Practice Algorithms (8.6.2) - Integration of Sensors and Actuators in Robotic Systems

Practice: Algorithms

Lesson 8.6.2Integration of Sensors and Actuators in Robotic Systems

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9 questions

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6 questions · about 4 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
What is the purpose of the Kalman Filter?
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Think about how it helps in measurements.
Question 2.
What type of systems does the Extended Kalman Filter cater to?
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Consider how systems might behave in non-linear contexts.
3Question 3.
What is the primary purpose of the Kalman Filter?
- To track position
- To combine data
- To filter noise
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Think about what is meant by 'noise' in the data.
4Question 4.
True or False: The Extended Kalman Filter can be used for linear systems.
- True
- False
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Consider the primary characteristics of the systems it serves.
Question 5.
Demonstrate how the Extended Kalman Filter can be implemented in an autonomous vehicle to combine data from GPS and LIDAR.
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Consider how each data source contributes to understanding the vehicle's environment.
Question 6.
Design a simple Bayesian Network to model the relationship between different sensor data inputs: temperature, humidity, and pressure.
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Think about how each sensor influences the others and how they collectively impact the outcome.

5 more questions in this section. Enroll free to practise them all.