Mir 250 Quick start guide

Technical Guide (en)

Date: 07/2020

Revision: v.1.0

express written permission of Mobile Industrial Robots A/S (MiR). MiR makes no warranties,

expressed or implied, in respect of this document or its contents. In addition, the contents of

the document are subject to change without prior notice. Every precaution has been taken in

the preparation of this manual. Nevertheless, MiR assumes no responsibility for errors or

omissions or any damages resulting from the use of the information contained.

Contact the manufacturer:

Mobile Industrial Robots A/S

Emil Neckelmanns Vej 15F

DK-5220 Odense SØ

www.mobile-industrial-robots.com

Phone: +45 20 377 577

Email: support@mir-robots.com

CVR: 35251235

Table of contents

1. About this document 4

2. Robot sub-systems 5

2.1 Navigation and control system 5

2.2 Safety system 22

2.3 Motor and brake control system 39

3. Robot components 45

3.1 Safety laser scanners 45

3.2 3D cameras 50

3.3 Proximity sensor modules and indicator lights 54

3.4 Drive train 59

3.5 Power board 64

3.6 Safety contactors 66

3.7 Robot computer 70

3.8 Safety PLC 72

3.9 Router and access point 76

1. About this document

This document describes the components, sub-systems, and connections in the MiR250

robot, providing an overview of how the robot works.

This guide is intended to be used to provide additional information regarding how MiR250

robots and their key components work. The guide focuses mainly on providing information

that may be used for troubleshooting the robot.

2. Robot sub-systems

The following sections describe these robot sub-systems:

•The navigation and control system determines the path the robot should follow to reach

its goal destination.

•The safety system monitors the robot's components and surroundings through several

functions and brings the robot to a stop if an unsafe situation occurs.

•The motor and brake system is part of the two previous systems and is used to either

move the robot along its path or to bring the robot to a stop.

2.1 Navigation and control system

The navigation and control system is responsible for driving the robot to a goal position

while avoiding obstacles.

System overview

The purpose of the navigation and control system is to guide the robot from one position on

a map to another position. The user provides the map and chooses the goal position the

robot must move to. The diagram in Figure 2.1 describes the processes in the navigation and

control system. The main processes involved in the navigation system are:

•Global planner

The navigation process starts with the global planner determining the best path for the

robot to get from its current position to the goal position. It plans the route to avoid walls

and structures on the map.

•Local planner

While the robot is following the path made by the global planner, the local planner

continuously guides the robot around detected obstacles that are not included on the

map.

•Obstacle detection

The safety laser scanners, 3D cameras, and proximity sensors are used to detect obstacles

in the work environment. These are used to prevent the robot from colliding with

obstacles.

•Localization

This process determines the robot's current position on the map based on input from the

motor encoders, inertial measurement unit(IMU), and safety laser scanners.

2. Robot sub-systems

•Motor controller, motors, and brakes

The motor controller determines how much power each motor must receive to drive the

robot along the intended path safely. Once the robot reaches the goal position, the brakes

are engaged to stop the robot.

Each part of the process is described in greater detail in the following sections.

Figure 2.1. Flow chart of the navigation and robot system. The user provides the necessary input for the robot

to generate a path to the goal position. The robot executes the steps in the navigation loop until it reaches the

goal position and stops by engaging the brakes.

2. Robot sub-systems

User input

To enable the robot to navigate autonomously, you must provide the following:

•A map of the area, either from a .png file or created with the robot using the mapping

function.

•A goal destination on that map.

•The current position of the robot on the map. This usually only needs to be provided when

a new map is activated.

Figure 2.2. On the map, the current position of the robot is identified by the robot icon , and the goal

destination is the robot position in this example. The robot computer now determines a path from the

current position to the goal position.

Once the robot computer has a map with the robot's current position and a goal destination,

it begins planning a route between the two positions on the map using the global planner.

Global planner

The global planner is an algorithm in the robot computer that generates a path to the goal

position.This path is known as the global path.

2. Robot sub-systems

Figure 2.3. The global path is shown with the blue dotted line that leads from the start to the goal position.

The global path is created only at the start of a move action or if the robot has failed to

reach the goal position and needs to create a new path. The generated path only avoids the

obstacles the robot detected when the path was made and the obstacles marked on the map.

The global path can be seen in the robot interface as a dotted line from the robot's start

position to the goal position.

Figure 2.4. The dotted line from the start position of the robot to the goal position is the global path generated

by the robot computer.

2. Robot sub-systems

Local planner

The local planner is used continuously while the robot is driving to guide it around obstacles

while still following the global path.

Figure 2.5. The global path is indicated with the dotted blue line. The local path is indicated with the blue arrow,

showing the robot driving around a dynamic obstacle.

Whereas the global planner creates a single path from start to finish, the local planner

continues to create new paths that adapt to the current position of the robot and the

obstacles around it. The local planner only processes the area that is immediately

surrounding the robot, using input from the robot sensors to avoid obstacles.

The local path is not displayed in the robot interfaces. The arrows in the

images here are visual aid used in this guide only.

2. Robot sub-systems

Figure 2.6. The local planner usually follows the global planner, but as soon as an obstacle gets in the way, the

local planner determines which immediate path will get the robot around the obstacle. In this case, it will likely

choose the path indicated with a green arrow.

Once the local path is determined, the robot computer derives the desired rotational

velocity of each drive wheel to make the robot follow the local path, and sends the desired

velocities for each motor to the motor controllers—see Motor controller and motors on

page19.

Obstacle detection

The robot detects obstacles continuously while driving. This enables the robot to use the

local planner to drive around obstacles and to determine the robot's current position on the

map.

Three sensor types are responsible for detecting obstacles:

•The safety laser scanners

•The 3D cameras

•The proximity sensors

The following illustrations show how the robot sees the surrounding environment and how it

is portrayed in the robot interface.

2. Robot sub-systems

What a human sees What the laser scanners

see What the 3D cameras see

A chair placed in the

corner of a room is

detectable by the robot.

In the robot interface, the

red lines on a map are

obstacles detected by the

laser scanners, and the

purple clouds are an

aggregate of the 3D

camera and laser scanner

data. The scanners only

detect the four legs of the

chair.

The 3D cameras detect

more details of the chair

when the robot gets close

enough to it.This view

cannot be seen in the

robot interface.

Safety laser scanners

The safety laser scanners on MiR250 are of the type AOPDDR (active opto-electronic

protective device responsive to diffuse reflection). AOPDDR is a protective device that uses

opto-electronic transmission and reception elements to detect the reflection of the optical

radiation generated by the protective device. The reflection is generated by an object in a

defined two-dimensional area. This is a type of ESPE (electro-sensitive protective device). In

this guide, the term safety laser scanner is used.

Two safety laser scanners, diagonally placed on front and rear corners of the robot, scan

their surroundings. Each safety laser scanner has a 270° field of view, overlapping and thus

providing a full 360° visual protection around the robot.

2. Robot sub-systems

When in motion, the safety laser scanners continuously scan the surroundings to detect

objects.

Figure 2.7. The two safety laser scanners together provide a full 360° view around the robot.

The laser scanners have the following limitations:

•They can only detect objects that intersect a plane at 200 mm height from the floor.

•They do not detect transparent obstacles well.

•The scanner data can be inaccurate when detecting reflective obstacles.

•The laser scanners may detect phantom obstacles if they are exposed to strong direct

light.

If you are using the robot in an area with walls made of glass or reflective

material, mark the walls as Forbidden zones on the map and not as a walls.

Walls in the map that the robot cannot detect will confuse the robot's

navigation system.

2. Robot sub-systems

3Dcameras

Two 3D cameras positioned on the front of the robot detect objects in front of the robot. The

3D cameras detect objects:

•Vertically up to 1800 mm at a distance of 1200 mm in front of the robot.

•Horizontally in an angle of 114° and 250 mm to the first view of ground.

The 3D cameras are only used for navigation. They are not part of the robot's safety system.

The camera readouts are used as 3D point cloud data. They are not recording

recognizable objects or people.

Figure 2.8 shows the field of view of the cameras.

2. Robot sub-systems

Figure 2.8. The two 3D cameras can see objects up to 1800 mm above floor height at a distance of 1200 mm in

front of the robot and have a horizontal field of view of 114°.

The 3D cameras have the following limitations:

•They can only detect objects in front of the robot, unlike the full 360° view of the laser

scanners.

•They do not detect transparent or reflective obstacles well.

•They do not detect holes or decending stairways.

•The cameras are not reliable at determining depth when viewing structures with

2. Robot sub-systems

repetitive patterns.

•The cameras may detect phantom obstacles if they are exposed to strong direct light.

Proximity sensors

Proximity sensors placed in all four corners of the robot detect objects close to the floor that

cannot be detected by the safety laser scanners.

Using infrared light, the proximity sensors point downwards and make sure that the robot

does not run into low objects, such as pallets and forklift forks. They have a range between

5-20 cm around the robot.

Because of the proximity sensor's limited range, the data from them is only useful when the

robot is standing still or moving at reduced speeds, for example, when the robot it pivoting

or docking.

Figure 2.9. The proximity sensors in the corners of the robot detect objects below the safety laser scanners

plane of view.

The proximity sensors have the following limitations:

•They do not have a long range and are mainly used to detect obstacles missed by the

laser scanners and cameras.

•When the robot is driving fast, obstacles detected by the proximity sensors are too close

for the robot to stop for or avoid them.

2. Robot sub-systems

Localization

The goal of the localization process is for the robot to determine where it is currently

located on its map. The robot has three inputs for determining where it is:

•The initial position of the robot. This is used as a reference point for the methods used to

determine the robot position.

•The IMUand encoder data. This is used to determine how far and fast the robot has

traveled from the initial position.

•The laser scanner data. This is used to determine the likely positions of the robot by

comparing the data with nearby walls on the map.

This data is used by a particle filter to determine the most likely position of the robot on the

map.

IMUand motor encoders

Both the data from the IMU (Inertial Measurement Unit) and motor encoders is used to

derive where and how fast the robot has traveled over time from its initial position. The

combination of both sets of data makes the derived position more accurate.

The IMU measures the acceleration and pivot speed of the robot. From this, the robot can

derive the distance the robot has driven and how much it has turned.

The motor encoders measure how many times the motor of each drive wheel has rotated.

With each rotation of the motor, the robot has driven forward the same length of the

circumference of the drive wheel. Measuring it from both encoders also enables the robot to

determine when it is turning.

If the drive wheels are worn down significantly or the robot is running with an

incorrect gear ratio, the robot will miscalculate how far it has traveled based

on the encoder data.

Laser scanners and particle filtering

The robot computer compares the input from the laser scanners with the walls on the map

to try to find the best match. This is done using a particle filter algorithm. The robot

2. Robot sub-systems

computer only compares with the area where it expects the robot to be based on the

encoder and IMUdata. This means it is important that the initial position of the robot is

correct.

The robot computer uses the comparison and the odometry data from the encoders and

IMUto produce a number of points where the robot is most likely to be. As the robot moves

and the sensors collect another set of data, the robot computes another set of likely positions

based on new data and the previous data. This process is known as particle filtering.

Failed localization Successful localization

Figure 2.10. In a failed localization, the robot cannot determine a position where the red lines (laser scanner

data) align with the black lines on the map. When the robot can localize itself, it determines a cluster of likely

positions, indicated in the images above as blue dots.

To make sure the robot can localize itself well using particle filtering, consider the following

when creating a map:

•There must be unique and distinguishable static landmarks on the map that are easily

recognizable. A landmark is a permanent structure that the robot can use to orient itself,

such as corners, doorways, columns, and shelves.

2. Robot sub-systems

No distinguishable landmarks Many distinguishing landmarks

•The robot must be able to detect the static landmarks that are marked on the map to be

able to approximate its current position. Make sure there are not too many dynamic

obstacles around the robot so that it cannot detect any static landmarks.

Cannot detect any landmarks Can detect enough landmarks

2. Robot sub-systems

•To improve the robot's localization, it can often help to divide long continuous walls on

the map. Even if the walls are connected in the actual work environment, it can help the

localization process if the walls on the map are divided into smaller sections.

Undivided walls Divided walls

•The robot does not compare the laser scanner data with the entire map, but only around

the area that it expects to be close to based on the IMU and encoder data and its initial

position. This is why it is important that the initial position you place the robot at on the

map is accurate.

•The robot can drive for a short distance without being correctly localized. As it drives, the

estimated positions should converge to a small area, indicating the robot has determined

an accurate estimate. If this does not occur within a set time limit, the robot reports a

localization error.

Motor controller and motors

The robot computer compares the desired velocity of the robot with its current velocity. The

computer determines how far the rotational velocity of each motor is from the desired

velocity of each motor needed to make the robot follow the intended path. The robot

computer sends the necessary changes in velocity for each motor to the motor controllers.

2. Robot sub-systems

The motor controller translates the difference into the amount of power that must be sent to

each motor to achieve the desired velocity. The motor controller regulates whether the

amount of power sent to the motors is resulting in the correct velocity by translating the

motor encoder data into the robot's velocity and comparing this to the desired velocity—see

Motor and brake control system on page39.

The robot computer keeps checking that the position derived from the localization process is

following the intended path. If the robot begins to drive away from the path, the computer

corrects the desired velocity that it sends to the motor controllers to ensure that the robot

drives with the correct trajectory.

In this way, the robot uses its sensors to determine how far it is from achieving the desired

trajectory, enabling it to correct itself as it drives.

Brakes

Once the approximated position of the robot determined from localization is the same as the

goal position calculated by the global planner, the robot stops by using the dynamic brake

function.

Figure 2.11. The robot has reached the goal position and stops by engaging the brakes.

The dynamic brake function stops the robot by short circuiting the power that was used to

rotate the motor. When this happens, the power that was used to drive the robot forward is

now reversed to stop the rotation of the drive wheels

Once the robot has stopped, the mechanical brakes are enabled. These brakes are used to

keep the robot in place once it has stopped. You can compare it with the parking brake or

hand brake in a car.

Other manuals for 250

Table of contents

Popular Robotics manuals by other brands

Sony

Sony Aibo ERS-7 user guide

Robotis

Robotis BIOLOID GP quick start

Epson

Epson RC90 Safety and installation

Epson

Epson LS20 MANIPULATOR MANUAL

ABB

ABB IRB 140 type C product manual

Robotronics

Robotronics Toby Tire Robot operating manual

SunFounder

SunFounder RollFlash user manual

Alumotion

Alumotion YouRing Start guide

Dobot

Dobot Conveyor Belt instructions

SunFounder

SunFounder Sloth Learning Kit manual

productive robotics

productive robotics OB7 user manual

ABB

ABB BullsEye Applications manual

ABB

ABB IRB 140 product manual

Mesh

Mesh GPIO instructions

iRobot

iRobot DJ Create quick start guide

Phomatix

Phomatix Dali the PhotoBot user manual

Hasbro

Hasbro Marvel Avengers Infinity War Legends Infinity... quick start guide

Totem

Totem MINI SHOOTER Assembly guide

MIR 250 Quick start guide

Popular Robotics manuals by other brands