Course

Unit I

MEMS and NEMS Sensors (Micro/Nano Electro Mechanical Systems), Smart Sensors (with self-diagnostics, calibration, and communication features), Wireless Sensor Networks (WSN), Biosensors and Chemical Sensors, Optical and Photonic Sensors, Infrared Sensors and LiDAR, Time-of-Flight (ToF) and Ultrasonic Range Finders, Light Detection, Image, and Vision Systems, AI/ML-based Sensor Fusion, 3D Vision Sensors (Structured Light, Stereo Vision, Depth Cameras), IoT-enabled Sensor Systems, Distance Measuring and Proximity Sensors.

Unit II

Force, Torque and Power Measurement, Flow Measurement (Ultrasonic, Magnetic, Coriolis), Advanced Temperature Measurement Systems, Piezoelectric Actuators (Advanced Applications), Hydraulic and Pneumatic Actuation Systems, Magneto strictive Actuators, Shape Memory Alloy (SMA) Actuators, Electroactive Polymer Actuators (EAPs), Soft Actuators and Artificial Muscles, Robotic Grippers and Haptics, Actuators in Wearables and Biomedical Devices, Wireless and Remote-Controlled Actuation Systems.

Unit III

Sensor Fusion (IMU, GPS, Wheel Encoders, Lidar, Vision), SLAM (Simultaneous Localization and Mapping), Obstacle Detection and Avoidance (using Lidar, ToF, Ultrasonic, Vision). Probabilistic Localization (Kalman Filter, Particle Filter), Occupancy Grid Mapping, Visual Odometry, Map-based vs. Map-less Navigation. Global Path Planning (A*, Dijkstra, RRT), Local Path Planning (Dynamic Window Approach, Potential Fields), Trajectory Generation and Following, Reactive and Behavior-based Navigation.

Learning Objectives

LO1: Understand and classify modern sensing technologies, including MEMS/NEMS, vision
           systems, LiDAR, and AI-enabled sensors.

LO2: Explore advanced actuation systems including soft actuators, EAPs, SMA-based
           systems, and their applications in robotics and wearables.

LO3: Learn principles of sensor fusion, SLAM, and localization for autonomous robotic
          navigation.

LO4: Study path planning and navigation techniques for intelligent and behavior-based mobile
           robots.

Course Outcomes

CO1: Evaluate and integrate advanced sensor technologies for intelligent robotic systems.

CO2: Analyze and apply advanced actuation mechanisms including smart materials and soft
          actuators in real-world robotic and biomedical systems.

CO3: Implement sensor fusion strategies for SLAM, localization, and mapping using
          probabilistic filters and multiple sensor modalities.

CO4: Design and simulate global and local path planning algorithms for autonomous robot
          navigation in structured and unstructured environments.

CO-PO Mapping                                    

Textbooks:

1. Robert H. Bishop (2017) Mechatronic Systems, Sensors, and Actuators: Fundamentals and Modeling; The Mechatronics Handbook, Second Edition
2. J. Russell and P. Norvig. Artificial Intelligence: A Modern Approach (3rd edition), Prentice-Hall, 2010.
3. Aurélien Géron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, Second Edition, O’Reilly Media, 2019.

Reference

4. Clarence W. de Silva (2015) Sensors and Actuators: Engineering System Instrumentation, Second Edition
5. Andrzej M Pawlak (2006) Sensors and Actuators in Mechatronics: Design and Applications
6. K.Bhattacharya, Control System Engineering, 3rd Edition, Pearson, 2013.
7. C.Kuo, ?Automatic control systems?, Prentice Hall of India, 7th Edition,1995.
8. R Beale & T Jackson, “Neural Computing, An Introduction”, Adam Hilger, 1990.