ai border security

Using AI-Enabled Drones for Border Security: Key Considerations

Patrolling, land surveying, topography mapping, search and rescue missions — drones have become a staple tool for the AI border security forces. The EU border agency Frontex deployed drones successfully in Greece and Malta to monitor illegal migration. In 2023, the agency spent another €144 million on new drone purchases to support wider deployments. The US Department of Homeland Security also purchased over $1.8 million of drones for border patrolling last year. 

Compact, cost-effective, and easily deployable, drones are also becoming more sophisticated, featuring pre-trained AI models for navigation, object detection, and land surveying. 

Combining Drones and AI for Border Security 

Drones offer many advantages to AI border security patrol — rapid coverage of wider areas, more effective maritime monitoring, and greater cost-efficiency compared to deploying helicopter flights. The new generation of AI-powered drones is also capable of supporting even more advanced security and surveying use cases. 

ai border security drone

Navigation in GPS-Denied Environments

Border patrolling drones are an effective deterrent to criminals contemplating smuggling operations or illegal border crossings. On the downside, drones are prone to signal jamming. Anti-drone guns can be purchased for several hundred dollars online, often without any ID required. Criminals are often taking advantage of that. Jamming is also a major issue during military conflicts or security training operations conducted in hostile environments. 

US manufacturer Bavovna developed an AI-powered navigation system, for effectively operating in GPS-denied environments. The system combines modular hardware and AI technology to deliver precise and dependable navigation even in GPS-denied environments amidst aggressive electronic warfare scenarios.

By combining ES capabilities with Sensor Fusion AI navigation, Bavovna’s solution ensures dependable drone operations even under heavy signal interference, jamming, and meaconing.  Pre-trained on real flight logs in GNSS-enabled and denied environments, the onboard AI system can navigate the drone along the pre-programmed flight plan using real-time sensor fusion. During recent field tests featuring a Radiobird Defender VTOL, Babovna’s AI navigation system helped pilot over 18 miles (30 km) of complex positioning without GNSS connectivity with an end-point positioning error of 0.42 – 0.63%. 

Moreover, Bavovna recently presented a new EW-resilient, low-SWAP signal intelligence (SIGINT) capture solution. The drone-mounted system can detect the location of radio-emitted sources in a range from 500MHz to 12GHz during a fully autonomous flight, even in GPS-denied environments. The system can provide coastal guards with greater situational awareness, enabling the detection of communication between the mainland and unauthorized boat traffic. 

Automated Patrolling and Intruder Detection

The latest drones performing AI border security functions are equipped with advanced computer vision systems and thermal cameras for a better object, vehicle, and human detection, even in poor lighting conditions. The pre-trained computer vision algorithms can detect heat signatures of people with high accuracy, enabling faster response from security forces. 

ai drone patrolling

For example, the Skydio X10 drone is pre-trained to detect and auto-follow people, vehicles, and other objects, allowing border officers to concentrate their attention in the right areas. Skydio NightSense algorithm also helps navigate drones more effectively in low and no-light conditions. Onboard sensors, paired with AI navigation algorithms, also support automatic obstacle detection and collision prevention. The algorithm also automatically reduces obstacle avoidance margins when piloting through narrow spaces — a handy feature for running environment scans indoors. 

Land Surveying and Mapping 

Drones as AI border security also help border forces collect geospatial data to map the border terrain better and inspect the current infrastructure in place (if any) for a fraction of the cost of chartered flights. 

Details scans help determine new reinforcement areas, close gaps, and ensure all protective infrastructure is in good shape without conducting on-foot inspections. 


Anafi AI from Parrot has a survey-grade accuracy of 0.46cm/px GSD at 100ft (30m), thanks to a 48 MP 1/2” CMOS sensor. Developed primarily for photogrammetry, Anafi AI includes several AI-assisted flight modes for creating (and flying) 3D asset scanning missions. During each flight, the AI technology continuously builds and updates an occupancy grid to ensure safer navigation. The algorithms also determine the best flight trajectory to avoid obstacles. 

Using AI-powered drones responsibly in border operations

Although drones can substantially increase border safety, their usage also raises some rightful concerns about privacy. 

The technology can be abused by law enforcement and used outside the authorized areas. 

A 2020 NGO investigation found that US border patrol drones were found flying miles away from the country’s borders. In February-March 2020, one border patrol UAV conducted 20 flights in Panama’s airspace. Countless occasions of border patrol drones hoovering miles away from the designated border areas have also been recorded. 

Such detours are problematic as they may be breaching unknowing citizens’ privacy. Border guards have also been found to use drones to collect and share non-immigration-related data with law enforcement in border communities, according to the flight logs

AI-enabled drones must be used responsibly within authorized areas only and for a select number of approved use cases. Border forces must also maintain transparent records about the type of data being collected and how it’s being stored and used. Doing so is essential to protect the citizens’ rights to privacy. 

autonomous navigation systems

Aerial Autonomous Navigation Systems: The Path Forward

Aircraft autopilot systems have been around since the early 20th century. Modern autopilot systems effectively support pilots at every phase of the flight, enabling greater safety, efficiency, and comfort. Unmanned aerial vehicles (UAVs) have even more advanced navigation and safety systems. 

But how technologically far are we from achieving full autonomy? Given the latest advances in sensor fusion, computer vision, deep and reinforcement learning, reliable autonomous navigation systems will become a reality once we resolve the last cluster of challenges.  

5 Challenges of Creating Autonomous Navigation Systems for UAVs 

Autonomous UAVs have many great use cases — from urban package delivery to automated industrial asset inspections, border patrols, or land surveying missions. What prevents the technology from going mainstream is the reliability requirements. Autonomous navigation systems must be ultra-safe with minimal possibility of error. To make that happen, industry leaders are working on solving the next five problems. 

Dynamic Environmental Awareness 

UAVs need continuous spatial awareness about flight conditions and obstacles to operate autonomously without posing risks to people and infrastructure. Modern drones already include advanced sensing capabilities, ranging from HD cameras to LiDAR and optical flow sensors. Computer vision and navigation algorithms can process all of these data points on-device to provide real-time environmental awareness to drones for autonomous operations. 

The Fly4Future team recently presented INEEGO — an indoor inspection drone with an autonomous navigation system. The drone can effectively glide through the premises using only a partially known map and adjust its path based on the data from onboard sensors to avoid obstacles. With INEEGO, pilots can seamlessly inspect AC pipelines, carrier beams, and other industrial structures for signs of deterioration with little risk of collision. 

GPS Denied Environments

GPS has long been a bottleneck in UAV reliability. If the signal is down or unreliable (which happens often in dense urban areas), the drone loses all positioning navigation. Drone signals can also be jammed or spoofed, further affecting the safety of autonomous flights. 

GPS Denied Environments

Soundly, there are alternatives to GNSS connectivity. US manufacturer Bavovna developed a hybrid AI-powered navigation system for operating in GPS-denied/compromised environments. The company’s flagship product is a SWAP,  low-cost, modular solution, combining an onboard hardware processing unit and pre-trained AI algorithms to provide precise Position, Navigation, and Timing (PNT), crucial for autonomous operations. With Bavovna’s system, UAVs can fly fully autonomous complex missions, relying exclusively on onboard sensors like IMU array (accelerometer, gyroscope), airflow sensor, compass, and barometer, among others. 

Battery Management 

Drones have limited battery capacity. If an autonomous UAV runs out of charge mid-route and fails to safely land, that’s no good. So, researchers are evaluating different options for improving drone battery management. 

NTIS Research Centre, for example, created an experimental mechatronic system for automatic drone battery management. Droneport can autonomously swap batteries on UAVs without any human intervention.  Compact and easily assembled, the Droneport robo-arm can perform scheduled battery swapping tasks on drones, equipped with a specialized battery holding case with high accuracy. 

Self-charging drones are another actively explored option. Drones4Safety technology, developed in Denmark, allows charging UAVs using railway and power line cables. When a drone reaches a low battery status, an autonomous navigation systems navigates it toward the nearest overhead line for charging, using data from GNSS and EGNOS. On-board sensors also provide extra detection and navigation towards the nearest power lines. Several field trials have already proven the viability of such an approach. However, the team still needs to work on the systems’ accuracy to avoid potential damage to the power lines. 

Autonomous Take-Off and Landing

Present-day drones require a pilot to facilitate take-off and landing. In autonomous operations, drones will have to auto-locate suitable landing sites and ensure that no obstacles or humans are in the way of a safe takeoff/landing. Again, this problem requires improvements to current navigation capabilities and some upgrades to hardware design. 

Autonomous Take-Off and Landing

Evolve Dynamics is working in this direction. The manufacturer recently showcased its 

Sky Mantis UAV can perform fully autonomous landing, loitering, and zonal position holds with high accuracy. The model relies on ground-based radar beacons communicating with a Sensoriis airside radar mounted on the Sky Mantis UAV, which is used to make decisions based on precision positioning data.

A group of Polish researchers, in turn, proposed a lightweight deep learning vision algorithm to support autonomous UAV landings and take-offs. It demonstrated near real-time performance on modern embedded GPU devices, and high safety and robustness in human presence detection and positioning error estimation. However, it’s yet to be tested in the field. 

Reliable Connectivity 

Autonomous UAVs with autonomous navigation systems will still require strong communication links with ground stations for localization and data exchanges (e.g., streaming video feeds, receiving updated flight plans, etc). Present-day communication ranges are limited to about 60 miles (35 km). Using lower frequencies allows higher ranges but limits the maximum data rate (i.e., results in higher latency). 

Software-defined networks (SDNs) for drones are emerging as a solution to this problem. With SDNs, the communication between the control layer and data link layer is commonly performed with OpenFlow protocol. OpenFlow protocol allows the network’s forwarding plane control over the network’s switch or router functionality. SDN design also improves the packet delivery ratio by tuning network speeds and UAV network security. 

The groundwork for enabling autonomous navigation systems for UAVs has already been substantial. In the coming years, we expect more solutions to move from the labs to the mainstream, ushering a new era of safe, autonomous aerial navigation.