PRE2022 3 Group5: Difference between revisions
m (added references to bottom of the page) |
(added space for papers, and added first 2 summaries) Tag: 2017 source edit |
||
| Line 3: | Line 3: | ||
{| class="wikitable" | {| class="wikitable" | ||
!Name!!Student id | !Name!!Student id | ||
!Role | |||
|- | |- | ||
|Vincent van Haaren||1 | |Vincent van Haaren||1 | ||
|Human Interaction Specialist | |||
|- | |- | ||
|Jelmer Lap||1569570 | |Jelmer Lap||1569570 | ||
|LIDAR & Environment mapping Specialist | |||
|- | |- | ||
|Wouter Litjens||1751808 | |Wouter Litjens||1751808 | ||
|Chasis & Drivetrain Specialist | |||
|- | |- | ||
|Boril Minkov||1 | |Boril Minkov||1 | ||
|Data Processing Specialist | |||
|- | |- | ||
|Jelmer Schuttert||1480731 | |Jelmer Schuttert||1480731 | ||
|Robotic Motion Tracking Specialist | |||
|- | |- | ||
|Joaquim Zweers||1734504 | |Joaquim Zweers||1734504 | ||
|Actuation and Locomotive Systems Specialist | |||
|} | |} | ||
| Line 62: | Line 69: | ||
|Examination | |Examination | ||
|} | |} | ||
==State of the Art== | |||
===Literature Research=== | |||
{| class="wikitable" | |||
|+Overview | |||
!Paper Title | |||
!Reference | |||
!Reader | |||
|- | |||
|Modelling an accelerometer for robot position estimation | |||
|<ref>Z. Kowalczuk and T. Merta, "Modelling an accelerometer for robot position estimation," 2014 19th International Conference on Methods and Models in Automation and Robotics (MMAR), Miedzyzdroje, Poland, 2014, pp. 909-914, doi: 10.1109/MMAR.2014.6957478.</ref> | |||
|Jelmer | |||
|- | |||
|An introduction to inertial navigation | |||
|<ref>Woodman, O. J. (2007). ''An introduction to inertial navigation'' (No. UCAM-CL-TR-696). University of Cambridge, Computer Laboratory.</ref> | |||
|Jelmer | |||
|- | |||
| | |||
| | |||
| | |||
|} | |||
====Modelling an accelerometer for robot position estimation==== | |||
The paper discusses the need for high-precision models of location and rotation sensors in specific robot and imaging use-cases, specifically highlighting SLAM systems (Simultaneous Localization and Mapping systems). | |||
It highlight sensors that we may also need: | |||
" In this system the orientation data rely on inertial sensors. Magnetometer, accelerometer and gyroscope placed on a single board are used to determine the actual rotation of an object. " | |||
It mentions that, in order to derive position data from acceleration, it needs to be doubly integrated, which tents to yield great inaccuracy. | |||
drawback: the robot needs to stop after a short time (to re-calibrate) when using double-integration to minimize error-accumulation: | |||
“Double integration of an acceleration error of 0.1g would mean a position error of more than 350 m at the end of the test”. | |||
An issue in modelling the sensors is that rotation is measured by gravity, which is not influenced by for example yaw, and gets more complicated under linear acceleration. | |||
The paper modelled acceleration, and rotation according to various lengthy math equations and matrices, and applied noise and other real-word modifiers to the generated data. | |||
It notably uses cartesian and homogeneous coordinates in order to seperate and combine different components of their final model, such as rotation and translation. These components are shown in matrix form and are derived from specification of real-world sensors, known and common effects, and mathematical derivations of the latter two. | |||
The proposed model can be used to test code for our robot's position computations. | |||
====An introduction to inertial navigation==== | |||
This paper (as report) is meant to be a guide towards determining positional and other navigation data from interia based sensors like gyroscopes, accelerometers and IMU's in general. | |||
It starts by explaining the inner workings of a general IMU, and gives an overview of an algorithm used to determine position from said sensors' readings using integration, showing what intermitted values represent using pictograms. | |||
It then proceeds to discuss various types of gyroscopes, their ways of measuring rotation (such as light inference), and resulting effects on measurements, which are neatly summarized in equations and tables. It takes a similar for Linear acceleration measurement devices. | |||
In the latter half the paper, concepts and methods relevant to processing the introduced signals are explained, and most importantly it is discussed how to partially account for some of the errors of such sensors. It starts by explaining how to account for noise using allan variance, and shows how this effects the values from a gyroscope. | |||
Next, the paper introduces the theory behind tracking orientation, velocity and position. It talks about how errors in previous steps propagate through the process, resulting in the infamously dangerous accumulation of inaccuracy that plagues such systems. | |||
Lastly, it shows how to simulate data from the earlier discussed sensors. Notably though the previous paper already discussed a more accurate and recent algorithm (building on this paper). | |||
<references /> | <references /> | ||
Revision as of 19:33, 10 February 2023
Group members
| Name | Student id | Role |
|---|---|---|
| Vincent van Haaren | 1 | Human Interaction Specialist |
| Jelmer Lap | 1569570 | LIDAR & Environment mapping Specialist |
| Wouter Litjens | 1751808 | Chasis & Drivetrain Specialist |
| Boril Minkov | 1 | Data Processing Specialist |
| Jelmer Schuttert | 1480731 | Robotic Motion Tracking Specialist |
| Joaquim Zweers | 1734504 | Actuation and Locomotive Systems Specialist |
Project Idea
The project idea we settled on is designing a crawler robot to autonomously create 3d maps of difficult to traverse environments so humans can plan routes through small unknown spaces
Project planning
| Week | Description |
|---|---|
| 1 | Group formation |
| 2 | Prototype design plans done
Bill of Materials created & Ordered components |
| break | Carnaval Break |
| 3 | Monday: Split into sub-teams
work started on prototypes for LIDAR, Locomotion and Navigation |
| 4 | Thursday: Start of integration of all prototypes into robot demonstrator |
| 5 | Thursday: First iteration of robot prototype done [MILESTONE] |
| 6 | Buffer week - expected troubles with integration |
| 7 | Environment & User testing started [MILESTONE] |
| 8 | Iteration upon design based upon test results |
| 9 | Monday: Final prototype done [MILESTONE] & presentation |
| 10 | Examination |
State of the Art
Literature Research
| Paper Title | Reference | Reader |
|---|---|---|
| Modelling an accelerometer for robot position estimation | [1] | Jelmer |
| An introduction to inertial navigation | [2] | Jelmer |
Modelling an accelerometer for robot position estimation
The paper discusses the need for high-precision models of location and rotation sensors in specific robot and imaging use-cases, specifically highlighting SLAM systems (Simultaneous Localization and Mapping systems).
It highlight sensors that we may also need: " In this system the orientation data rely on inertial sensors. Magnetometer, accelerometer and gyroscope placed on a single board are used to determine the actual rotation of an object. "
It mentions that, in order to derive position data from acceleration, it needs to be doubly integrated, which tents to yield great inaccuracy.
drawback: the robot needs to stop after a short time (to re-calibrate) when using double-integration to minimize error-accumulation: “Double integration of an acceleration error of 0.1g would mean a position error of more than 350 m at the end of the test”.
An issue in modelling the sensors is that rotation is measured by gravity, which is not influenced by for example yaw, and gets more complicated under linear acceleration. The paper modelled acceleration, and rotation according to various lengthy math equations and matrices, and applied noise and other real-word modifiers to the generated data.
It notably uses cartesian and homogeneous coordinates in order to seperate and combine different components of their final model, such as rotation and translation. These components are shown in matrix form and are derived from specification of real-world sensors, known and common effects, and mathematical derivations of the latter two.
The proposed model can be used to test code for our robot's position computations.
This paper (as report) is meant to be a guide towards determining positional and other navigation data from interia based sensors like gyroscopes, accelerometers and IMU's in general.
It starts by explaining the inner workings of a general IMU, and gives an overview of an algorithm used to determine position from said sensors' readings using integration, showing what intermitted values represent using pictograms.
It then proceeds to discuss various types of gyroscopes, their ways of measuring rotation (such as light inference), and resulting effects on measurements, which are neatly summarized in equations and tables. It takes a similar for Linear acceleration measurement devices.
In the latter half the paper, concepts and methods relevant to processing the introduced signals are explained, and most importantly it is discussed how to partially account for some of the errors of such sensors. It starts by explaining how to account for noise using allan variance, and shows how this effects the values from a gyroscope.
Next, the paper introduces the theory behind tracking orientation, velocity and position. It talks about how errors in previous steps propagate through the process, resulting in the infamously dangerous accumulation of inaccuracy that plagues such systems.
Lastly, it shows how to simulate data from the earlier discussed sensors. Notably though the previous paper already discussed a more accurate and recent algorithm (building on this paper).
- ↑ Z. Kowalczuk and T. Merta, "Modelling an accelerometer for robot position estimation," 2014 19th International Conference on Methods and Models in Automation and Robotics (MMAR), Miedzyzdroje, Poland, 2014, pp. 909-914, doi: 10.1109/MMAR.2014.6957478.
- ↑ Woodman, O. J. (2007). An introduction to inertial navigation (No. UCAM-CL-TR-696). University of Cambridge, Computer Laboratory.