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3 Commits
| Author | SHA1 | Date |
|---|---|---|
 a2nr
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4d7fdf707d | |
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Anggoro Dwi
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6ab468a65e | |
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Anggoro Dwi
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6bfea0fb4f |
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@ -48,6 +48,13 @@ menjadi koordinat kartesian untuk dapat dimasukkan dalam state kendali formasi.
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\end{bmatrix} \label{eq:algo_koordinat}
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\end{align}
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\begin{figure}
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\centering
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\includegraphics[scale=.2]{BAB3/img/estimate_coordinate.png}
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\caption{Strategi Penentuan Koordinat Langakah Kedua}
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\label{fig:strategiPenentuanKoordinat_dua}
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\end{figure}
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\textbf{Langkah kedua}. Koordinat di Persamaan~\eqref{eq:algo_koordinat} akan menghasilkan bias dikarenakan
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Persamaan~\eqref{eq:algo_getAngle} tidak mengetahui letak kuadran sudutnya. Menggunakan ilustrasi di Gambar~\ref{fig:strategiPenentuanKoordinat_satu},
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langkah pertama menghasilkan dua kemungkinan koordinat robot $B_1$ dan $B_1'$ .
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@ -71,13 +78,6 @@ menjadi jarak dan akan dibandingkan jarak tersebut dengan informasi jarak dari s
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berpindah ke $A''$ . Apabila terdapat perbedaan maka kejadian di Persamaan~\eqref{eq:algo_getAngle} diubah kejadian selanjutnya
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dan mengkoreksi koordinat sebelumnya.
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\begin{figure}
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\centering
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\includegraphics[scale=.2]{BAB3/img/estimate_coordinate2.png}
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\caption{Strategi Penentuan Koordinat Langakah Kedua}
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\label{fig:strategiPenentuanKoordinat_dua}
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\end{figure}
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\section{Analisa Algoritama Dengan Tetangga Statis}
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Telah dijelaskan pada Bab \ref{bab:empat:Strategi_koordinat_tetangga} bahwa robot bergerak kearah yang random dengan jarak tertentu untuk mengetahui koordinat tetangga.
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@ -0,0 +1,6 @@
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Andreas Febrian
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Lia Sadita
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Fahrurrozi Rahman
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Andre Tampubolon
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Erik Dominikus
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Anggoro Dwi Nur Rohman
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@ -0,0 +1,11 @@
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@Comment{$ biblatex control file $}
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options = {3.7:0:0:1:0:1:1:0:0:0:0:0:3:1:3:1:0:0:3:1:79:+:+:none},
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@ -0,0 +1,441 @@
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||||
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|
||||
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|
||||
\field{pages}{1\bibrangedash 5}
|
||||
\field{title}{Current research in multirobot systems}
|
||||
\field{volume}{7}
|
||||
\field{journaltitle}{Artificial Life and Robotics}
|
||||
\field{month}{03}
|
||||
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||||
\verb{doi}
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||||
\verb 10.1109/ISDEA.2012.316
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||||
\endverb
|
||||
\field{isbn}{978-1-4673-4893-5}
|
||||
\field{pages}{1335\bibrangedash 1339}
|
||||
\field{title}{Study on Formation Control of Multi-Robot Systems}
|
||||
\field{month}{01}
|
||||
\field{year}{2013}
|
||||
\endentry
|
||||
|
||||
\entry{6889491}{inproceedings}{}
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||||
\name{author}{3}{}{%
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||||
{{hash=WX}{%
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||||
family={{Wang}},
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|
||||
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|
||||
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|
||||
given={J.},
|
||||
giveni={J\bibinitperiod},
|
||||
}}%
|
||||
}
|
||||
\keyw{dynamic programming;mobile robots;multi-robot
|
||||
systems;neurocontrollers;optimal control;predictive control;quadratic
|
||||
programming;recurrent neural nets;torque control;trajectory control;model
|
||||
predictive control approach;multirobot formation control problem;simplified
|
||||
dual neural network;leader-follower scheme;desired trajectory
|
||||
tracking;dynamic quadratic optimization problem;one-layer recurrent neural
|
||||
network;optimal control input;Vectors;Lead;Wheels;Neural networks;Robot
|
||||
kinematics;Mathematical model}
|
||||
\strng{namehash}{WXYZWJ1}
|
||||
\strng{fullhash}{WXYZWJ1}
|
||||
\field{labelnamesource}{author}
|
||||
\field{labeltitlesource}{title}
|
||||
\field{booktitle}{2014 International Joint Conference on Neural Networks
|
||||
(IJCNN)}
|
||||
\verb{doi}
|
||||
\verb 10.1109/IJCNN.2014.6889491
|
||||
\endverb
|
||||
\field{issn}{2161-4393}
|
||||
\field{pages}{3161\bibrangedash 3166}
|
||||
\field{title}{Model predictive control of multi-robot formation based on
|
||||
the simplified dual neural network}
|
||||
\field{year}{2014}
|
||||
\warn{\item Invalid format of field 'month'}
|
||||
\endentry
|
||||
|
||||
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|
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\name{author}{3}{}{%
|
||||
{{hash=FSE}{%
|
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family={Ferik},
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||||
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||||
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|
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}
|
||||
\keyw{Cluster space, Behavioral control, Fuzzy adaptive, Multi-robots}
|
||||
\strng{namehash}{FSENMTBU1}
|
||||
\strng{fullhash}{FSENMTBU1}
|
||||
\field{labelnamesource}{author}
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||||
\field{labeltitlesource}{title}
|
||||
\field{abstract}{%
|
||||
Cooperation between autonomous robot vehicles holds several promising
|
||||
advantages like robustness, adaptability, configurability, and scalability.
|
||||
Coordination between the different robots and the individual relative motion
|
||||
represent both the main challenges especially when dealing with formation
|
||||
control and maintenance. Cluster space control provides a simple concept for
|
||||
controlling multi-agent formation. In the classical approach, formation
|
||||
control is the unique task for the multi-agent system. In this paper, the
|
||||
development and application of a novel Behavioral Adaptive Fuzzy-based
|
||||
Cluster Space Control (BAFC) to non-holonomic robots is presented. By
|
||||
applying a fuzzy priority control approach, BAFC deals with two conflicting
|
||||
tasks: formation maintenance and target following. Using priority rules, the
|
||||
fuzzy approach is used to adapt the controller and therefore the behavior of
|
||||
the system, taking into accounts the errors in the formation states and the
|
||||
target following states. The control approach is easy to implement and has
|
||||
been implemented in this paper using SIMULINK real-time platform. The
|
||||
communication between the different agents and the controller is established
|
||||
through Wi-Fi link. Both simulation and experimental results demonstrate the
|
||||
behavioral response where the robot performs the higher priority tasks first.
|
||||
This new approach shows a great performance with a lower control signal when
|
||||
benchmarked with previously known results in the literature.%
|
||||
}
|
||||
\verb{doi}
|
||||
\verb https://doi.org/10.1016/j.asoc.2016.03.018
|
||||
\endverb
|
||||
\field{issn}{1568-4946}
|
||||
\field{pages}{117 \bibrangedash 127}
|
||||
\field{title}{A Behavioral Adaptive Fuzzy controller of multi robots in a
|
||||
cluster space}
|
||||
\verb{url}
|
||||
\verb http://www.sciencedirect.com/science/article/pii/S1568494616301272
|
||||
\endverb
|
||||
\field{volume}{44}
|
||||
\field{journaltitle}{Applied Soft Computing}
|
||||
\field{year}{2016}
|
||||
\endentry
|
||||
|
||||
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|
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|
||||
{{hash=YC}{%
|
||||
family={Yoshioka},
|
||||
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|
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|
||||
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|
||||
}}%
|
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{{hash=NT}{%
|
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|
||||
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|
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|
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|
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}}%
|
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}
|
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\strng{fullhash}{YCNT1}
|
||||
\field{labelnamesource}{author}
|
||||
\field{labeltitlesource}{title}
|
||||
\field{abstract}{%
|
||||
This paper deals with formation control strategies based on Virtual
|
||||
Structure (VS) for multi-vehicle systems. We propose several control laws for
|
||||
networked multi-nonholonomic vehicle systems in order to achieve VS
|
||||
consensus, VS Flocking and VS Flocking with collision-avoidance. First,
|
||||
Virtual Vehicle for the feedback linearization is considered, and we propose
|
||||
VS consensus and Flocking control laws based on a virtual structure and
|
||||
consensus algorithms. Then, VS Flocking control law considering collision
|
||||
avoidance is proposed and its asymptotical stability is proven. Finally,
|
||||
simulation and experimental results show effectiveness of our proposed
|
||||
approaches.%
|
||||
}
|
||||
\verb{doi}
|
||||
\verb https://doi.org/10.3182/20080706-5-KR-1001.00865
|
||||
\endverb
|
||||
\field{issn}{1474-6670}
|
||||
\field{note}{17th IFAC World Congress}
|
||||
\field{number}{2}
|
||||
\field{pages}{5149 \bibrangedash 5154}
|
||||
\field{title}{Formation Control of Nonholonomic Multi-Vehicle Systems based
|
||||
on Virtual Structure}
|
||||
\verb{url}
|
||||
\verb http://www.sciencedirect.com/science/article/pii/S1474667016397609
|
||||
\endverb
|
||||
\field{volume}{41}
|
||||
\field{journaltitle}{IFAC Proceedings Volumes}
|
||||
\field{year}{2008}
|
||||
\endentry
|
||||
|
||||
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|
||||
\name{author}{3}{}{%
|
||||
{{hash=OKK}{%
|
||||
family={Oh},
|
||||
familyi={O\bibinitperiod},
|
||||
given={Kwang-Kyo},
|
||||
giveni={K\bibinithyphendelim K\bibinitperiod},
|
||||
}}%
|
||||
{{hash=PMC}{%
|
||||
family={Park},
|
||||
familyi={P\bibinitperiod},
|
||||
given={Myoung-Chul},
|
||||
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|
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}}%
|
||||
{{hash=AHS}{%
|
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family={Ahn},
|
||||
familyi={A\bibinitperiod},
|
||||
given={Hyo-Sung},
|
||||
giveni={H\bibinithyphendelim S\bibinitperiod},
|
||||
}}%
|
||||
}
|
||||
\keyw{Formation control, Position-based control, Displacement-based
|
||||
control, Distance-based control}
|
||||
\strng{namehash}{OKKPMCAHS1}
|
||||
\strng{fullhash}{OKKPMCAHS1}
|
||||
\field{labelnamesource}{author}
|
||||
\field{labeltitlesource}{title}
|
||||
\field{abstract}{%
|
||||
We present a survey of formation control of multi-agent systems. Focusing
|
||||
on the sensing capability and the interaction topology of agents, we
|
||||
categorize the existing results into position-, displacement-, and
|
||||
distance-based control. We then summarize problem formulations, discuss
|
||||
distinctions, and review recent results of the formation control schemes.
|
||||
Further we review some other results that do not fit into the
|
||||
categorization.%
|
||||
}
|
||||
\verb{doi}
|
||||
\verb https://doi.org/10.1016/j.automatica.2014.10.022
|
||||
\endverb
|
||||
\field{issn}{0005-1098}
|
||||
\field{pages}{424 \bibrangedash 440}
|
||||
\field{title}{A survey of multi-agent formation control}
|
||||
\verb{url}
|
||||
\verb http://www.sciencedirect.com/science/article/pii/S0005109814004038
|
||||
\endverb
|
||||
\field{volume}{53}
|
||||
\field{journaltitle}{Automatica}
|
||||
\field{year}{2015}
|
||||
\endentry
|
||||
|
||||
\entry{Oh2014}{article}{}
|
||||
\name{author}{2}{}{%
|
||||
{{hash=OKK}{%
|
||||
family={Oh},
|
||||
familyi={O\bibinitperiod},
|
||||
given={Kwang-Kyo},
|
||||
giveni={K\bibinithyphendelim K\bibinitperiod},
|
||||
}}%
|
||||
{{hash=AHS}{%
|
||||
family={Ahn},
|
||||
familyi={A\bibinitperiod},
|
||||
given={Hyo-Sung},
|
||||
giveni={H\bibinithyphendelim S\bibinitperiod},
|
||||
}}%
|
||||
}
|
||||
\keyw{formation control, distance-based control, graph rigidity,
|
||||
Hamiltonian systems, gradient systems}
|
||||
\strng{namehash}{OKKAHS1}
|
||||
\strng{fullhash}{OKKAHS1}
|
||||
\field{labelnamesource}{author}
|
||||
\field{labeltitlesource}{title}
|
||||
\field{abstract}{%
|
||||
SUMMARYWe study the local asymptotic stability of undirected formations of
|
||||
single-integrator and double-integrator modeled agents based on interagent
|
||||
distance control. First, we show that n-dimensional undirected formations of
|
||||
single-integrator modeled agents are locally asymptotically stable under a
|
||||
gradient control law. The stability analysis in this paper reveals that the
|
||||
local asymptotic stability does not require the infinitesimal rigidity of the
|
||||
formations. Second, on the basis of the topological equivalence of a
|
||||
dissipative Hamiltonian system and a gradient system, we show that the local
|
||||
asymptotic stability of undirected formations of double-integrator modeled
|
||||
agents in n-dimensional space is achieved under a gradient-like control law.
|
||||
Simulation results support the validity of the stability analysis. Copyright
|
||||
© 2013 John Wiley \& Sons, Ltd.%
|
||||
}
|
||||
\verb{doi}
|
||||
\verb 10.1002/rnc.2967
|
||||
\endverb
|
||||
\verb{eprint}
|
||||
\verb https://onlinelibrary.wiley.com/doi/pdf/10.1002/rnc.2967
|
||||
\endverb
|
||||
\field{number}{12}
|
||||
\field{pages}{1809\bibrangedash 1820}
|
||||
\field{title}{Distance-based undirected formations of single-integrator and
|
||||
double-integrator modeled agents in n-dimensional space}
|
||||
\verb{url}
|
||||
\verb https://onlinelibrary.wiley.com/doi/abs/10.1002/rnc.2967
|
||||
\endverb
|
||||
\field{volume}{24}
|
||||
\field{journaltitle}{International Journal of Robust and Nonlinear Control}
|
||||
\field{year}{2014}
|
||||
\endentry
|
||||
|
||||
\entry{Rozenheck2015}{inproceedings}{}
|
||||
\name{author}{3}{}{%
|
||||
{{hash=RO}{%
|
||||
family={{Rozenheck}},
|
||||
familyi={R\bibinitperiod},
|
||||
given={O.},
|
||||
giveni={O\bibinitperiod},
|
||||
}}%
|
||||
{{hash=ZS}{%
|
||||
family={{Zhao}},
|
||||
familyi={Z\bibinitperiod},
|
||||
given={S.},
|
||||
giveni={S\bibinitperiod},
|
||||
}}%
|
||||
{{hash=ZD}{%
|
||||
family={{Zelazo}},
|
||||
familyi={Z\bibinitperiod},
|
||||
given={D.},
|
||||
giveni={D\bibinitperiod},
|
||||
}}%
|
||||
}
|
||||
\keyw{gradient methods;multi-agent systems;PI control;velocity
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
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||||
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||||
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|
||||
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|
||||
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|
||||
practical approach in order to estimate the coefficients of coulomb and
|
||||
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|
||||
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|
||||
motors as inputs and the linear and angular velocities of the robot as
|
||||
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|
||||
to demonstrate the performance of the proposed modeling.%
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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Penelitian ini ditujukan untuk mengembangkan algoritma kendali formasi berdasarkan jarak
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|
||||
Kendali formasi berdasarkan jarak diterapkan pada model holonomic mobile robot
|
||||
menggunakan \textit{omniwheel}.
|
||||
Algoritma \textit{cosinus} digunakan untuk menemukan koordinat tetangga
|
||||
pada kondisi awal.
|
||||
Hasil percobaan dibuktikan secara grafik dari perbandingan menggunakan algoritma dan tidak, bahwa
|
||||
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|
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|
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%----------------------------------------------------------------------------------------
|
||||
% TITLE PAGE
|
||||
%----------------------------------------------------------------------------------------
|
||||
|
||||
\title[ ]{Kendali Formasi Murni Berdasarkan Jarak Menggunakan Algoritma Cosinus Pada Sistem Orde Dua} % The short title appears at the bottom of every slide, the full title is only on the title page
|
||||
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\author{Anggoro Dwi Nur Rohman} % Your name
|
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{
|
||||
Universitas Brawijaya \\ % Your institution for the title page
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\medskip
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\textit{anggoro\_dwi@student.ub.ac.id} % Your email address
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||||
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\note{}
|
||||
|
||||
%----------------------------------------------------------------------------------------
|
||||
% BAB 1
|
||||
%----------------------------------------------------------------------------------------
|
||||
|
||||
\section{Pendahuluan}
|
||||
\subsection{Latar Belakang}
|
||||
\begin{frame}
|
||||
\frametitle{Next Section}
|
||||
\tableofcontents[currentsubsection]
|
||||
\end{frame}
|
||||
\note{}
|
||||
|
||||
\begin{frame}
|
||||
\frametitle{Latar Belakang}
|
||||
\begin{figure}
|
||||
\centering
|
||||
\includegraphics[scale=.5]{BAB1/img/presentation_journal1.png}
|
||||
\end{figure}
|
||||
\end{frame}
|
||||
\note{
|
||||
Penelitian ini bermula dari jurnal survey oleh Kwang-Kyo dan kawan kawan.
|
||||
Dimana penulis menggolongkan tentang kendali formasi kedalam beberapa klompok.
|
||||
Penggolongan tersebut dirangkum dari beberapa metode yang beliauw pilih.\\
|
||||
|
||||
Beliau menggolongkan kendali formasi tersebut berdasarkan variable yang disensor, variabel yang dikendalikan, metode koordinat, dan metode interaksinya.\\
|
||||
}
|
||||
\begin{frame}
|
||||
\frametitle{Latar Belakang}
|
||||
\begin{columns}[c]
|
||||
\column{.45\textwidth}
|
||||
Kendali formasi dibagi menjadi 3, yaitu :
|
||||
\begin{enumerate}
|
||||
\item Berdasarkan Posisi
|
||||
\item Berdasarkan Perpindahan
|
||||
\item Berdasarkan Jarak
|
||||
\end{enumerate}
|
||||
\column{.45\textwidth}
|
||||
\begin{figure}
|
||||
\centering
|
||||
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|
||||
\end{figure}
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||||
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||||
\begin{figure}
|
||||
\centering
|
||||
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|
||||
\end{figure}
|
||||
\end{frame}
|
||||
\note{
|
||||
\begin{itemize}
|
||||
\item Posisi \\
|
||||
Variable yang diperoleh dari sensor dan variable yang dikendalikan adalah posisi dari robot.\\
|
||||
koordinat yang digunakan adalah berdasarkan koordinat global. \\
|
||||
kemampuan untuk berkomunikasi tidak begitu dibutuhkan.
|
||||
\item Pergerakan \\
|
||||
Variable yang diperoleh dari sensro dan variable yang dikendalikan adalah posisi relatif terhadap tetangganya. \\
|
||||
Dapat diperhatikan pada gambar dibawah bahwa
|
||||
Koordinat yang digunakan setiap robot harus disearahkan terhadap semua robot dan penyearahan koordinat tersebut berdasarkan koordinat global.\\
|
||||
Kemampuan untuk berkomunkasi dibutuhkan setiap robot untuk bertukar informasi mengenai penyearahan koordinat.
|
||||
\item Jarak \\
|
||||
dapat diperhatikan juga pada gambar dibawah.
|
||||
Variable yang diperoleh dari sensor adalah koordinat relatif terhadap tetangga.\\
|
||||
Variable yang kendalikan adalah jarak terhadap tetangganya.\\
|
||||
Koordinat yang digunakan setiap robot adalah koordinat local atau koordinat robot itu sendiri.\\
|
||||
Kemampuan untuk berkomunikasi sangat dibutuhkan karena setiap robot akan aktif saling bertukar informasi untuk mengetahui koordinat relatif nya masing masing.
|
||||
\end{itemize}
|
||||
Dari ketiga golongan tersebut jika divisualkan berdasarkan kemampuan sensor kemampuan berkomunikasi dapat lihat pada gambar disamping. \\
|
||||
Semakin golongan tersbut keatas makan metode tersebut membutuhkan kemampuan sensor yang tinggi dan semakin kebawah sebaliknya. \\
|
||||
Semakin golongan tersebut ke kanan semakin golongan tersebut membutuhkan kemampuan interaksi yang tinggi dan semakin kekiri sebaliknya. \\
|
||||
|
||||
}
|
||||
|
||||
|
||||
\begin{frame}
|
||||
\frametitle{Latar Belakang}
|
||||
\textbf{Rangkuman dan Potensial Permasalahan}\\
|
||||
\begin{figure}
|
||||
\centering
|
||||
\includegraphics[scale=.55]{BAB1/img/presentation_rangkuman.png}
|
||||
\end{figure}
|
||||
\end{frame}
|
||||
\note{
|
||||
Dari ketiga metode tersebut, formasi berdasarkan jarak merupakan metode yang dimungkinkan untuk diterapkan sensor lebih sedikit dari metode lainnya.
|
||||
Teknologi komunikasi sekarang pun juga sudah bisa dikatakan bisa untuk diterapkan pada metode tersebut secara praktiknya.
|
||||
Pemaparan dengan menggunakan model yang lebih real sangat dibutuhkan sebagai kontribusi dalam bidang kendali multi-robot.
|
||||
Dengan harapan penerapan real model tersebut dapat bermanfaat terhadap masyarakat luas.
|
||||
|
||||
}
|
||||
|
||||
\subsection{Identifikasi dan Perumusan Masalah}
|
||||
\begin{frame}
|
||||
\frametitle{Next Section}
|
||||
\tableofcontents[currentsubsection]
|
||||
\end{frame}
|
||||
\note{}
|
||||
|
||||
\begin{frame}
|
||||
\frametitle{Identifikasi dan Perumusan Masalah}
|
||||
\begin{columns}
|
||||
\column{.45\textwidth}
|
||||
\textbf{Identifikasi} dilakukan menggunakan penelitian sebelumnya oleh \cite{Rozenheck2015}.
|
||||
\begin{align*}
|
||||
\dot{x}_f(t) & = A_f(x)x_f(t)+B_f(x)d+Bv_{ref} \\
|
||||
x_f(t) & = \begin{bmatrix} x & v & \xi_1 & \xi_2 \end{bmatrix}^T \\
|
||||
x & = \begin{bmatrix} x_1^T & \dots & x_n^T \end{bmatrix}^T \mathbb{R}^{2n} \\
|
||||
v & = \dot{x} \\
|
||||
x_i & \in \mathbb{R}^2 \\
|
||||
\end{align*}
|
||||
\textbf{Model yang digunakan}
|
||||
\begin{align*}
|
||||
\dot{x}_i(t) = u_i(t), \quad i = 1, \hdots, n,
|
||||
\end{align*}
|
||||
\column{.45\textwidth}
|
||||
\begin{figure}
|
||||
\centering
|
||||
\includegraphics[scale=.3]{BAB1/img/presentation_identifikasi_1.png}
|
||||
\includegraphics[scale=.1]{BAB2/img/plotMotion3Robot.png}
|
||||
\end{figure}
|
||||
\end{columns}
|
||||
\end{frame}
|
||||
\note{
|
||||
\textbf{Identifikasi} \\
|
||||
Identifikasi dilakukan menggunakan penelitian sebelumnya oleh Bapak Rozenheck.\\
|
||||
Menghasilkan sebuah metode yang menggunakan kendali PI untuk analisis Kendali formasi.\\
|
||||
Metode tersebut menghasilkan formasi pada multi agent tetap terjaga ketika salah satu agent diberikan kecepatan secara konstan dan memberikan respon yang baik ketika pengaturan konstanta PI dengan tepat.
|
||||
Tetapi model yang digunakan masih menggunakan model orde satu, dengan kata lain metode tersebut dimungkinkan untuk diterapkan model yang lebih komplek.
|
||||
|
||||
\textit{Next}
|
||||
}
|
||||
|
||||
\begin{frame}
|
||||
\begin{figure}
|
||||
\centering
|
||||
\includegraphics[scale=.27]{BAB2/img/plotMotion3Robot.png}
|
||||
\end{figure}
|
||||
\end{frame}
|
||||
\note{}
|
||||
|
||||
\begin{frame}
|
||||
\frametitle{Identifikasi dan Perumusan Masalah}
|
||||
\textbf{Batasan-batasan permasalahan sebagai berikut :}
|
||||
\begin{enumerate}
|
||||
\item Variable sensor yang digunakan adalah jarak antar individu robot.
|
||||
\item Komunikasi antar robot diasumsikan ideal, dalam artian percobaan tidak dilakukan diluar jarak jangkauan prangkat komunikasi.
|
||||
\end{enumerate}
|
||||
|
||||
\textbf{Perumusan Masalah:}
|
||||
\begin{enumerate}
|
||||
\item Bagaimanakan strategi untuk kendali formasi apabila variable yang dikendalikan adalah jarak antar robot?.
|
||||
\item Bagaimanakah pergerakan kendali formasi berdasarkan jarak apabila model yang digunakan adalah holonomic mobile robot ?.
|
||||
\end{enumerate}
|
||||
\end{frame}
|
||||
\note{}
|
||||
|
||||
\subsection{Tujuan dan Manfaat}
|
||||
|
||||
\begin{frame}
|
||||
\frametitle{Next Section}
|
||||
\tableofcontents[currentsubsection]
|
||||
\end{frame}
|
||||
\note{}
|
||||
\begin{frame}
|
||||
\frametitle{Tujuan dan Manfaat}
|
||||
|
||||
\textbf{Tujuan}
|
||||
\begin{enumerate}
|
||||
\item Mengetahui strategi untuk kendali formasi apabila variable yang dikendalikan adalah jarak antar robot.
|
||||
\item Mengetahui pergerakan kendali formasi berdasarkan jarak apabila model yang digunakan adalah holonomic mobile robot.
|
||||
\end{enumerate}
|
||||
|
||||
\textbf{Manfaat}
|
||||
\begin{enumerate}
|
||||
\item Memberikan referensi untuk permasalahan kendali multi-robot, kususnya pada permasalhaan kendali formasi, terhadap model yang lebih nyata.
|
||||
\item Membuka peluang penelitian dibidang kendali mengenai kendali formasi pada kendali multi-robot dilingkungan Fakultas Teknik Elektro, Universitas Brawijaya.
|
||||
\end{enumerate}
|
||||
\end{frame}
|
||||
\note{}
|
||||
|
||||
\section{Krangka Konsep Penelitian}
|
||||
\begin{frame}
|
||||
\frametitle{Next Section}
|
||||
\tableofcontents[currentsection]
|
||||
\end{frame}
|
||||
\note{
|
||||
}
|
||||
|
||||
\begin{frame}
|
||||
\frametitle{Kerangka Konsep Penelitian}
|
||||
\begin{figure}
|
||||
\input{BAB3/img/structur.tex}
|
||||
\end{figure}
|
||||
\end{frame}
|
||||
\note{
|
||||
\frametitle{Krangka Konsep Penelitian}
|
||||
Berikut ini adalah krangka penelitian dimana seperti yang telah diterangkan sebelumnya. \\
|
||||
Berdasarkan literatur oleh Oh, kendali formasi dibagi menjadi tiga bagian. \\
|
||||
Pada metode berdasarkan jarak, penelitian menggunakan simple model telah banyak dilakukan. \\
|
||||
Pengembangan selanjutnya diharapkan menuju ke model real. \\
|
||||
Dalam tahap pengembangan menuju real, diperlukan pengembangan model real. \\
|
||||
Sehingga Fokus penelitian yang saya ambil adalah kendali formasi berdasarkan jarak dengan model real.\\
|
||||
\textit{Next}
|
||||
}
|
||||
\subsection{Definisi Permasalahan Kendali Formasi}
|
||||
\begin{frame}
|
||||
\frametitle{Next Section}
|
||||
\tableofcontents[currentsubsection]
|
||||
\end{frame}
|
||||
\note{}
|
||||
\begin{frame}
|
||||
\frametitle{Definisi Permasalahan}
|
||||
\begin{itemize}
|
||||
\item Dari ketiga kategori tersebut, kendali formasi berbasis jarak sangat dibutuhkan pembahasan
|
||||
mengenai penerapan metode tersebut pada agent yang nyata.
|
||||
\textit{Simple model, Model real,} dan \textit{Real} dapat dikatakan sebuah tahap pengemabangan.
|
||||
\item model agent yang lebih relistik (\textit{Model real}) perlu untuk dipelajari lebih lanjut untuk menambah kepraktisan metode kendali multi-agent berdasarkan jarak.
|
||||
\item Peneliti sebelumnya oleh \cite{Rozenheck2015}, menggunakan \textit{Simple model} untuk mengembangkan kendali multi-robotnya.
|
||||
\item \textbf{Maka, penelitian ini akan difokuskan pada kendali formasi berbasis jarak
|
||||
kendali PI yang telah dilakukan sebelumnya dengan menggunakan model nyata.}
|
||||
\end{itemize}
|
||||
\end{frame}
|
||||
\note{}
|
||||
|
||||
\subsection{Permasalah dan Solusi}
|
||||
\begin{frame}
|
||||
\frametitle{Next Section}
|
||||
\tableofcontents[currentsubsection]
|
||||
\end{frame}
|
||||
\note{}
|
||||
\begin{frame}
|
||||
\frametitle{Permasalahan dan Solusi}
|
||||
\textbf{Permasalahan}
|
||||
\begin{itemize}
|
||||
\item state yang digunakan pada kendali formasi ,
|
||||
$x_f(t) = \begin{bmatrix} x & v & \xi_1 & \xi_2 \end{bmatrix}^T$,
|
||||
membutuhkan koordinat relatif tetangga.
|
||||
\item Batasan penelitian hanya dapat mengukur jarak terhadap tetangganya.
|
||||
\item Sedangkan koordinat relatif berbentuk kartesian,
|
||||
sehingga koordinat polar yang akan digunakan lalu diubah menjadi kartesian.
|
||||
\item Koordinat polar membutuhkan sudut untuk dapat diubah menjadi kartesian.
|
||||
\item \textbf{Karena itu, dibutuhkan algoritka kusus untuk mendapatkan sudut tersebut}
|
||||
\end{itemize}
|
||||
\textbf{Solusi}
|
||||
\begin{itemize}
|
||||
\item menggunakan hukum cosinus untuk menentukan sudut
|
||||
\item robot saling mengirim informasi kecepatan kepada tetangga digunakan untuk memantau
|
||||
koordinat relatif terhadap tetangga.
|
||||
\item \textbf{Sebagai inisialisasi menggunakan algoritma cosinus. Selebihnya menggunakan komunikasi untuk memantau koordinat relatif tetangga}
|
||||
\end{itemize}
|
||||
\end{frame}
|
||||
\note{}
|
||||
|
||||
\section{Kajian Pustaka}
|
||||
\subsection{Pemodelan Robot}
|
||||
\begin{frame}
|
||||
\frametitle{Next Section}
|
||||
\tableofcontents[currentsubsection]
|
||||
\end{frame}
|
||||
\note{
|
||||
|
||||
}
|
||||
\begin{frame}
|
||||
\frametitle{Pemodelan Robot}
|
||||
\begin{figure}
|
||||
\centering
|
||||
\includegraphics[scale=.3]{BAB2/img/presentasi_modelRobot_jurnal.png}
|
||||
\end{figure}
|
||||
\end{frame}
|
||||
\note{
|
||||
|
||||
\frametitle{Pemodelan Robot}
|
||||
Pemodelan robot merujuk dari penelitian sebelumnya oleh Correia.\\
|
||||
\textit{Next}
|
||||
}
|
||||
\begin{frame}
|
||||
\frametitle{Pemodelan Robot}
|
||||
\textbf{Model Robot}
|
||||
\begin{figure}
|
||||
\centering
|
||||
\includegraphics[scale=.3]{BAB2/img/presentasi_modelRobot_model.png}
|
||||
\end{figure}
|
||||
|
||||
\textbf{Persamaan Newton Orde dua Model Robot}
|
||||
\begin{align*}
|
||||
F_{\dot{x}_r}(t) - B_{\dot{x}_r}\dot{x}_r(t) - C_{\dot{x}_r}sgn(\dot{x}_r(t)) & = M\ddot{x}_r(t) \\
|
||||
F_{\dot{y}_r}(t) - B_{\dot{y}_r}\dot{y}_r(t) - C_{\dot{y}_r}sgn(\dot{y}_r(t)) & = M\ddot{y}_r(t) \\
|
||||
\Gamma(t) - B_{\dot{\theta}}\dot{\theta}(t) - C_{\dot{\theta} }sgn(\dot{\theta}(t) ) & = I\ddot{\theta}(t)
|
||||
\end{align*}
|
||||
\end{frame}
|
||||
\note{
|
||||
Dimana Peneliti mengembangkan sebuah model berdasarkan hukum fisika. \\
|
||||
Dari persamaan tersebut terbagi menjadi 3 persamaan yang mempresentasikan arah gerak robot\\
|
||||
\textit{Next}
|
||||
}
|
||||
\begin{frame}
|
||||
\frametitle{Pemodelan Robot}
|
||||
\textbf{Parameter dan Response}
|
||||
\begin{columns}
|
||||
\column{.45\textwidth}
|
||||
\begin{figure}
|
||||
\centering
|
||||
\includegraphics[scale=.3]{BAB2/img/presentasi_modelRobot_parameter.png}
|
||||
\end{figure}
|
||||
\column{.45\textwidth}
|
||||
\begin{figure}
|
||||
\centering
|
||||
\includegraphics[scale=.2]{BAB2/img/presentasi_modelRobot_velocityV.png}
|
||||
\includegraphics[scale=.2]{BAB2/img/presentasi_modelRobot_velocityVn.png}
|
||||
\includegraphics[scale=.2]{BAB2/img/presentasi_modelRobot_velocityW.png}
|
||||
\end{figure}
|
||||
\end{columns}
|
||||
\end{frame}
|
||||
\note{
|
||||
Peneliti juga mencantumkan konstanta yang digunakan ketika mengidentifikasi persamaan modelnya.\\
|
||||
Dan disamping ini adalah grafik respon kecepatan setiap arahnya.
|
||||
\textit{Next}
|
||||
}
|
||||
|
||||
\section{Metode Penelitian}
|
||||
\subsection{Prangkat Percobaan}
|
||||
\begin{frame}
|
||||
\frametitle{Next Section}
|
||||
\tableofcontents[currentsubsection]
|
||||
\end{frame}
|
||||
\note{}
|
||||
\begin{frame}
|
||||
\frametitle{Prangkat Percobaan}
|
||||
\textbf{Hardware-in-the-loop} \\
|
||||
Merujuk dari \cite{Jim1999} \\
|
||||
\begin{figure}
|
||||
\centering
|
||||
\includegraphics[scale=.4]{BAB3/img/hil_graph.png}
|
||||
\end{figure}
|
||||
\begin{itemize}
|
||||
\item Model dijalankan di PC menggunakan bahasa pemrograman Python.
|
||||
\item Sistem Tertanam menggunakan: \\
|
||||
Microcontroller STM3F466 \\
|
||||
ARM Cortex-M4 \\
|
||||
Clock 180Mhz \\
|
||||
Flash Memmory 256K \\
|
||||
Mbed Library dengan RTOS
|
||||
\end{itemize}
|
||||
\end{frame}
|
||||
\note{
|
||||
\frametitle{Prangkat Percobaan}
|
||||
\textbf{Hardware in loop} \\
|
||||
\textit{Hardware-in-the-loop} (HIL) adalah metode untuk pengembangan prangkat kendali dengan memanfaatkan model sebagai objek kendalinya. Seperti pada gambar,
|
||||
bahwa HIL terdiri dari dua prangkat, yaitu prangkat untuk menjalankan objek kendali atau dapat
|
||||
disebut sebagai model/plant dan prangkat sistem kontrolnya, dalam kasus ini sistem kontrol menggunakan sistem tertanam (\textit{embedded system}).
|
||||
|
||||
\textit{Next}
|
||||
}
|
||||
\begin{frame}
|
||||
\frametitle{Prangkat Percobaan}
|
||||
\textbf{Hardware-in-the-loop Kendali Formasi} \\
|
||||
\begin{figure}
|
||||
\centering
|
||||
\scalebox{.7}{\input{BAB4/img/Diagram_hil_controller.tex}}
|
||||
\end{figure}
|
||||
\begin{itemize}
|
||||
\item digunakan 3 prangkat sistem tertanam
|
||||
\item Komunikasi dengan PC mempresentasikan aktuator dan sensor
|
||||
\item Komunikasi antar kendali untuk pertukaran informasi
|
||||
\end{itemize}
|
||||
\end{frame}
|
||||
\note{
|
||||
\textbf{Hardware-in-the-loop Kendali Formasi} \\
|
||||
Dalam penerapan multi-robot, digunakan 3 perangkat sistem tertanam untuk mempresentasikan kendali 3 robot.
|
||||
Setiap prangkat pengendali akan saling terhubung satu sama lain dan semua prangkat pengendali terhubung dengan prangkat PC.
|
||||
Komunikasi antar prangkat pengendali akan digunakan untuk pertukaran informasi.
|
||||
Sedangkan komunikasi dengan PC akan mempresentasikan aktuator dan sensor untuk setiap prangkat
|
||||
kendali. PC akan merekam setiap keluaran dari model dan masukan dari setiap prangkat kendali
|
||||
sebagai tampilan pergerakan robotnya.
|
||||
\textit{Next}
|
||||
}
|
||||
\subsection{Strategi Kendali Multi Robot}
|
||||
\begin{frame}
|
||||
\frametitle{Next Section}
|
||||
\tableofcontents[currentsubsection]
|
||||
\end{frame}
|
||||
\note{}
|
||||
\begin{frame}
|
||||
\frametitle{Strategi Kendali Multi Robot}
|
||||
\textbf{Kendali Robot} \\
|
||||
\begin{itemize}
|
||||
\item Kendali Robot dengan input koordinat, output koordinat
|
||||
\item Menggunakan State Feedback
|
||||
\end{itemize}
|
||||
\begin{figure}
|
||||
\centering
|
||||
\scalebox{.7}{\input{BAB4/img/statefeedback.tex}}
|
||||
\end{figure}
|
||||
\begin{itemize}
|
||||
\item Sistem robot controlable dan observable
|
||||
\item Menggunakan QLR untuk menentukan konstanta $K_s$
|
||||
\item Menggunakan rumus $N = -[C(A-BK_s)^{-1}B]^{-1}$
|
||||
\item $u < 6/12 volt$
|
||||
\end{itemize}
|
||||
\end{frame}
|
||||
\note{
|
||||
\small
|
||||
\textbf{Kendali Robot}
|
||||
\begin{itemize}
|
||||
\item Sperti yang telah diketahui bahwa Kendali formasi menggunakan state koordinat robot untuk dikendalikan.
|
||||
Maka input sistem robot yyang dibutuhkan adalah state tujuan berupa koordinat.
|
||||
Makan sistem kendali robot ini memiliki input koordinat dan output koordinat.
|
||||
\item Untuk mencapai itu digunakan state feedback
|
||||
\item Syarat untuk menggunakan state feedback harus controlable dan observable. \\
|
||||
Untuk mengetahui nya parameter tersebut digunakan sistem dari penelitian sebelumnya.
|
||||
\item State feedback akan dioptimalisasi menggunakan metode QLR untuk menentukan konstanta $K_s$
|
||||
\item Menggunakan inferse dari sistem akan menemukan konstanta N.
|
||||
\item Dari kenyataanya bahwa $u$ memiliki batasan input, yaitu sekitar besaran 6-12 volt. \\
|
||||
Akan tetapi dalam kalkulasinya hasil perhitangan dari state feedback akan menghasilkan nilai $u$
|
||||
yang melebihi batasan tersebut. Maka secara program akan diberikan batasan nilai input dalam persamaan tersebut.
|
||||
\end{itemize}
|
||||
|
||||
\textit{Next}
|
||||
}
|
||||
\begin{frame}
|
||||
\frametitle{Strategi Kendali Multi Robot}
|
||||
\textbf{Respon Kendali Robot} \\
|
||||
$ r = \begin{bmatrix}6 & -3 & -90 &0 &0 &0\end{bmatrix} $
|
||||
\begin{figure}
|
||||
\centering
|
||||
\includegraphics[scale=.5]{BAB4/img/presentasi_respon_robot.png}
|
||||
\end{figure}
|
||||
\end{frame}
|
||||
\note{
|
||||
\textbf{Respon Kendali Robot} \\
|
||||
|
||||
berikut adalah respon dari kendali robot.
|
||||
|
||||
\textit{Next}
|
||||
}
|
||||
\begin{frame}
|
||||
\frametitle{Strategi Kendali Multi Robot}
|
||||
\textbf{Strategi penentuan koordinat tetangga}
|
||||
\begin{columns}
|
||||
\column{.2\textwidth}
|
||||
\begin{figure}
|
||||
\includegraphics[scale=.3]{BAB3/img/estimate_coordinate.png}
|
||||
\end{figure}
|
||||
\column{.45\textwidth}
|
||||
\begin{figure}
|
||||
\centering
|
||||
\includegraphics[scale=.3]{BAB4/img/presentasi_algoritma_cosinus.png}
|
||||
\end{figure}
|
||||
\end{columns}
|
||||
\end{frame}
|
||||
\note{
|
||||
\textbf{Strategi penentuan koordinat tetangga}
|
||||
|
||||
\begin{itemize}
|
||||
\item Pertama tama akan diconfigurasi komunikasi antar robot
|
||||
\item Lalu digenerate random direction
|
||||
\item setelah itu mengukur jarak tetangga dengan informasi konfigurasi komunikasi
|
||||
\item lalu dari hasil direksi yang random digunakan untuk menggerakkan robot dengan jarak yang telah ditentukan.
|
||||
\item Setelah robot mencapai jarak tersebut dilakukan kembali pengukura jarak
|
||||
\item Dari kedua hasil jarak tersebut dikalkulasi dengan rumus cosinus untuk mendapat kan sudut
|
||||
\item dari sudut tersebut diubah menjadi koordinat kartesian
|
||||
\end{itemize}
|
||||
}
|
||||
\begin{frame}
|
||||
\frametitle{Strategi Kendali Multi Robot}
|
||||
\textbf{Implementasi}
|
||||
\begin{figure}
|
||||
\centering
|
||||
\scalebox{.65}{\input{BAB4/img/implement-control.tex}}
|
||||
\end{figure}
|
||||
\end{frame}
|
||||
\note{
|
||||
\textbf{Implementasi}
|
||||
\begin{itemize}
|
||||
\item Implementasi akan menggabungkan antara state space kendali robot dengan kendali formasi.
|
||||
\item Kendali Robot sebagai kendali tingat akhir dan kendali formasi sebagai kendali tingkat awal
|
||||
\item Kendali ini akan diterapkan ke robot secara individual. Karena kendali utama membutuhkan state koordinat dari individulain, maka state koordinat tersebut digantikan dengan sensor dan algoritma yang dikembangkan
|
||||
\end{itemize}
|
||||
}
|
||||
|
||||
\subsection{Strategi Uji Coba}
|
||||
\begin{frame}
|
||||
\frametitle{Next Section}
|
||||
\tableofcontents[currentsubsection]
|
||||
\end{frame}
|
||||
\note{}
|
||||
\begin{frame}
|
||||
\frametitle{Strategi Uji Coba}
|
||||
\textbf{Analisa Kesetabilan Model} \\
|
||||
\begin{columns}[c]
|
||||
\column{.45\textwidth}
|
||||
\begin{itemize}
|
||||
\item Area kestabilan metode explicit euler
|
||||
\end{itemize}
|
||||
\begin{figure}
|
||||
\centering
|
||||
\includegraphics[scale=.3]{BAB2/img/equler_explicit.png}
|
||||
\end{figure}
|
||||
\begin{align*}
|
||||
y[k+1] & = (1+h\lambda)y[k] \\
|
||||
& = (1 + z)y[k] \\
|
||||
& = R(z)y[k]
|
||||
\end{align*}
|
||||
\column{.45\textwidth}
|
||||
\begin{itemize}
|
||||
\item Persamaan Model Robot akan diimplementasi pada PC
|
||||
\item Metode implementasi pada PC menggunakan Metode Explicit Euler
|
||||
\item Akan dicari konstanta $h$, sampling time, sampai $z$ dalam range kesetabilan diagram disamping
|
||||
\item Akan dibuktikan secara grafik
|
||||
\end{itemize}
|
||||
\end{columns}
|
||||
\end{frame}
|
||||
\note{}
|
||||
\begin{frame}
|
||||
\frametitle{Strategi Uji Coba}
|
||||
\textbf{Analisa Algoritma Dengan Tetangga Statis}
|
||||
\begin{columns}
|
||||
\column{.45\textwidth}
|
||||
\begin{figure}
|
||||
\includegraphics[scale=.4]{BAB3/img/estimate_coordinate.png}
|
||||
\end{figure}
|
||||
\column{.45\textwidth}
|
||||
\begin{itemize}
|
||||
\item Akan dianalisa dengan membandingkan berbagai jarak ($l_a$) untuk mengetahui respon algoritma yang sesuai dan optimal
|
||||
\item Menghasilkan jarak terbaik untuk algoritma cosinus.
|
||||
\item Pembuktian dilakukan secara grafik.
|
||||
\end{itemize}
|
||||
\end{columns}
|
||||
\end{frame}
|
||||
\note{}
|
||||
\begin{frame}
|
||||
\frametitle{Strategi Uji Coba}
|
||||
\textbf{Analisa Percobaan Keseluruhan}\\
|
||||
\begin{columns}
|
||||
\column{.45\textwidth}
|
||||
\begin{figure}
|
||||
\includegraphics[scale=.1]{BAB2/img/plotMotion3Robot.png}
|
||||
\end{figure}
|
||||
\column{.45\textwidth}
|
||||
\begin{itemize}
|
||||
\item Melanjutkan analisa static dengan menjalankan semua robot
|
||||
\item Akan menghasilkan grafik respon dari keseluruhan robot
|
||||
\item Hipotesis nya adalah keseluruhan robot akan menjaga jarak formasi dengan baik
|
||||
\end{itemize}
|
||||
\end{columns}
|
||||
|
||||
\end{frame}
|
||||
\note{}
|
||||
\section{End}
|
||||
\begin{frame}
|
||||
\Huge{\centerline{The End}}
|
||||
|
||||
\end{frame}
|
||||
\note{}
|
||||
\begin{frame}
|
||||
\frametitle{Daftar Pustaka}
|
||||
|
||||
\printbibliography
|
||||
\end{frame}
|
||||
\note{}
|
||||
\end{document}
|
||||
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Reference in New Issue