Modified 2018-02-28 by tanij
Modified 2017-11-20 by RomeoStaub
Modified 2017-11-20 by RomeoStaub
Estimate better models to make localization and control more efficient and robust to different configurations of the robot.
Modified 2017-11-23 by Andrea Censi
NOSCE TE IPSUM
(Know thyself)
Modified 2017-11-20 by RomeoStaub
Modified 2017-11-20 by RomeoStaub
Modified 2017-11-20 by RomeoStaub
Every Duckiebot is different in configuration.
Mission = we need to make control robust to different configuration
Problem statement = we need to identify kinematic model to make control robust enough
Modified 2017-11-20 by RomeoStaub
Modified 2017-11-22 by Andrea Censi
We make use of the no lateral slipping motion hypothesis and the pure rolling constrain as shown in the #duckiebot-modeling, to write the following equation:
\begin{align} \label{eq:mod-kin-3}\tag{1} \left\{ \begin{array}{l} \dot x_A^R &= R (\dot \varphi_R +\dot \varphi_L)/2 \\ \dot y_A^R &= 0 \\ \dot \theta &= \omega = R(\dot \varphi_R - \dot \varphi_L)/(2L) \end{array} \right., \end{align}
Further we make the assumption that for steady state that there is a linear relationship between the input voltage and the velociy of the wheel:
\begin{align} \label{eq:mod-kin-4}\tag{2} v_r=R \dot \varphi_l=c_r V_r\\ v_r=R \dot \varphi_l=c_l V_l \end{align}
This lets us rewrite equation \eqref{eq:mod-kin-3}:
\begin{align} \label{eq:mod-kin-5}\tag{3} \left\{ \begin{array}{l} \dot x_A^R &= (c_r V_r+c_l V_l)/2 \\ \dot y_A^R &= 0 \\ \dot \theta &= \omega = (c_r V_r+c_l V_l)/(2L) \end{array} \right., \end{align}
Using the assumption that we can measure $v_A$ we can determine $c_r$ by setting the voltage $V_l=0$. The same procedure can be done to get $c_l$.
Using the assumption that we can measure $\dot \theta$ we will then get the semiaxis length $L$.
Modified 2017-11-20 by RomeoStaub
Modified 2017-11-20 by RomeoStaub
Modified 2017-11-20 by RomeoStaub
Modified 2017-11-22 by Andrea Censi
Run lane follower with old version and new version with kinematic model. Drive on the track for one minute and count the number of times the bot touches the side or center line.
Metrics
We will use the performance measurement setup of the devel-control group
Modified 2017-11-20 by RomeoStaub
Modified 2017-11-22 by Andrea Censi
Parameter estimation
Mapping voltage → velocity
Modified 2017-11-20 by RomeoStaub
Modified 2017-11-20 by RomeoStaub
Duckiebots with different hardware configurations for testing
Modified 2017-11-22 by Andrea Censi
Modified 2017-11-20 by RomeoStaub
None
Modified 2017-11-20 by RomeoStaub
Date | Task Name | Target Deliverables |
---|---|---|
17/11/17 | Kick-Off | Preliminary Design Document |
24/11/17 | Play around | Identify current problems |
01/12/17 | First estimation | find paramers of robot |
08/12/17 | Validation | Performance measure |
15/12/17 | Caster wheel | Performance measure of new implementation |
22/12/17 | Buffer | |
29/12/17 | Documentation | Duckuments |
05/01/18 | End of Project |
Modified 2017-11-20 by RomeoStaub
What data do you need to collect?
Modified 2017-11-20 by RomeoStaub
Performances of the current implementation
TODO Jacopo Tani: fix links
The following was marked as "todo".
TODO Jacopo Tani: fix links
File book/fall2017_projects/13_sysid/10-preliminary-design-sysid.md.
File book/fall2017_projects/13_sysid/10-preliminary-design-sysid.md
in repo duckietown/docs-fall2017_projects branch master commit f551eedd
last modified by Andrea Censi on 2018-09-02 16:46:23
create_notes_from_elements
in module mcdp_docs.task_markers
.the above contains a number of interesting sections of relevance to the work of this group:
exact modeling of caster wheel and the kinematic constraints it introduces (pg. 395)
different system identification procedures: parametric or nonparametric (Chapter 14); in particular, a note on Observability (pg. 337)
we want to maximize performance of control + localization. Control uses unicycle model in Frenet frame (pg. 803 of handbook of robotics)
We need to identify wheel radii (r_1, r_2: assume equal at start = r), semi-axle length L, and motors steady state parameters (mapping between voltage and angular rate, i.e. mapping between voltage and velocity once (a) wheel radius is known and no slipping hypothesis is made).
Adaptive control (pg. 147): another approach is implementing an adaptive controller. It is meant to work with plant uncertainty.
Modified 2017-11-22 by Andrea Censi
What could go wrong?
Mitigation strategy:
No questions found. You can ask a question on the website.