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AKTU · B.Tech · 5th semester

AKTU Intelligent Systems and Robotics (BEC053) Question Paper 2024-25

UniversityDr. A.P.J. Abdul Kalam Technical University (AKTU), Lucknow
CourseB.Tech, Semester V
SubjectIntelligent Systems and Robotics (BEC053)
ExamTheory Examination 2024-25 (odd semester)
Time · Marks3 hours · 70 marks
SourceAKTU question paper, from the ABESIT question paper bank
View original PDF Other years

Note: Attempt all Sections. In case of any missing data; choose suitably.

Section A

Q1. Attempt all questions in brief · 2 × 7 = 14 marks

  1. Differentiate between mobile ground robots and uninhabited air vehicles.
  2. Define stability in the context of control systems.
  3. Define fuzzy logic in the context of robotics.
  4. Define Bayesian belief networks and their application.
  5. Differentiate between genetic algorithms and simulated annealing.
  6. Define a feed-forward network and its application.
  7. Explain decision tree.

Section B

Q2. Attempt any three of the following · 7 × 3 = 21 marks

  1. Compare and contrast mobile ground robots with uninhabited air vehicles, discussing their design and control challenges.
  2. Explain probabilistic path planning. Explain its process and advantages in robot navigation.
  3. Explain the role of probability and error models in measurement systems? Explain with examples.
  4. Explain decision trees structure and use in classification problems with examples.
  5. Compare genetic algorithms, simulated annealing, and particle swarm optimization in terms of their methodologies and applications.

Section C

Q3

Attempt any one part of the following · 7 × 1 = 7 marks

  1. Discuss the biological and cognitive paradigms used in robot design.
  2. Explain the Denavit-Hartenberg transformation in detail with an example for a two-joint robotic arm.

Q4

Attempt any one part of the following · 7 × 1 = 7 marks

  1. Explain the principles of open-loop and closed-loop control systems with examples from robotics.
  2. Explain the significance of stability and performance analysis in control systems.

Q5

Attempt any one part of the following · 7 × 1 = 7 marks

  1. Explain the Extended Kalman Filter and Particle Filter techniques with applications in robotics.
  2. Explain the role of sensors in robotics. Explain the different types of sensors and their integration in sensor-based estimation.

Q6

Attempt any one part of the following · 7 × 1 = 7 marks

  1. Discuss Bayesian belief networks and their application in robot decision- making.
  2. Explain the process of task planning for individual and multiple agents in robotics. Provide examples.

Q7

Attempt any one part of the following · 7 × 1 = 7 marks

  1. Explain forward chaining and backward chaining with examples of their use in robotics.
  2. Discuss the implementation of deep-learning algorithms in robotics with specific examples of applications.

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