University Mechanical Engineering Assignment: CACCS, AI, and Robotics

This document presents a mechanical engineering assignment solution focusing on the Co-operative Adaptive Cruise Control System (CACCS). The assignment involves modeling the CACCS using MATLAB and Simulink, analyzing the relationship between vehicle velocity and frictional drag, and estimating the frictional coefficient. It explores the impact of different input signals on the output and coefficient. Furthermore, the assignment delves into the relationship between Artificial Intelligence (AI) and Robotics, explaining their differences and common areas. It defines AI, discusses its applications, and presents a Venn diagram illustrating the intersection of AI and Robotics. The solution also includes relevant references to support the concepts discussed.

Running head: MECHANICAL ENGINEERING
MECHANICAL ENGINEERING
Name of the Student
Name of the University
Author Note

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1)
The Co-operative Adaptive Cruise Control System (CACCS) is entirely based on information
that is received from a vehicle which is ahead in the same lane. The model diagram of the
system is given below.
The linear relationship between the velocity of the cart v and the frictional drag coefficient is
given by,
F = -b*v.
This can be modelled in MATLAB or Simulink with the given values and specified input
signals.
MATLAB function:
t = 0:10; % time vector
m = 1000; % mass of the vehicle
u(1:6) = 1161; % applied propusive force
u(7:11) = 0; % force is withdrawn after 5 sec.

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slope = 5.136/5;
v(1:6) = slope.*t(1:6);
slope1 = (4-5.136)/(11-6);
v(7:11) = t(7:11).*slope1;
subplot(2,1,1)
plot(t,u,'r-')
xlabel('time in sec')
ylabel('force in Newton')
legend('applied force')
hold on
subplot(2,1,2)
plot(t,v,'b-')
xlabel('time in sec')
ylabel('velocity in m/sec')
legend('velocity of vehicle')
Plot:

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0 1 2 3 4 5 6 7 8 9 10
time in sec
0
500
1000
force in Newton applied force
0 1 2 3 4 5 6 7 8 9 10
time in sec
-2
0
2
4
velocity in m/sec velocity of vehicle
2) Now, the data obtained from the Simulink model can provide the least square estimate and
based on that estimate the coefficient b can be found.
3) Now, if the input signals in the model are changed to square wave or some other waves
then the output will be different and thus the frictional coefficient will be changed.
4)
Relationship between the Artificial Intelligence and Robotics:
The artificial intelligence (AI) is a term that refers to using computers or machines for doing
intelligence tasks (Russell and Norvig 2016). The AI mainly focuses on developing
algorithms which will help to learn machines automatically or with minimal human
interactions about certain tasks. The AI algorithms are mainly applied to medical diagnosis,
e-commerce, gaming, Robotics and in many more sections(Lu et al. 2018). The disciplines
under AI other than robotics are natural language processing, computer vision, e-commerce

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and computational biology. Many researches are going in different sections of AI like in
estimation theory, interface of natural languages, machine learning, probabilistic modelling
of language models for using those in spoken language machine interfaces, multi-agent
negotiation and modelling and integration of haptic, auditory and information of motor
(Steels and Brooks 2018). The robotics and artificial intelligence are two very different thing
but a small subsection is common between two fields. The relationship between the robotics
and the artificial intelligence can be presented by the following Venn diagram (Markoff
2016).
Robotics is basically a portion of technology which mainly deals with the robots. The robots
are basically machines those are programmable that are able to perform actions in an
autonomous way or by a semi-autonomous way (Ghahramani 2015). The three most
important property of a robot are the interaction between the robots and the physical world
happens via the sensors and the actuators. The robots are not bound to be autonomous, as an
example Telerobots are totally controlled by the human operators and it is a branch of the
robotics (Laird, Lebiere and Rosenbloom 2017). It is often desired that robots must be able to
think by their own and thus it need some artificial intelligence. The largely accepted

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definition of the robot is that a machine which is developed, designed and programmed on
physical system to serve some particular purposes.
The AI is also classified by a segment of computer science which involve developing
algorithms for accomplishing tasks which generally requires intelligence of humans. The
algorithms of AI can effectively learn, perceive, solve-problems, understand human language
and also able to apply logic reasoning. In the modern AI is used by most advanced
organizations like in Google’s search engine, recommended Amazon’s search engines and in
many advanced computer games. Most of the AI algorithms are not used for controlling
robots.

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References:
Ghahramani, Z., 2015. Probabilistic machine learning and artificial
intelligence. Nature, 521(7553), p.452.
Laird, J.E., Lebiere, C. and Rosenbloom, P.S., 2017. A Standard Model of the Mind: Toward
a Common Computational Framework Across Artificial Intelligence, Cognitive Science,
Neuroscience, and Robotics. Ai Magazine, 38(4).
Lu, H., Li, Y., Chen, M., Kim, H. and Serikawa, S., 2018. Brain intelligence: go beyond
artificial intelligence. Mobile Networks and Applications, 23(2), pp.368-375.
Markoff, J., 2016. Machines of loving grace: The quest for common ground between humans
and robots. HarperCollins Publishers.
Russell, S.J. and Norvig, P., 2016. Artificial intelligence: a modern approach. Malaysia;
Pearson Education Limited,.
Steels, L. and Brooks, R., 2018. The artificial life route to artificial intelligence: Building
embodied, situated agents. Routledge.