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| en:safeav:curriculum:hw-b [2025/10/20 14:07] – [Table] larisas | en:safeav:curriculum:hw-b [2025/11/05 08:57] (current) – airi |
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| ====== Module: Hardware and Sensing Technologies (Part1) ====== | ====== Module: Hardware and Sensing Technologies (Part 1) ====== |
| | **Study level** | Bachelor || | |
| | **ECTS credits** | 1 ECTS || | ^ **Study level** | Bachelor | |
| | **Study forms** | Hybrid or fully online || | ^ **ECTS credits** | 1 ECTS | |
| | **Module aims** | Provide a practical foundation in embedded protocols, sensing hardware, and navigation/positioning for autonomous systems. Students will learn how sensors (IMU, GNSS, LiDAR/camera), actuators and power systems connect to embedded computing units via standard buses (I²C, SPI, UART, CAN/CAN‑FD, Ethernet/DDS), how data moves deterministically across the system, and how to calibrate, time‑synchronise and validate multi‑sensor setups. Emphasis is placed on interface compatibility, thermal/power constraints, EMC considerations and the integration lifecycle (from requirements to HIL testing) that turns components into a reliable platform. || | ^ **Study forms** | Hybrid or fully online | |
| | **Pre-requirements** | Basic electronics (Ohm’s law, signals, voltage/current levels), programming fundamentals (preferably C/C++ or Python), and introductory control/linear algebra (vectors, matrices). Ability to use a Linux-based toolchain and Git is beneficial. Prior exposure to microcontrollers or SBCs (e.g., STM32, Arduino, Raspberry Pi, Jetson) is helpful but not mandatory. || | ^ **Module aims** | The aim of the module is to provide a practical foundation in sensing hardware, embedded communication and navigation/positioning for autonomous systems. The course develops students’ ability to design, integrate and validate multi-sensor and actuator setups on embedded platforms, taking into account interface compatibility, timing, power and electromagnetic constraints to build reliable autonomy-ready platforms.| |
| | **Learning outcomes** | Knowledge:\\ • Explain operating principles and specs of common sensors (IMU, GNSS, range and vision sensors) and actuators.\\ • Describe embedded communication protocols (I²C, SPI, UART, CAN/CAN‑FD, Ethernet, DDS) and timing/synchronisation concepts.\\ • Outline the hardware integration lifecycle, calibration methods, environmental/EMC testing, and safety/quality standards.\\ Skills:\\ • Select appropriate sensors/computing units for a given task and justify trade‑offs of accuracy, latency, power and cost.\\ • Configure and bring up device buses, log and interpret sensor data, and perform basic multi‑sensor calibration.\\ • Build a minimal HIL test to validate a perception/control loop and document results.\\ Understanding/Attitudes:\\ • Recognize integration risks (interface incompatibility, EMI, thermal/power limits) and propose mitigations.\\ • Appreciate supply chain constraints and obsolescence planning when choosing components.\\ • Work safely, ethically and reproducibly, documenting configurations and changes. || | ^ **Pre-requirements** | Basic knowledge of electronics and programming, as well as introductory control and linear algebra. Ability to work with Linux-based tools and version control is beneficial, while prior experience with microcontrollers or single-board computers is recommended but not mandatory. | |
| | ** Topics ** | Sensors, Computing Units, and Navigation Systems:\\ • Sensor taxonomy and specs (IMU, GNSS, magnetometer, LiDAR, depth, camera); calibration (extrinsics/IMU alignment).\\ • Embedded computing: MCUs vs. SoCs (CPU/GPU/accelerators), power/thermal design, memory and I/O.\\ • Navigation and positioning: GNSS/IMU basics, odometry, sensor fusion concepts.\\ Embedded Protocols and Communication Backbones:\\ • I²C/SPI/UART fundamentals; CAN/CAN‑FD; Ethernet, TSN concepts; DDS/ROS 2 communications.\\ Integration Lifecycle and Reliability:\\ • Requirements → interface design → assembly → HIL/SIL → environmental & EMC testing; timing/synchronisation; redundancy.\\ Supply Chain & Lifecycle Considerations:\\ • Component availability, quality/traceability, cybersecurity (SBOM/firmware signing), and obsolescence planning. || | ^ **Learning outcomes** | **Knowledge**\\ • Explain operating principles and specs of common sensors and actuators.\\ • Describe embedded communication protocols and timing/synchronisation concepts.\\ • Outline the hardware integration lifecycle, calibration methods, environmental/EMC testing, and safety/quality standards.\\ **Skills**\\ • Select appropriate sensors/computing units for a given task and justify trade-offs of accuracy, latency, power and cost.\\ • Configure and bring up device buses, log and interpret sensor data, and perform basic multi-sensor calibration.\\ • Build a minimal HIL test to validate a perception/control loop and document results.\\ **Understanding**\\ • Recognize integration risks and propose mitigations.\\ • Appreciate supply chain constraints and obsolescence planning when choosing components.\\ • Work safely, ethically and reproducibly, documenting configurations and changes. | |
| | **Type of assessment** | The prerequisite of a positive grade is a positive evaluation of module topics and presentation of practical work results with required documentation || | ^ **Topics** | 1. Sensors, Computing Units, and Navigation Systems:\\ — Sensor taxonomy and specs (IMU, GNSS, magnetometer, LiDAR, depth, camera); calibration (extrinsics/IMU alignment).\\ — Embedded computing: MCUs vs. SoCs (CPU/GPU/accelerators), power/thermal design, memory and I/O.\\ — Navigation and positioning: GNSS/IMU basics, odometry, sensor fusion concepts.\\ 2. Embedded Protocols and Communication Backbones:\\ — I²C/SPI/UART fundamentals; CAN/CAN-FD; Ethernet, TSN concepts; DDS/ROS2 communications.\\ 3. Integration Lifecycle and Reliability:\\ — Requirements → interface design → assembly → HIL/SIL → environmental & EMC testing; timing/synchronisation; redundancy.\\ 4. Supply Chain & Lifecycle Considerations:\\ — Component availability, quality/traceability, cybersecurity (SBOM/firmware signing), and obsolescence planning. | |
| | **Learning methods** | Lecture: Concept overviews with worked hardware schematics and bus timing examples.\\ Lab works: Hands‑on bring‑up of sensors and a microcontroller/SBC, bus sniffing, timestamping and calibration; mini HIL demo.\\ Individual assignments: Short design/calculation tasks (component selection, interface budgets) with a brief technical note.\\ Self‑learning: Curated readings and datasheets; recommended MOOC videos to reinforce embedded and navigation concepts. || | ^ **Type of assessment** | The prerequisite of a positive grade is a positive evaluation of module topics and presentation of practical work results with required documentation | |
| | **AI involvement** | Yes — assisted code scaffolding and debugging, log summarisation, data analysis/visualisation and literature search support. Students must verify outputs, cite use of AI tools, and avoid uploading proprietary or assessment‑sensitive data. | | | ^ **Learning methods** | **Lecture** — Concept overviews with worked hardware schematics and bus timing examples.\\ **Lab works** — Hands-on bring-up of sensors and a microcontroller/SBC, bus sniffing, timestamping and calibration; mini HIL demo.\\ **Individual assignments** — Short design/calculation tasks (component selection, interface budgets) with a brief technical note.\\ **Self-learning** — Curated readings and datasheets; recommended MOOC videos to reinforce embedded and navigation concepts. | |
| | **References to\\ literature** | Lee, E. A., & Seshia, S. A. (2020). Introduction to Embedded Systems (3rd ed.). MIT Press.\\ Kopetz, H. (2011). Real‑Time Systems: Design Principles for Distributed Embedded Applications (2nd ed.). Springer.\\ Isermann, R. (2017). Mechatronic Systems: Fundamentals. Springer.\\ Raj, A., & Saxena, P. (2022). Emerging trends in autonomous systems hardware integration. IEEE Access, 10.\\ Thrun, S. (2010). Toward robotic cars. Communications of the ACM, 53(4).\\ NIST SP 800‑161 (2020). Supply Chain Risk Management Practices for Federal Information Systems and Organizations. || | ^ **AI involvement** | Assisted code scaffolding and debugging, log summarisation, data analysis/visualisation and literature search support. Students must verify outputs, cite use of AI tools, and avoid uploading proprietary or assessment-sensitive data. | |
| | **Lab equipment** | Yes || | ^ **Recommended tools and environments** | STM32 or similar MCU development boards, Raspberry Pi / NVIDIA Jetson, typical sensors (IMU, GNSS, LiDAR, camera), CAN bus and logic analyzers, ROS2-based logging | |
| | **Virtual lab** | Yes || | ^ **Verification and Validation focus** | | |
| | **MOOC course** | || | ^ **Relevant standards and regulatory frameworks** | ISO 26262, ISO 11452 / CISPR 25 / ISO 7637, ISO 16750, CAN | |
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