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Reading time: 9 minutes
What You Will Learn
- You will learn how artificial intelligence enables drones to navigate obstacles, perform precision landings, and execute complex maneuvers with minimal human input. This innovation enhances efficiency, reduces human error, and expands drone capabilities across various industries.
- You will learn why Beyond Visual Line of Sight (BVLOS) drone flights require advanced AI-powered systems like detect-and-avoid (DAA) and computer vision to ensure safe and efficient navigation. Reliable connectivity solutions, such as Elsight’s Halo, play a crucial role in maintaining stable data transmission and meeting regulatory requirements.
- You will learn how drones with limited size, weight, and power (SWaP) budgets can still leverage AI by offloading data processing to cloud and edge computing services. This approach allows real-time AI analysis while maintaining lightweight and efficient drone designs.
- You will learn why seamless communication is essential for AI-powered drone operations, especially in BVLOS scenarios. By using solutions like Elsight’s Halo, drones can combine multiple cellular networks to ensure uninterrupted connectivity, improving operational reliability and safety.
The use of commercial drones and the value of their markets are both continually on the rise, with a number of different sources forecasting the global drone industry to surpass $50 billion this decade. In order to continue the increase of these levels of innovation and scale, operating models need to move beyond the standard paradigm of a single drone operated by a single pilot within VLOS (visual line of sight).
One way to drive this innovation is the use of artificial intelligence (AI). Once considered the domain of science fiction, AI has benefited from recent improvements in processing technology and miniaturization and is now ideal for providing drones with enhanced autonomy as well as advanced intelligent data analysis.
The application of AI can allow drones to achieve results quicker and more accurately than pilots and operators are able to and pave the way for taking humans out of the loop altogether. Besides the increase in efficiency and cost-effectiveness that this will bring, AI could also enhance the safety of unmanned aviation by removing human error and operator fatigue from the equation.
Implement AI-powered detect-and-avoid (DAA) systems to enhance safety, reduce human error, and enable autonomous drone flights, particularly for BVLOS missions.
Table of Contents
AI vs machine learning vs computer vision
Before we explore the uses of AI for commercial drones further, it is worth looking at some of the terminologies that are often used adjacent to AI, and how these concepts differ technically.
Artificial intelligence is a highly broad set of technologies that allow computers and robotics to simulate aspects of human intelligence such as learning and problem-solving. AI encompasses a variety of different approaches and algorithms that allow a computer to utilize information and make intelligent decisions.
Machine learning (ML) is a particular subset of AI and one of its most popular applications. It uses algorithms such as neural networks to allow a computer to learn from provided information and the environment around it and solve problems without the need for direct instruction. Unlike traditional software algorithms, machine learning algorithms are usually designed to improve over time with exposure to new data.
Going down another level, computer vision (CV) is an application of machine learning. It allows computer systems to analyze image and video data, spot patterns and extract information, and make decisions based on these results. Computer vision-based object recognition, identification, and detection are highly useful applications for a variety of autonomous drone operations, including navigation and detect-and-avoid. These tasks can also be used for enhanced data processing in a number of specific market segments such as utility drone inspection, precision agriculture, and photogrammetry.
Ensure your drone’s AI functions smoothly by integrating a robust connectivity solution like Elsight’s Halo, which aggregates multiple cellular networks to maintain stable data transmission.
AI for BVLOS and autonomous operations
In order to scale up to a new level of commercial viability, drone operations need to advance to BVLOS (beyond visual line of sight) flight. Such operations are highly regulated by most aviation authorities around the world, including the FAA and EASA, and require lengthy certification processes during which foolproof safety and reliability systems must be demonstrated.
AI and computer vision can help provide some of these essential capabilities. One of the most critical technologies for BVLOS is detect-and-avoid (DAA), which enables drones to detect obstacles and other hazards in the environment and autonomously maneuver to avoid collisions. Computer vision models can be applied to camera feeds that continuously monitor the surrounding airspace in real-time and trained to pick out other drones and manned aircraft, birds, powerlines and a variety of other potential hazards.
AI can also allow drones to make safe landings with improved accuracy, as well as emergency landings in the case of unforeseen circumstances. Combining object recognition with other inputs such as GPS receivers and wind sensors, AI-based algorithms can select a suitable landing site and direct the flight computer to adjust the drone’s trajectory and speed to achieve the safest possible touchdown.
In addition to landing, other precision maneuvers may also benefit from the application of AI drone technology. Similar object detection and precise positioning algorithms could be applied to drone delivery, which may need to take into account the environment around the selected drop zone and ensure that the cargo is released without any risk to people, property or the package itself.
If your drone has limited onboard computing power, consider offloading AI data analysis to cloud or edge computing services to minimize SWaP constraints while maintaining real-time performance.
AI applications: A solution for drones on a lower SWaP budget
While some AI applications only require post-mission processing, others, such as object tracking, need data to be analyzed during flight in or close to real-time. Such capabilities come at a cost, as onboard embedded systems that can handle this complex processing are typically highly compute-intensive and can vastly increase the SWaP (size, weight, and power) requirements of the drone.
One way to allow drones with lower SWaP budgets to still take advantage of AI is to offload the processing to cloud servers and edge computing services. Many regions covered by 4G LTE and 5G cellular networks will provide the necessary bandwidth and latency for this approach to be successful.
If you are designing a drone system that uses AI and computer vision to facilitate BVLOS operations, your cellular communications link needs to be rock-solid in order to persuade regulators that your platform is safe to fly. This is where Elsight’s Halo comes in.
Halo is a compact and lightweight connectivity solution that provides maximum connection confidence for BVLOS UAS drones. Using advanced bonding technology, it can aggregate bandwidth from up to four separate cellular connections into one secure datalink. It also provides automatic traffic balancing that adapts to the dynamic needs of any drone mission and allows the communications system to switch to a backup link in case of network coverage issues, providing maximum uptime and reliability for your aircraft.
Stay ahead of evolving BVLOS regulations by investing in AI-driven automation and redundant communication systems that meet aviation safety standards and improve operational reliability.
To find out how Halo can be an asset to your AI-powered or data-intensive commercial drone platform, please get in touch to find out more.
Key Takeaways
- Artificial intelligence enables drones to perform complex tasks like obstacle detection, precision landing, and hazard avoidance with minimal human intervention. This advancement improves efficiency, safety, and scalability for commercial drone operations.
- Beyond Visual Line of Sight (BVLOS) flights depend on AI-powered systems like detect-and-avoid (DAA) and computer vision for real-time navigation and obstacle avoidance. Reliable connectivity solutions, such as Elsight’s Halo, are essential to ensure uninterrupted data transmission and regulatory compliance.
- While AI applications demand high computing power, drones with lower size, weight, and power (SWaP) budgets can offload processing to cloud or edge computing services. Advanced cellular networks like 4G LTE and 5G provide the necessary bandwidth to support real-time AI data analysis during flight.
FAQs
1. What is detect-and-avoid (DAA) and how does it enable safe BVLOS operations?
Detect-and-avoid (DAA) systems enable drones to identify, track, and avoid other aircraft or obstacles without a pilot’s direct visual line of sight. They combine sensors, AI, and flight logic to support autonomous decision-making and safe separation in shared airspace. DAA is considered a core enabling capability for scalable BVLOS operations.
2. Can small drones run AI onboard, or do they need to offload processing to the cloud or edge?
Many small drones can run lightweight AI onboard for tasks such as navigation, object detection, and obstacle avoidance. However, advanced AI workloads often exceed the drone’s SWaP limits and are offloaded to edge or cloud infrastructure. Reliable low-latency connectivity is therefore critical for real-time AI-assisted BVLOS missions.
3. What connectivity, latency, and bandwidth requirements are needed for real-time AI and BVLOS missions?
AI enabled BVLOS missions require continuous, low latency, high reliability links for C2 and telemetry, plus sufficient uplink bandwidth for video and sensor streams used in real time analytics. As a rule of thumb, C2/DAA links target single to low double digit millisecond latency with guaranteed reliability, while high resolution video can demand from hundreds of kbps to multiple Mbps per stream; edge processing and quality of service prioritize safety traffic to reduce bandwidth needs.
4. How does Elsight’s Halo improve connection reliability for AI-powered drones?
Halo aggregates multiple communication links, including cellular, satellite, and other RF links, into a single logical connection, continuously monitoring link quality and seamlessly rerouting traffic in real time. Its multilink bonding and dynamic routing across the best-performing networks maintain uninterrupted connectivity, improving resilience for AI-powered BVLOS operations.
5. What regulatory or certification steps are typically required to operate BVLOS drones with AI systems?
Operators must demonstrate safe, reliable BVLOS operations through a documented safety case that includes hazard/risk assessments, operational procedures, and validated command and control performance. Regulators now expect specific evidence for AI assisted functions (for example, DAA, autonomy, or perception stacks), such as test data, performance metrics across edge cases, validation in representative environments, and continuous monitoring plans. Depending on scope and risk, approvals can include BVLOS waivers or operational authorizations, and may extend to airworthiness assessments and supplemental or type certification processes that cover the AI components and their integration.
