Length: 2 Days
The Autonomous Systems Certification Course by Tonex equips participants with the skills and knowledge necessary to design and develop AI solutions for autonomous vehicles, drones, and other robotic systems. This comprehensive program covers the latest advancements and best practices in the field, preparing individuals to tackle the challenges of creating intelligent autonomous systems.
Learning Objectives:
- Gain a deep understanding of AI algorithms and techniques relevant to autonomous systems.
- Learn to design and develop software architectures for autonomous vehicles, drones, and robotic systems.
- Master sensor integration and perception algorithms for environment understanding.
- Explore decision-making algorithms and strategies for autonomous navigation and control.
- Understand safety and regulatory considerations in the development of autonomous systems.
- Acquire hands-on experience through practical exercises and case studies.
Audience: Professionals and enthusiasts seeking expertise in designing and developing AI solutions for autonomous vehicles, drones, and robotic systems. This course is ideal for engineers, researchers, and developers looking to advance their careers in the rapidly evolving field of autonomous systems.
Course Outline:
Module 1: Introduction to Autonomous Systems
- Evolution of Autonomous Systems
- Applications and Use Cases
- Key Components of Autonomous Systems
- Challenges and Opportunities
- Ethical and Social Implications
- Future Trends
Module 2: AI Algorithms for Perception and Decision Making
- Machine Learning Basics
- Computer Vision Techniques
- Sensor Fusion Methods
- Reinforcement Learning
- Planning and Pathfinding Algorithms
- Behavior-based Approaches
Module 3: Software Architecture Design for Autonomous Systems
- Layered Architecture
- Modular Design Principles
- Real-time Operating Systems (RTOS)
- Communication Protocols
- Simulation and Testing Environments
- Software Development Tools
Module 4: Sensor Integration and Environmental Understanding
- Types of Sensors Used in Autonomous Systems
- Sensor Calibration Techniques
- Object Detection and Tracking
- Localization and Mapping (SLAM)
- Data Fusion Strategies
- Environmental Modeling
Module 5: Navigation and Control Strategies for Autonomous Vehicles
- PID Control
- Model Predictive Control (MPC)
- Trajectory Planning
- Obstacle Avoidance Techniques
- Vehicle Dynamics and Kinematics
- Human-Machine Interaction
Module 6: Safety and Regulatory Considerations in Autonomous Systems Development
- Safety Standards and Regulations
- Functional Safety (ISO 26262)
- Cybersecurity for Autonomous Systems
- Fail-safe Mechanisms
- Ethical Decision Making in AI
- Liability and Insurance Issues