout_bringup gmapping.launch
Use the joytick to control the robot to move around the scene. After building the map, save the map to the specified directory (usually to `description'). The name of this map is `map'. Please be careful not to repeat the map name.
rosrun map_server map_saver f ~/catkin_ws/src/scout_base/scout_description/maps/map

NoteThe last `map' is the map name. Any English characters can be used. Cartographer Algorithm
roslaunch scout_bringup scout_cartographer.launch
Use the joytick to control the robot to move around the scene. After building the map, save the map to the specified directory (usually to `description'). The name of this map is `map'. Please be careful not to repeat the map name.
rosrun map_server map_saver -f ~/catkin_ws/src/scout_base/scout_description/maps/map
Autonomous navigation practice: (1) Start the lidar and publish the coordinate transformation of base_link-> <laser_link>.
roslaunch scout_bringup open_rslidar.launch
2Start move_base navigation.
roslaunch scout_bringup navigation_4wd.launch
Note: If you need to customize the opened map, please open the navigation.launch file to modify the parameters, as shown in the figure below. Please modify the marked horizontal line to the name of the map you want to open.

3Correct the actual position of the chassis in the scene on the map displayed in rviz by publishing an approximate position and use a joystick to correct the chassis rotation. Correction is completed when the laser shape overlaps the scene shape in the map.
After setting the positioning, the laser shape and the scene shape in the map have basically overlapped, and the correction is completed. As shown below

4Set the target point on the multi-point navigation plug-in on the left column, click to start navigation, and switch the controller to command control mode.
The target point is generated, as the picture shown below.

Click to start navigation, the map has generated a path (in green), and it will automatically navigate to the target point.
3.8 Navigation and positioning on vision and laser
3.8.1 rtamap algorithm introduction The rtabmap algorithm provides an appearance-based positioning and composition solution that is independent of time and scale. Aimed at solving online closed-loop detection problems in large environments. The idea of the solution is that in order to meet some limitations of real-time performance, closed-loop detection only uses a limited

number of anchor points, and at the same time can access the anchor points of the entire map when needed. 3.8.2 rtabmap mapping algorithm (1) Launch the radar. Enter the command in the terminal:
roslaunch scout_bringup open_rslidar.launch
(2) Launch the realsense. Enter the command in the terminal:
roslaunch realsense2_camera rs_aligned_depth.launch
(3) Launch the rtamap algorithm mapping mode. Enter the command in the terminal:
roslaunch scout_bringup rtabmapping.launch
When the rviz interface shown is in the picture below, the rtbmap mapping mode starts successfully.
After building the map, you can terminate the program directly and the built map will be automatically saved in the .ros file in the home directory with the file name rtabmap.db. The .ros folder is a hidden folder and needs to be displayed through the Ctrl+h command.

3.8.3 rtabmap navigation algorithm (1) Launch the LiDAR. Enter the command in the terminal:
roslaunch scout_bringup open_rslidar.launch
(2) Launch the realsense. Enter the command in the terminal.
roslaunch realsense2_camera rs_aligned_depth.launch
(3) Launch the mapping mode. Enter the command in the terminal:
roslaunch scout_bringup rtabmapping.launch localization:=true
(4) Launch move_base. Enter the command in the terminal:
roslaunch scout_bringup scout_rtab_navigation.launch
Because we use visual positioning, there is no need to make corrections when using rtabmap navigation. You can directly start setting the target point for navigation. The operation steps are shown in the figure. Set the target point on the multi-point navigation plug-in on the left column, click to start navigation, and switch the handle to command control mode.
The target has been generated.

Click to start navigation. The map has generated a path (green) and will automatically navigate to the target point.

3.9 Visual Application
3.9.1 Traffic lights identification
3.9.1.1 Function Introduction After detecting the traffic light target through darknet_ros, it is necessary to identify the traffic light color and position it in the three-dimensional space to generate the positional relationship between the object and the camera. This method can only achieve the identification and positioning of traffic lights, but cannot obtain the traffic light posture. A depth camera is required, and its recognition distance depends on the depth camera range. 3.9.1.2 Operation Launch the realense depth camera. Enter the command in the terminal:
roslaunch realsense2_camera rs_camera.launch
Launch the yolo_v3. Enter the command in the terminal:
roslaunch darknet_ros yolo_v3_tiny.launch
Launch the traffic lights identification function:
roslaunch scout_deeplearning traffic_light_located.launch
3.9.2 QR code Tracking
3.9.2.1 Function Introduction After QR code recognition through ar_track_alvar, the location information of the QR code is obtained and the TF relationship between the QR code and the camera is generated.
3.9.2.2 Operation Launch realsense depth camera. Enter the command in the terminal:
roslaunch realsense2_camera rs_camera.launch
Launch the QR code identification. Enter the command in the terminal:

roslaunch scout_visions ar_code.launch
Launch the tracking function. Enter the command in the terminal:
roslaunch scout_visions ar_control.launch

Common problems
1. CAN0 cannot be recognized
1) It may be a problem with the USB interface on the computer and needs to be replaced. 2) Or maybe there are no gs_usb drivers. Take the TX2 for example and add the gs_usb module.
git clone https://github.com/HubertD/socketcan_gs_usb.git
cd socketcan_gs_usb
make
If there is error during `make', please recompile the kernel.
cd /lib/modules/4.4.38-tegra/
cd build
sudo make
Go back to the socketcan_gs_usb directory, execute make, and add the gs_usb.ko file after successful compilation.
sudo modprobe can_dev can insmod gs_usb.ko
ifconfig -a
There is one more can2, added successfully!
2. There is no data returning back after executing candump can0
1) There may be a problem with the microUSB cable and need to replace it with a new one. 2) The high and low connections of the CAN are reversed. The yellow one should be connected to can_H and the blue one should be can_L. If the connection is reversed, it needs to be replaced. 3) Virtual machine. If you are using a virtual machine, you will not be able to receive data.

4) USB to CAN module fail. 5) gs_usb module is not enabled.
3. If `device or resource busy' occurs
It means that it may be enabled repeatedly. In this case, you need to unplug and plug the USB port again to solve the problem.
4. Recognized as other serial number. For example: can2
You can change all `can0' in the code to `can2'
5. If `Network is down' occurs
The Can may be not enabled. Execute the following command:
rosrun scout_bringup setup_can2usb.bash
6. The asio.hpp problem
Execute the following command:
sudo apt-get install libasio-dev
7. `pcl_ros' occurs when compiling
Execute the following command:
sudo apt-get install ros-melodic-pcl-ros
8. Errors occur when launching LiDAR
1. ERROR"base_link" passed to lookupTransform argument source_frame does not exist. The error is caused by TF not being fully started. It will be automatically corrected after it is fully started. 2. `Waiting for laser_scans...' (1) Check the whether the cable of LiDAR is correctly connected (2) Check whether the Router's network segment is:
192.168.1.1
(3) Check the ip address of IPC. The default address is
192.168.1.102

Appendix
Appendix1 Basic Operating Commands
2.1 Directory operating commands
(1) Directory switch: cd cd / switch to the root directory cd /usr switch to the usr directory under the root directory cd ../ switch to the upper level directory or cd .. cd ~ switch to the home directory cd - switch to the last visited directory (2) Directory view: ls ls: view all directories and files in the current directory ls -a: view all directories and files in the current directory (including hidden files) ls -l or ll: list view all directories and files in the current directory (list view, which displays more information) ls /dir: view all directories and files in the specified directory, like: ls /usr (3) Create directory: mkdir mkdir aaa: create a directory named aaa in the current directory mkdir /usr/aa: create a directory named aaa in the specified directory (4) Show hidden directory: Ctrl+h In the folder, you can display the hidden folders in the folder by Ctrl+h (5)Terminate program: Ctrl+c Enter the command Ctrl+c in the terminal to forcefully terminate the program being executed
2.2 ROS commonly used commands
(1) Compile command: catkin_make Used to compile packages in the entire workspace

(2) Initialize workspace: catkin_init_workspace Used to initialize the workspace when creating the workspace for the first time (3) Create package: catkin_create_pkg Used to create a package, and its syntax is:
catkin_create_pkg <package_name> [depend1] [depend2] [depend3]...
4Node running command rosrun is used to run the .cpp files and the .py files, and its syntax is:
rosrun package_name node_name roslaunch is used to run .launch files. In the launch files, the .cpp files and the .py files can be called at the same time, and its syntax is:
roslaunch package_name node_name

Appendix2 Parameter Configuration of Navigation Package

If you need to try to debug the parameters in the package yourself, you can refer to the following list.
7.1 Configurable parameters in the gmapping package

Parameter

Type Default

Description

~throttle_scans

int

The scan data threshold to be processed; the default is to process 1 scan data at a time (it can be set larger to skip some scan data)

Parameter ~base_frame ~map_frame ~odom_frame ~map_update_interval
~maxUrange
~sigma
~kernelSize ~lstep ~astep ~iterations ~lsigma
~ogain

Type Default

Description

string base_link Robot base coordinate system

string map

Map coordinate system

string odom

Odometer coordinate system

float 5.0

Map update frequency

float 80

Detect the maximum available range, that is, the range that the beam can reach

float 0.05

Standard deviation of endpoint matching

int

Used to find the corresponding kernel size

float 0.05

Translation optimization step

float 0.05

Rotation optimization step

int

Scan matching iterations

float 0.075

Laser standard deviation for likelihood calculation

float 3.0

Used for smooth resampling effect during likelihood calculation

Parameter ~lskip ~minimumScore ~srr
~srt ~str ~stt ~linearUpdate ~angularUpdate

Type Default

Description

int

The number of beams skipped in each scan.

float 0.0

The lowest value of the scan matching result

float 0.1

The mileage error during translation as a translation function (rho/rho)

float 0.2

The mileage error during translation as a rotation function (rho/theta)

float 0.1

The mileage error during rotation as a translation function(theta/rho)

float 0.2

The mileage error during rotation as a rotation function (theta/theta)

float 1.0

The robot translates a certain distance and processes the laser data once

float 0.5

The robot rotates a certain distance and processes the laser

Parameter
~temporalUpdate
~resampleThreshold ~particles ~xmin ~ymin ~xmax ~ymax ~delta

Type Default

Description

data once

float -1.0

If the latest scan processing is slower than the update, one scan is processed. Turn off time-based updates when the value is negative.

float 0.5

Resampling threshold based on Neff

int

Number of particles in the filter

float -100.0

The initial minimum size of the map in the x direction

float -100.0

The initial minimum size of the map in the y direction

float 100.0

The initial maximum size of the map in the x direction

float 100.0

The initial maximum size of the map in the y direction

float 0.05

Map resolution

Parameter

Type Default

Description

~llsamplerange

float 0.01

The translation sampling distance of likelihood calculation

~llsamplestep

float 0.01

The translation sampling step of likelihood calculation

~lasamplerange

float 0.005

The angle sampling distance of likelihood calculation

~lasamplestep

float 0.005

The angle sampling step of likelihood calculation

~transform_publish_period float 0.05

TF transform publishing period

~occ_thresh

float 0.25

The threshold of raster map occupancy rate

~maxRange

float ----

The maximum range of sensor

7.2 Configurable parameters in the cartographer package

Parameter

Default

Analysis

map_frame

The ID of the ROS coordinate
system used to publish submaps, map
the parent coordinate system of
the pose, usually "map".

Parameter tracking_frame
published_frame odom_frame

Default

Analysis

The ID of the ROS coordinate system tracked by the SLAM base_footprint algorithm. If IMU is used, its coordinate system should be used, usually "imu_link".

odom

The ID of the ROS coordinate system used to publish the pose sub-coordinate system, like the "odom" coordinate system. If an "odom" coordinate system is provided by different parts of the system, in this case, the "odom" pose in the map_frame will be published. Otherwise, it may be appropriate to set it to "base_link".

odom

It is enabled when provide_odom_frame is true. The coordinate system is used to publish local SLAM results

Parameter provide_odom_frame use_odometry num_laser_scans

Default true false 1

Analysis
between published_frame and map_frame, usually "odom".
If enabled, local, non-closed-loop, and continuous poses will be published as odom_frame in map_frame.
If enabled, subscribe to nav_msgs/Odometry messages on the "odom" topic. The mileage information will be provided, which is included in SLAM.
The number of laser scanning topics subscribed. Subscribe to sensor_msgs/LaserScan on the "scan" topic of one laser scanner or subscribe to the topics "scan_1", "scan_2", etc. on multiple laser scanners.

Parameter

Default

num_multi_echo_laser_scans

num_subdivisions_per_laser_scan 1

num_point_clouds

Analysis
The number of subscribed multi-echo laser scanning topics. Subscribe to sensor_msgs/MultiEchoLaserScan on the "echoes" topic of a laser scanner or subscribe to the topics "echoes_1", "echoes_2", etc. for multiple laser scanners.
The number of point clouds that divide each received (multi-echo) laser scan. The subdivision scan can cancel the scan acquired by the scan when the scanner is moving. There is a corresponding trajectory builder option to accumulate subdivision scans into the point cloud that will be used for scan matching.
The number of point cloud topics to be subscribed to. Subscribe to

Parameter

Default

lookup_transform_timeout_sec 0.2

submap_publish_period_sec

0.3

pose_publish_period_sec

5e-3

trajectory_publish_period_sec

30e-3

Analysis
sensor_msgs/PointCloud2 on the "points2" topic of a range finder or subscribe topics "points2_1", "points2_2", etc. for multiple range finders.
The timeout seconds of looking up and transforming with tf2.
The period (in seconds) for publishing submaps, eg.0.3 seconds.
The period (in seconds) for publishing poses, eg. 5e-3, with a frequency of 200 Hz.
The period for publishing trajectory tag in seconds, eg. 30e-3, lasting 30 milliseconds.

7.3 Configurable parameters in the amcl package
Note: The parameter configuration files of the amcl package are: amcl_param_diff.yaml (the file is the amcl parameter file used in the four-wheel differential, omnidirectional wheel, and track motion modes), and amcl_param.yaml (the file is the amcl parameter file used in the Ackermann motion mode) .

Parameter min_particles max_particles kld_err
kld_z
update_min_d update_min_a resample_interval

Type Default Description

The minimum number of particles

int

100

allowed.

The maximum number of particles

int

5000

allowed.

double 0.01

The maximum error between the true distribution and the estimated distribution.

double 0.99

The upper normal quantile of (1-p), where p is the probability that the error on the estimated detuning will be less than kld_err.

double 0.2m

A translation movement needs to be performed before performing the filter update.

double

/ 6.0 radians

A rotation movement needs to be performed before performing the filter update.

int

The number of filter updates required before resampling.

Parameter

Type Default Description

transform_tolerance double 0

The time at which the published transformation will be post-processed to indicate that the transformation will be effective in the future.

recovery_alpha_slow double 0

The exponential decay rate of the slow average weight filter is used to decide when to recover by adding random poses. A good value may be 0.001.

recovery_alpha_fast double 0.0m

The exponential decay rate of the fast average weight filter is used to decide when to recover by adding random poses. A good value may be 0.1.

initial_pose_x

double 0.0m

The initial pose average (x), used to initialize the filter with Gaussian distribution.

initial_pose_y

double 0.0rad

The initial pose average (y), used to initialize the filter with Gaussian distribution.

initial_pose_a

double 0.5 *

The initial pose average (yaw), used to

Parameter
initial_cov_xx initial_cov_yy initial_cov_aa gui_publish_rate save_pose_rate

Type Default Description

0.5m

initialize the filter with Gaussian distribution.

double

0.5 * 0.5m

The initial pose covariance (x * x), used to initialize the filter with Gaussian distribution.

The initial pose covariance (y * y), used to double -1.0 Hz initialize the filter with Gaussian
distribution.

double 0.5 Hz

The initial pose covariance (yaw * yaw), used to initialize the filter with Gaussian distribution.

double FALSE

The maximum rate (Hz) of publishing visual scans and paths. -1.0 is disabled.

double FALSE

Store the maximum rate (Hz) of the last estimated pose and covariance of the parameter server in the variables ~initial_pose_ and ~initial_cov_. This saved pose will be used in subsequent runs to initialize the filter. -1.0 is

Parameter use_map_topic first_map_only

Type Default Description

disabled.

bool -1

When set to be true, AMCL will subscribe to the map topic instead of making a service call to receive its map.

bool -1

When set to be true, AMCL will only use the first mapping it subscribes to instead of updating each time a new mapping is received.

7.4 Configurable parameters in DWA

Parameter

Type Default Description

acc_lim_x

double 2.5

Robot's x acceleration limit (m/s2)

acc_lim_y

double 2.5

Robot's y acceleration limit (m/s2)

acc_lim_th

double 3.2

Robot's rotational acceleration limit (m/s2)

max_vel_trans

double 0.55

The absolute value of the maximum translational velocity of the robot (m/s).

min_vel_trans

double 0.1

The absolute value of the minimum

Parameter

Type Default Description

translational velocity of the robot (m/s).

max_vel_x

double 0.55

Robot's maximum x velocity (m/s)

min_vel_x

double 0.0

Robot's minimum x velocity (m/s), negative when moving in reverse

max_vel_y

double 0.1

Robot's maximum y velocity (m/s)

min_vel_y

double -0.1

Robot's minimum y velocity (m/s)

max_rot_vel

double 1.0

The absolute value of the maximum rotation velocity of the robot (rad/s)

min_rot_vel

double 0.4

The absolute value of the minimum rotation velocity of the robot (rad/s)

yaw_goal_tolerance

double 0.05

The radian tolerance of the yaw/rotation when the controller achieves its goal

xy_goal_tolerance

double 0.10

The tolerance of the controller in the distance between x and y when achieving the goal (m/s)

latch_xy_goal_tolerance bool

false

If the goal tolerance is locked, when

Parameter
sim_time sim_granularity vx_samples vy_samples vth_samples controller_frequency

Type Default Description

the robot reaches the goal xy position, it will simply rotate into position, even if it eventually exceeds the goal tolerance while doing so.

double 1.7

Time to simulate the trajectory forward in seconds

double 0.025

Step taken between points on a given trajectory (m/s)

int

The number of samples used when exploring the x velocity space

The number of samples used when

int

exploring the y velocity space

The number of samples used when

int

exploring the theta velocity space

double 20.0

Call the controller's frequency. If it is not set in the controller's namespace, use searchParam to read the parameters from the parent namespace. Use together with

Parameter

Type Default Description

move_base, which means you only need to set its "controller_frequency" parameter and you can safely not set this parameter.

path_distance_bias

double 32.0

The weight that how close the controller should be to the given path

goal_distance_bias

double 24.0

The weight that the controller should try to reach its local goal and it should also control the velocity

occdist_scale

double 0.01

The weight that the controller should try to avoid obstacles

forward_point_distance double 0.325

The distance from the center of the robot to the additional scoring point, in meters

stop_time_buffer

double 0.2

The amount of time the robot must stop before colliding for the trajectory to be valid, in seconds

scaling_speed

double 0.25

The absolute value of the speed at which the robot's footprint is scaled

Parameter max_scaling_factor
publish_cost_grid
oscillation_reset_dist prune_plan

Type Default Description

(m/s)

double 0.2

The biggest factor in scaling a robot's footprint

Whether the cost grid that the planner will use when planning will be published? When it's true, sensor_msgs/PointCloud2 will be available on the ~/cost_cloud topic. bool false Each point cloud represents a cost grid and has a field for each individual scoring function component and the total cost of each cell, taking the scoring parameters into account.

double 0.05

How far the robot must move in meters before resetting the oscillation tag

bool true

Define whether the robot will eat the plan when moving along the path. If it's set to be true, the points will fall

Parameter

Type Default Description from the end of the plan as soon as the robots move more than 1 meter.

Brand of the group
Official Distributor gr@generationrobots.com
+33 5 56 39 37 05 www.generationrobots.com

References

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