Probability theory and statistics for Robotics and ME

In summary, the author has a deep understanding of probability and statistics which is valuable in the field of control theory. He thinks that specializing in a field with hardcore theory is the best strategy for him as it is easy for him to make connections between theory and practice. He needs advices on what to concentrate on in his career.
  • #1
debelino
10
1
I study control theory and robotics. Recently I figured out that I have a much deeper understanding of probability and statistics compared to my colleagues. Is this 'talent' valuable in my field and if so, where? We used this theory to define white noise, but nothing more...as of now.

Also I am generally good in math and physics and I have a high IQ. I am lazy to do repetitive work and because of all this I think a good strategy for me would be to specialise in a field full of hardcore theory to reduce competition by taking advantage of my scientifically oriented mind. It is easy for me to make connections between theory and practice and fulfils me to see that something I theorized about is working in the real world.

I need advices for my career. What should I concentrate on? Any book recommendation is also welcome. Sorry for my English, it is not my first language.
 
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  • #2
Probability and statistics is very important in control theory. In every human effort, there is feedback, random behavior, and optimization. Regarding random behavior in control theory, your input sensors are not perfect and have a random component. Kalman filters are used to combine information, taking into account the reliability of each of the inputs. Also, there are usually random influences that can not be measured at all.
For a simple example that doesn't require Kalman filters, suppose an airplane flight control needs to estimate lateral velocities. It can integrate accelerometer inputs to get very fast reactions that, unfortunately, will drift and accumulate an error. It can also get GPS information at a much lower rate which does not react fast but does not drift. A complementary filter can be used to combine the high frequency input from the accelerometer with the low frequency input from the GPS and get the best of both in a single lateral velocity signal.
 
  • #3
Thank you for your fast replay.
Can you recommend me good books on this topic? What professional direction is good in my case?
 
  • #4
debelino said:
Thank you for your fast replay.
Can you recommend me good books on this topic? What professional direction is good in my case?
Unfortunately I am not up to date on current textbooks. I also do not know what your background is. Kalman filters are in many books on optimal control, but it is probably after a lot of preceding material. To get the background material, you can look at your university texts on control laws, multivariate statistics, and optimization.

Just to get an idea of what is involved, you may be interested in https://www.utdallas.edu/~sethi/Prosper/Control-Tex-Chapte13.pdf . It is advanced and I don't know what your background is, so don't worry if it looks intimidating. I am just mentioning it to show you how central statistics is in a field like stochastic optimal control.
 

1. What is the importance of probability theory and statistics in Robotics and ME?

Probability theory and statistics are essential tools for understanding and modeling uncertainty in the fields of Robotics and Mechanical Engineering (ME). In Robotics, probabilistic methods are used to make decisions and control actions in uncertain environments. In ME, statistical analysis is used to analyze data and make predictions about the behavior of systems. These concepts are crucial for designing and developing reliable and efficient robotic and mechanical systems.

2. How are probability theory and statistics applied in Robotics and ME?

In Robotics, probability theory is used to model and make inferences about uncertain inputs, such as sensor measurements and environmental conditions. This allows robots to make decisions and plan actions based on the most likely outcomes. In ME, statistics is used to analyze data from experiments and simulations to understand the behavior of mechanical systems and make predictions about their performance.

3. What are some common applications of probability theory and statistics in Robotics and ME?

Some common applications include: path planning and control for autonomous robots, sensor fusion and localization, fault detection and diagnosis, reliability analysis for mechanical systems, and optimization of design parameters for improved performance.

4. Can you provide an example of how probability theory and statistics are used in Robotics and ME?

Sure, one example is in autonomous vehicles. Probability theory is used to model and predict the behavior of other vehicles and objects in the environment. This information is then used by the vehicle's control system to plan a safe and efficient path. Additionally, statistics is used to analyze data from sensors to improve vehicle localization and perception.

5. What is the difference between probability theory and statistics in the context of Robotics and ME?

Probability theory deals with the mathematical study of uncertainty and the likelihood of events occurring. It provides a framework for making decisions based on uncertain information. Statistics, on the other hand, is concerned with the collection, analysis, and interpretation of data to make inferences and predictions about a population. In Robotics and ME, probability theory is used to model uncertainty, while statistics is used to analyze data and make predictions about system behavior.

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