Introduction

The escalating volume of high-resolution data generated by modern drones – from 4K video and LiDAR point clouds to hyperspectral imagery – presents a significant challenge for traditional cloud-based processing models. Transmitting vast datasets wirelessly to remote servers for analysis introduces latency, bandwidth limitations, and privacy concerns, hindering real-time decision-making in critical applications like autonomous navigation, immediate anomaly detection, and rapid response. This is where edge computing and onboard processing become indispensable. By empowering drones with localized computational power, data can be processed, analyzed, and acted upon at the source, dramatically reducing latency, enhancing efficiency, and enabling true autonomy. This essential training course focuses on Edge Computing & Onboard Processing for Drones, equipping professionals to design and implement intelligent, high-performance aerial systems that revolutionize real-time data exploitation.

This intensive training course delves into the core principles and practical implementation of edge computing architectures tailored specifically for drone platforms. Participants will gain hands-on experience with selecting and configuring onboard processing hardware, optimizing data pipelines for real-time analytics, and deploying machine learning models at the edge. We will explore topics such as sensor data fusion, efficient algorithms for onboard processing, and secure communication strategies within distributed drone networks. By mastering the intricate balance of computational power, energy efficiency, and connectivity at the edge, you will be prepared to develop cutting-edge autonomous drone solutions that perform complex tasks with unprecedented speed, reliability, and local intelligence.

Target Audience

• Robotics Engineers & Developers
• Autonomous Systems Researchers
• Embedded Systems Engineers
• AI/ML Engineers working with UAVs
• Drone System Architects
• IoT Engineers
• Software Engineers with Drone Experience
• Data Scientists working with real-time sensor data

Course Objectives

• Understand the fundamental concepts of edge computing and its relevance to drones.
• Learn the benefits and challenges of onboard data processing compared to cloud processing.
• Identify suitable edge computing hardware platforms for drone integration.
• Master techniques for optimizing data pipelines for real-time onboard analysis.
• Implement machine learning models directly on drone hardware (edge AI).
• Understand sensor fusion and data reduction strategies for efficient onboard processing.
• Develop secure and efficient communication protocols for edge-enabled drones.
• Explore the role of 5G/6G in enhancing edge computing capabilities for drones.
• Learn to troubleshoot and optimize onboard processing performance and power consumption.
• Design and prototype edge computing solutions for specific drone applications.
• Understand the future trends and ethical considerations of highly autonomous edge-enabled drones.

DURATION

10 Days

COURSE CONTENT

Module 1: Introduction to Edge Computing and Drones

• Defining edge computing and its distinction from cloud computing.
• Why edge computing is critical for drone applications.
• Benefits of onboard processing: reduced latency, lower bandwidth, enhanced security.
• Challenges of edge computing on drones: limited resources, power constraints.
• Overview of current edge-enabled drone applications.

Module 2: Drone Data & Processing Demands

• Types of high-volume drone data: 4K video, LiDAR point clouds, hyperspectral imagery.
• The need for real-time data analytics in critical drone missions.
• Data size, velocity, and variety challenges for traditional processing.
• Latency issues in cloud-based drone data processing.
• Scenarios requiring immediate onboard decision-making.

Module 3: Onboard Processing Hardware for Drones

• Introduction to microcontrollers, FPGAs, and GPUs for edge processing.
• Single Board Computers (SBCs) suitable for drone integration (e.g., NVIDIA Jetson, Raspberry Pi).
• Considerations for power consumption, weight, and size of onboard compute units.
• Hardware accelerators for AI/ML inference at the edge.
• Selecting the right onboard processor for specific computational tasks.

Module 4: Operating Systems and Software Frameworks for Edge Drones

• Lightweight operating systems for embedded drone systems.
• Introduction to ROS (Robot Operating System) for onboard robotics.
• Software development kits (SDKs) for onboard processing and drone control.
• Containerization (e.g., Docker) for deploying applications on the edge.
• Middleware and communication protocols for onboard data flow.

Module 5: Real-time Data Pipelines and Optimization

• Designing efficient data acquisition and transfer pipelines on the drone.
• Data compression and reduction techniques for limited bandwidth.
• Stream processing algorithms for real-time analytics onboard.
• Managing data storage and buffering on edge devices.
• Optimizing code for resource-constrained onboard processors.

Module 6: Edge AI for Drones: Onboard Machine Learning

• Principles of deploying machine learning models at the edge.
• TinyML and lightweight AI models for onboard inference.
• Converting and optimizing AI models for edge hardware.
• Computer vision applications (object detection, classification) directly on the drone.
• Real-time anomaly detection and predictive analytics onboard.

Module 7: Sensor Fusion at the Edge

• Combining data from multiple onboard sensors (RGB, thermal, LiDAR, IMU) for richer insights.
• Kalman filters and advanced sensor fusion algorithms for accurate state estimation.
• Onboard localization and mapping (SLAM) using fused sensor data.
• Improving navigation and perception through real-time sensor integration.
• Benefits of fused data for robust decision-making.

Module 8: Secure Edge Communication for Drones

• Wireless communication protocols for drone-to-ground and drone-to-drone links.
• Data encryption and authentication for secure edge processing.
• Mitigating cybersecurity risks at the edge.
• Role of 5G/6G networks in enabling high-bandwidth, low-latency edge communication.
• Designing resilient communication architectures for distributed drone intelligence.

Module 9: Power Management and Energy Efficiency

• Strategies for optimizing power consumption of onboard processors.
• Low-power modes and dynamic voltage/frequency scaling.
• Impact of computational load on drone battery life.
• Energy harvesting and alternative power sources for extended endurance.
• Designing power-efficient edge computing solutions.

Module 10: Edge-to-Cloud Continuum for Drones

• Understanding the balance between onboard, edge, and cloud processing.
• When to process data at the edge, near the edge, or in the cloud.
• Data offloading strategies to cloud for long-term storage and complex analysis.
• Hybrid architectures for scalable and flexible drone deployments.
• Managing data synchronization and consistency across the continuum.

Module 11: Future Trends & Edge Drone Applications

• AI-powered autonomous navigation with onboard processing.
• Real-time 3D reconstruction and digital twin creation at the edge.
• Drone swarms leveraging distributed edge intelligence.
• Edge computing for urban air mobility (UAM).
• Ethical considerations and societal impact of highly autonomous edge drones.

Module 12: Case Studies in Edge-Enabled Drone Deployments

• Real-world examples of drones performing onboard inspections for powerlines.
• Edge AI for real-time crop disease detection in agriculture.
• Autonomous delivery drones with onboard obstacle avoidance.
• Disaster response drones performing real-time damage assessment.
• Security and surveillance drones with onboard facial recognition/object tracking.

Module 13: Project & Optimization Techniques

• Hands-on project: Implementing a simple edge AI application on a drone simulator or hardware.
• Performance tuning for onboard processing algorithms.
• Debugging edge computing issues on drone platforms.
• Measuring latency, throughput, and power consumption.
• Strategies for continuous improvement and updating edge drone systems.

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Training Venue

The training will be held at our Skills for Africa Training Institute Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Skills for Africa Training Institute certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Terms of Payment: Unless otherwise agreed between the two parties’ payment of the course fee should be done 10 working days before commencement of the training.