ai pilot

AI Pilot: How Far Has Navigation Autonomy Advanced? 

Global Navigation Satellite Systems (GNSS) systems such as GPS have been a staple technology in land, maritime, and aerial navigation for decades. Super useful? Absolutely. But, GNSS has its fair share of downsides too.

Weather, buildings, natural obstacles, and some electronic systems cause inevitable signal degradation and latency in signal acquisition. Heavy reliance on a network of satellites and ground stations, attractive targets for physical or cyber-attacks, also raises rightful security concerns. For UAVs and ROVs, signal spoofing, jamming, and other forms of electromagnetic warfare (EW) are also a problem. 

For many mobility scenarios signal loss or high latency is unacceptable, even more so if we’re to advance to full autonomy. But if not GNSS, what other technologies can provide reliable positioning and navigation data? A couple of options are on the table. 

Sensor Fusion with AI 

Every commercial UAV is equipped with a set of standard sensors, such as an accelerometer, gyroscope, compass, airflow sensor, and altimeter, for situational awareness. More advanced enterprise models may also include LiDAR distance sensors or ultrasonic sensors to detect proximity to obstacles.

Combined, these provide real-time information on the drone’s location, orientation, and movement, and a GPS/GNSS receiver tallies this with geo coordinates, which allow the drone to follow a pre-planned flight route. When GPS is down, the best models will hold position or attempt to automatically return home. 

Yet, recent advances in sensor fusion also enable autonomous navigation scenarios. Instead of the GPS signal and pilot commands, the drone can rely on data from its Inertial Measurement Unit (IMU) and AI algorithms to safely continue the route. By integrating and processing sensor data streams on the device, AI pilot models can provide precise situational awareness to drones and full autonomy. 

Bavovna Navigation Kit includes an 800gr/28 oz onboard edge devices and SaaS access to a fine-tuned AI Hybrid navigation model, pre-trained on historical data from the available sensors. Already implemented on eight UAV platforms, ranging from fixed-wing and VTOLs to multi-copters and FPVs, the system maintains an End Point Positioning Error (EPPE) of under 0.5% on complex trajectories of up to 30 km or 18.6 miles. 

Intended for dual use, Bavovna’s hardware sits in an EMI-protected case and can be further augmented with a SIGINT RF module for bypassing EW and EM obstacles. AI pilot system, not only enables more reliable autonomous navigation but also improves obstacle detection, UAV routing, and ultimately operating efficiencies. 

ai pilot bavovna

LiDAR-Based SLAM Systems

An alternative approach to sensor fusion is the Simultaneous Localization and Mapping (SLAM) technique. While sensor fusion primarily relies on IMU data, SLAM algorithms use camera and LiDAR data to general a real-time map and localize the piloted system in a GPS-denied environment. Opposed to cameras, 3D LiDARS provides an immediate point cloud of the environment, eliminating the latency. Data processing software has to be installed both on the flight controller and the onboard computer. 

SLAM algorithms like LOAM and Cartographer have demonstrated great performance during scientific experiments in terms of trajectory execution and landing errors. Commercially, Elios 3 from Flyability and Hovermap series from Emesent, and Scout 137 from ScoutDi rely on SLAM for indoor navigation in GNSS-denied environments (with an added bonus of advanced mapping scenarios). 

The downside of photo-SLAM, however, is high resource consumption: CPU, memory, and processing, especially for photo-realistic mapping scenarios. This, in turn, requires heavier and more expensive onboard units, which increase the system operating costs. Likewise, high resource consumption results in faster battery drainage on smaller UAVs, undercutting the precious flight time. Although some recent advancements in processing frameworks help optimize resource usage. 

AI Pilots: Navigation Guided by Magnetic Fields 

Commercial and military aircraft are also increasingly relying on AI-assisted navigation, oftentimes powered by hybrid INS/GPS signals. For security reasons, however, the military is looking to progressively minimize reliance on satellite networks.  

The US Air Force has been continuously testing different AI pilots to autonomously pilot fighter jets. Technologically, we’re already there — AI systems successfully piloted an F-16 in a dogfighting exercise over California’s Edwards Air Force Base in May 2024. But there’s always room for further improvements. 

The Air Force research team is working on a new AI navigation pilot to potentially navigate the plane using the Earth’s magnetic fields. So far, such scenarios haven’t been feasible as the aircraft generate too much electromagnetic noise, interfering with onboard magnetometer measurements.

magnetic fields navigation

However, a recent model, tested on C-17, showed very promising results that could lead to “tactical airdrop quality” and a potential solution to “things we can do, should we end up operating in a GPS-denied environment.“, according to Col. Garry Floyd, director of the Department of Air Force-MIT Artificial Intelligence Accelerator program.

While we may not be there yet, widespread autonomous AI piloting without GNSS is no longer a question of “if”, but rather “when”, especially for unmanned aerial vehicles.

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. 

ai navigation

How AI Revolutionizes Aerial Navigation: 3 Innovative Use Cases

Machine learning and deep learning are staples in many industries. Ground transportation systems use predictive algorithms to optimize travel flows, predict congestion, and improve fuel usage. The sky’s the new limit. 

Impressive advances in AI navigation move from research labs to the mainstream, promising greater reliability, safety, and efficiency to aeronautics.

Inertial AI navigation for UAVs in GPS-compromised environments

GPS signal, necessary for most UAV operations, can be unavailable after a natural disaster or in military situations., a graduate of the US Air Force Labs Mass Challenge acceleration program, is developing an AI-powered PNT navigation system for aerial, surface, and subsurface vehicles.

AI navigation for UAVs drones

Designed for dual use, Bavovna’s inertial navigation system uses sensor fusion and pre-trained ML/DL algorithms to offer autonomous operations. Thanks to hardened core electronics, the system is resistant to common electromagnetic warfare threats. It’s also a low-SWaP solution with minimal power requirements and weight, suitable for UAV models and Class II aerial vehicles.

During trials, the Aurelia X6 Max multicopter could fly autonomously without remote control, GPS, or any other communication, collect location intelligence, and return to the home position. Bavovna AI navigation system aims for a 0.5% positioning error, even in complex journeys up to 30 miles (48 km). The team is working to extend the system use cases to cover Signals Intelligence, mine detection, automatic target engagement, and security surveillance.

AI copilots for commercial aviation flights

Modern aircraft have highly sophisticated autopilot systems to help pilots control altitude, course, thrust, and navigation. However, the onslaught of alerts and system interfaces requiring the pilot’s attention can be intimidating. NASA estimated that 34 different competing activities distract or preoccupy the pilots, ranging from communication to searching for VMC traffic. These can lead to human errors and dangerous consequences.

AI airflow traffic management

Air-Guardian, a new MIT CSAIL project, aims to improve the HMI of existing autopilot systems to ensure safer operations. The AI system uses eye-tracking to detect pilot distraction and “saliency maps” to understand aircraft behavior.

Based on a continuous-depth neural network model, the copiloting system can identify early signs of risks and take over the controls when needed. During trials, the Air Guardian system reduced flight risks and improved navigation success.

Advanced airflow traffic management to clear up congestion 

Unforeseen events like bad weather conditions adversely affect air traffic, causing congestion in a specific sector of the air navigation space. This, in turn, affects all network participants, leading to a cascade effect of delayed flights. 

AI airflow traffic management

Co-funded by the European Union and led by the Universita Ta Malta, the ASTRA project is short for AI-enabled tactical FMP hotspot prediction and resolution. The goal is to improve the prediction of air traffic congestion areas one hour in advance and suggest optimal resolution paths to traffic controllers.

The prediction algorithm will be trained on historical data (from 2019 to today) from the EUROCONTROL organization. The AI navigation system will provide FMPs with prescriptive scenarios to optimize flow management positions, ensuring safety, efficiency, lower fuel consumption, and environmental impacts.

AI has untapped potential in airborne navigation, and we expect more innovation in sensor fusion, AI-powered PNT, and aerial traffic management to happen in the next several years. 


AI GPS/INS Integration: The Path Forward to Better Navigation

GPS and INS are core components of global navigation systems, but both have inherent limitations. GPS is prone to signal blockage and jamming, as well as reflection on buildings and terrains, leading to signal distortion and poor position estimation. Signal accuracy also degrades at high speeds and rapid directional changes, making it less reliable for ultra-fast UAVs.

INS sensors are prone to drift due to poor initial calibration or sensor noise caused by mechanical vibration, temperature variations, cross-axis sensitivity, and wear. They also require periodic recalibration, especially for high-precision positioning.

Different machine learning and deep learning methods can improve GPS/INS signal reliability and quality—and, in some cases, even fully replace it.

The new era of Inertial navigation and AI GPS systems  

Artificial neural networks have already demonstrated strong results in real-time object recognition and signal processing in the automotive and manufacturing sectors. AI GPS in navigation systems are the next frontier.

gps inertial navigation

Researchers tested different deep learning algorithms to optimize inertial sensing and sensor fusion tasks for land, aerial, and maritime vehicles. One group developed visual-inertial odometry (VIO), an integrated inertial and image sensor fusion technique based on deep learning to predict the UAS’s position with 0.167 Mean Square Error (RMSE).

Another group successfully used a convolutional neural network (CNN) to analyze noise information from the inertial measurement units using the Kalman filter, which improves the position and orientation estimation of the navigation system. Validation on real data from an autonomous underwater vehicle showed a 35.4% higher position accuracy than conventional methods.

AI GPS effectively compensates for individual sensor limitations by enabling rapid data processing from auxiliary sources. By leveraging an array of sensors and integrating AI algorithms, the vehicle can autonomously recalibrate, correct positional data, and operate smoothly in signal-compromised environments.

Advantages of AI-assisted navigation:

  • Higher situational awareness and adaptability 
  • Reduced dependence on satellite infrastructure 
  • Lower vulnerability to signal jamming
  • Better performance on complex terrain 
  • Smarter collision detection and prevention 
  • Improved coordination for drone swarms 

Real-world applications of AI sensor fusion for navigation 

At Bavovna, we’ve been developing a new AI-powered PNT navigation system to enhance existing inertial navigation systems of UAVs. By using AI for sensor fusion, Bavovna’s algorithms can safely pilot vehicles at high speeds and complex trajectories, even in GPS-compromised environments.

During a recent test, we successfully piloted a Radiobird Defender 001, a multi-weather VTOL, through its 30-km flying mission with an end-point positioning error of 0.42-0.63% on average.  

ai gps

Sensor fusion from the onboard IMU array and airspeed provided navigation. We use sensors like a compass and barometer, with analysis performed on the edge using Bavovna AI Navigation Kit. We use a proprietary Kalman filtration algorithm and fine-tuned sensor fusion neural network trained on 100 hours of fight logs minimum.

SWaP efficient, the hardware kit is enclosed in a protective enclosure to minimize environmental and electromagnetic warfare impacts. The core electronics are also hardened with an EMI composite shield (-92 dBA/mm). 

AI algorithms and sensor fusion enable a higher degree of precision comparable to GPS and GNSS but with more reliability and resilience. This results in more efficient drone operations due to enhanced autonomy, obstacle avoidance, and route optimization, even in GPS-denied environments.