Certified Autonomous Robotics Engineer (CARE) Certification Program by Tonex
CARE elevates engineers to design, build, and deploy autonomous robotic systems that perceive, decide, and act in complex environments. Participants master SLAM, computer vision, deep reinforcement learning, multi-sensor fusion, and real-time decision pipelines for dependable autonomy at the edge.
The program emphasizes rigorous safety cases, fault tolerance, and mission assurance across defense, manufacturing, and research contexts. Cybersecurity is addressed as a first-class system requirement to harden autonomy stacks and data links against attacks. You learn how cybersecurity influences perception integrity, control resilience, and update pipelines. You also examine governance and secure lifecycle practices that sustain trust in robotic operations.
Learning Objectives
- Apply SLAM, vision, and deep RL to achieve robust autonomy under uncertainty
- Architect multi-sensor fusion pipelines with latency, bandwidth, and accuracy trade-offs
- Design multi-agent coordination and swarms with scalable communication and control
- Implement safety, reliability, and formal assurance techniques for critical missions
- Build edge AI stacks for real-time perception and decision systems
- Integrate cybersecurity principles to protect autonomy, data, and control channels
- Plan verification, validation, and deployment with continuous monitoring
Audience
- Robotics Engineers and Autonomy Developers
- AI and Computer Vision Professionals
- Control and Systems Engineers
- Safety and Reliability Engineers
- Cybersecurity Professionals
- R&D Scientists and Technical Leaders
- Product and Program Managers in Robotics
Course Modules
Module 1: Perception Foundations
- SLAM fundamentals and map quality
- Visual and inertial odometry integration
- 3D sensing with LiDAR and radar
- Feature tracking and data association
- Robustness to lighting and weather
- Calibration and health monitoring
Module 2: Sensor Fusion Design
- Bayesian and factor-graph fusion
- EKF and smoothing architectures
- Time synchronization strategies
- Handling dropouts and drift
- Estimating uncertainty and covariances
- Performance profiling on edge
Module 3: Decision and Control
- Behavior trees and task planners
- Deep RL for goal-directed policies
- Model predictive control for agility
- Constraint handling and safety guards
- Trajectory generation and replans
- Runtime verification hooks
Module 4: Multi-Agent and Swarms
- Distributed consensus and coverage
- Formation control and reconfiguration
- Communication topologies and QoS
- Task allocation and market methods
- Resilience to node failures
- Ethical and rules-of-engagement
Module 5: Safety and Assurance
- Hazard analysis and FMEA
- Redundancy and graceful degradation
- Fault detection and isolation
- Requirements to evidence traceability
- Standards landscape and compliance
- Operational readiness reviews
Module 6: Edge AI Systems
- Model compression and quantization
- Streaming inference and scheduling
- Real-time OS and middleware choices
- Telemetry and observability
- Secure update and rollback
- Supply chain risk management
Exam Domains
- Autonomous Perception and Mapping
- Sensor Fusion and Estimation
- Planning and Control for Autonomy
- Swarm Coordination and Communication
- Safety Engineering and Assurance
- Secure Deployment and Operations
Course Delivery
The course is delivered through a combination of lectures, interactive discussions, guided workshops, and project-based learning, facilitated by experts in the field of Certified Autonomous Robotics Engineer CARE. Participants will have access to online resources, including readings, case studies, and tools for practical exercises.
Assessment and Certification
Participants will be assessed through quizzes, assignments, and a capstone project. Upon successful completion of the course, participants will receive a certificate in Certified Autonomous Robotics Engineer CARE.
Question Types
- Multiple Choice Questions MCQs
- Scenario-based Questions
Passing Criteria
To pass the Certified Autonomous Robotics Engineer CARE Certification Program exam, candidates must achieve a score of 70% or higher.
Advance your impact in autonomy today. Enroll in CARE by Tonex to design safer, smarter, and more secure robotic systems that deliver mission-ready performance.