코스 개요

Introduction to Safety and Explainability in Robotics

  • Overview of safety and transparency in robotic systems
  • Regulatory and ethical context for robotics and AI
  • Standards and frameworks: ISO 26262, ISO 10218, and ISO/IEC 42001

Risk and Hazard Analysis

  • Identifying hazards in autonomous and semi-autonomous systems
  • Performing Failure Mode and Effects Analysis (FMEA)
  • Quantifying risk and mitigation through safety design

Verification and Validation Techniques

  • Testing robotic behaviors in simulated environments
  • Formal verification and test case design
  • Data-driven validation and monitoring techniques

Safety Case Development

  • Structure and content of a safety case
  • Documenting compliance and traceability
  • Using tools for evidence management and risk justification

Explainable AI for Robotics

  • Making decision-making processes transparent
  • Interpretability techniques for ML-based control systems
  • Explaining robotic behaviors to users and regulators

Ethical and Governance Considerations

  • Ethical principles in robotics and autonomous systems
  • Bias, accountability, and responsibility in AI-driven robotics
  • Balancing innovation with public trust and regulation

Hands-On Workshop: Building a Safe and Explainable Robotics Scenario

  • Designing a small robotic simulation in ROS 2 or Gazebo
  • Applying verification and validation procedures
  • Developing and presenting a safety case summary

Summary and Next Steps

요건

  • Basic understanding of robotics systems and control architectures
  • Familiarity with Python programming and simulation tools
  • Knowledge of system engineering or safety processes

Audience

  • System engineers working on robotics or autonomous systems
  • Safety officers ensuring compliance with functional safety standards
  • Technical managers overseeing robotics integration and deployment
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