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Ethics→requirements, clearly documented.
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Req matrix, STPA-lite hazard log, geofence/abort.
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Robust perception with calibration & OOD checks.
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Evaluation kit: scenarios, metrics, model/system cards.
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Human-on-the-loop controls, privacy-preserving sensing.
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Fail-safe comms + logging; coordinated multi-UAS; draft assurance case (tests + SOPs).
AI for autonomous defense drones & surveillance
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Course Overview
Limited SeatsClosing Soon
AI for autonomous defense drones & surveillance
Professional Certification Program
🎥
FormatLive + Recorded
📅
Duration8 Weeks
📜
CertificationVerified
Program Syllabus
Module 1
About This Course
A three-day, hands-on workshop to build ethical, safe, auditable AI for non-weaponized autonomous drones in public-good missions—turning governance and privacy-by-design into requirements and hazard logs, making perception robust with calibration/OOD checks and model cards, and implementing human oversight, safe comms, geofencing, and coordinated multi-UAS behaviors—so you leave with concrete templates, test evidence, and operator SOPs.
Module 2
Aim
Enable participants to design, test, and operate ethical, safe, and auditable AI for non-weaponized autonomous drones, translating policy into concrete technical controls and operator procedures.
Module 3
Workshop Objectives
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Turn governance, ethics, and privacy-by-design into actionable requirements and controls.
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Build a policy-aware requirements matrix, hazard log, and clear geofence/abort criteria.
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Design robust perception with calibration & OOD detection to manage false alarms/misses.
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Create evaluation plans and model/system cards that document limits and safe use.
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Implement privacy-preserving sensing and human-on-the-loop supervision.
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Engineer fail-safe comms, logging/traceability, and coordinated multi-UAS behaviors.
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Compile an assurance case with test evidence and operator SOPs for audits/field trials
Module 4
Workshop Structure
Module 5
📅 Day 1 – Governance, Ethics & Compliance for Autonomous Aerial Systems
- Legal & ethical foundations: meaningful human control, safety-of-life priorities, privacy/civil-liberties by design
- Policy alignment: risk assessments, impact assessments, auditability, documentation (model/system cards)
- Data governance: consent, provenance, bias/representativeness, secure storage & retention limits
- Hands-on: Build a policy-aware requirements matrix and hazard log (e.g., STPA-lite) for a benign aerial monitoring use case (infrastructure inspection), including geofencing and abort criteria
Module 6
📅 Day 2 – Robust Perception & Tracking (Safety-Critical & Non-Targeted)
- Robustness concepts: dataset shift, OOD detection, calibration, false-alarm vs miss trade-offs
- Evaluation & validation: test plans, scenario coverage, interpretability & explanations for operator trust
- Privacy-preserving sensing patterns: minimization, masking/redaction, on-device filtering
- Hands-on: Evaluate a pre-trained detector on a public, non-person dataset; compute calibration/OOD metrics, document results in a model card with limitations and safe-use guidelines
Module 7
📅 Day 3 – Human Oversight, Safe Comms & Multi-Drone Coordination (Benign Missions)
- Human-on-the-loop interfaces: alerts, positive control, rate-limiters, locked-safety modes, no-fly & no-observe zones
- Communications safety: designing fail-safe behaviors for degraded/lost links (no evasion tactics), logging and traceability
- Multi-UAS coordination for public-good scenarios (e.g., search-and-rescue): collision avoidance, deconfliction, fairness of tasking
- Hands-on: Simulate a multi-UAS search-and-rescue mission with strict geofences and lost-link behaviors; produce an assurance case outline (safety case, test evidence, operator SOPs)
Module 8
Who Should Enrol?
- Engineers/ML practitioners, UAS developers, QA/safety, PMs, compliance, public-sector tech.
- Basic Python, core ML (detection/classification), Git/CLI, clear documentation.
- ROS/ArduPilot/DroneSim, STPA/FMEA, model/system cards.
- Commitment to non-weaponized, public-good use; privacy-by-design & human control.
Module 9
Important Dates
Module 10
Registration Ends
11/10/2025
IST 4:30
Module 11
Workshop Dates
11/10/2025 – 11/12/2025
IST 5:30 PM
Module 12
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Workshop Outcomes
Module 14
Fee Structure
Module 15
Student
₹1999 | $60
Module 16
Ph.D. Scholar / Researcher
₹2999 | $70
Module 17
Academician / Faculty
₹3999 | $80
Module 18
Industry Professional
₹5999 | $100
Module 19
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
Module 20
Join Our Hall of Fame!
Take your research to the next level with NanoSchool.
Module 21
Publication Opportunity
Get published in a prestigious open-access journal.
Module 22
Centre of Excellence
Become part of an elite research community.
Module 23
Networking & Learning
Connect with global researchers and mentors.
Module 24
Global Recognition
Worth ₹20,000 / $1,000 in academic value.
Module 25
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Lead Instructor
Dr. Sarah Chen
PhD in Computational Mechanics from MIT with 15+ years of experience in Industrial AI. Former Lead Data Scientist at Tesla and current advisor to Fortune 500 manufacturing firms.
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