Drone Software Development for Manufacturers

According to research by Fortune Business Insights, the global drone software market will grow at a CAGR of 32% from 2019 to 2032. This is explained by the growing use of UAVs for automation and process safety in the military, commercial, and civil sectors. At the same time, the more complex the tasks assigned to them and the greater the autonomy they require, the greater the need for specialized management software. Below, as a team with extensive experience in this niche, we’ll share our insights on this matter.

Drone Software Architecture Components

Using a multi-layered architecture with strict process isolation is essential for UAV software development: it ensures that a fault in a module won't lock up the flight controller and cause the aircraft to crash. Here's a brief description of these layers:

• Hardware abstraction layer. This layer is designed to hide the peculiarities of specific hardware from the high-level logic, so it utilizes drivers and interfaces. If the manufacturer decides to replace one gyroscope with another or upgrade to a more powerful processor, the core control logic will remain unchanged. This layer also includes the UAV operating system, managing task priorities and ensuring deterministic execution.
• The layer of the flight stack and control loops. This layer implements the flight stack, a suite of algorithms that translates mission commands into precise RPM signals for electronic speed controllers. It handles sensor fusion, synthesizing data from the IMU, barometer, and GNSS to eliminate noise via Extended Kalman Filters. Cascaded PID or LQR controllers operate here, processing correction signals thousands of times per second, up to 1kHz or higher.
• Navigation and mission layer. This is the highest layer, which includes modules responsible for performing complex flight tasks: waypoint-based route planning, automatic takeoff and landing, object following, and more. Here, developers implement computer vision algorithms for real-time obstacle avoidance and interfaces that enable data transfer to a ground station or an onboard computer (e.g., NVIDIA Jetson). 

Choosing this modular architecture provides two decisive advantages: safe iteration (updating features without risking core flight stability) and parallel development (enabling separate teams to work on AI, GCS, and firmware simultaneously).

Flight Control Software

The main objective of such software is to minimize UAV positioning errors and ensure predictable vehicle response in dynamic environments.

Stabilization, navigation, and sensor fusion

We use cascaded PID controllers or more advanced linear quadratic controllers for these tasks. It's also important to understand that a single sensor always introduces error or drift, so it's crucial to focus on implementing Kalman filters during development, combining data from accelerometers, gyroscopes, magnetometers, barometric sensors, and GNSS. In more advanced drone flight control systems, we also add visual odometry and LiDAR, which are critical for flights in GPS-denied environments.

Real-time constraints and safety requirements

Each control cycle must complete within a strictly defined time, while execution time variance must approach zero. The code also implements emergency termination algorithms: return to the starting point when the communication loss happens and emergency landing in the event of critical battery discharge or obstacle avoidance. Ultimately, we always adhere to standards close to DO-178C to prevent uncontrolled system behavior.

Embedded Firmware and Hardware Integration

For serial UAV production, the choice of embedded components and operating systems determines scalability.

Microcontrollers, RTOS, and firmware updates

We typically focus on 32-bit architectures (STM32 H7/F7) or specialized SoCs. NuttX, FreeRTOS, or ChibiOS are our preferred operating systems due to their real-time task scheduling and efficient memory management. Overall, they enable the isolation of critical flight loops from secondary logging and telemetry tasks.

Communication between sensors, ESCs, and flight controllers

Since the performance of UAV software depends on the speed of data exchange between components within the aircraft, it’s important to use protocols for communication between the flight controller and ESCs and smart sensors such as UAVCAN or Cyphal. For high-speed communication with internal sensors such as IMUs and barometers, the I2C and SPI protocols are particularly useful. In turn, the Mavlink protocol acts as a communicator between the drone and the ground control station and companion computer, enabling telemetry transmission over narrow communication channels. 

To ensure secure firmware updates in the field, we implement dual-banking mechanisms. In this case, the new firmware is located in a memory bank, so switching to it occurs only after verifying a checksum, preventing the drone from becoming useless when a communication failure happens.

Ground Control Software

For a UAV manufacturer, the quality of ground control software determines how minimal the risk of human error is.

Mission planning and real-time monitoring

Developing such software typically involves creating dynamic flight mission algorithms. To do this, we define waypoints, each of which is an object with a set of attributes. In addition to real-time capturing and analyzing coordinates, it contains algorithms for changing the altitude hold, gimbal control, triggering a payload, or performing a specific maneuver such as hovering.

Such software also often requires implementing point-in-polygon algorithms at the sub-millisecond level – they continuously compare the drone's movement vector with a database of restricted areas, automatically recalculating a route in the event of a potential violation.

As for UAV software development for the agricultural sector and surveying, terrain-following algorithms are likely required. These involve integrating software with high-precision digital elevation models to automatically adjust the flight mission so that the drone maintains a constant relative altitude, accounting for the flight controller's processing latency.

User interfaces for operators and engineers

The interface should display only critical data for the user's current role.

Specifically, for operators, we design a minimalist heads-up display with an artificial horizon, battery status and voltage in percentage, RSSI signal strength, and key alarms. We also use color coding so operators can instantly assess the system's status without interpreting numerical values.

For diagnostics and commissioning specialists, we provide access to raw MAVLink data streams, real-time vibration spectrograms, and graphical EKF displays. This ensures identification of resonances or compass degradation before they lead to loss of control.

Communication and Data Processing Layers

Since poor communications are a bottleneck for any UAV manufacturer, it’s crucial to build a system suitable for operation in conditions of unstable connectivity and active electronic warfare.

Telemetry, video streaming, and command links

For instant data transmission, you can rely on a channel-division architecture such as frequency/time or hybrid transmission systems. If you add to this the protocols with redundant coding and forward error correction and the MAVLink stack with an encryption layer and packet signing (HMAC-SHA256), you’ll make your software impossible for an attacker to inject false commands.

As for video streaming, for ultra-low latency, it's best to use H.265 codecs. They provide 40-50% better compression than H.264 while maintaining the same detail. Finally, special attention should be paid to the RTSP and SRT protocols – they minimize video stuttering during flights beyond line-of-sight.

Edge computing vs onboard processing

Each task in drone software must be executed on the most appropriate node. Developers divide such drone control systems into several layers. The first is onboard processing, which is implemented using processors such as the NVIDIA Jetson Orin or Raspberry Pi Compute Module 4. This is where latency-sensitive tasks are performed, such as those based on computer vision and/or requiring object detection and tracking using optimized neural networks.

Conversely, when a task requires resource-intensive computation (for example, photogrammetry or real-time generation of dense 3D clouds), the edge computing layer is activated. It transmits data to a mobile edge server at the ground station.

Cloud and Platform-Level Drone Software

For serial manufacturers, their software must be scalable to a whole swarm of drones.

Fleet management and log analysis for predictive maintenance

If you consider developing a cloud-based system that automatically accumulates data after each flight, it will need to be equipped with an automated log parsing mechanism that identifies hidden trends. Each drone should also have a health card with engine hours, battery charge cycles, and update history.

Remote diagnostics and updates

If you need remote control software over 4G/5G and satellite channels, you'll need to consider developing specialized low-latency relay servers to relay commands with minimal jitter. You'll also need to implement multi-tenant authorization, allowing remote pilots from control centers to take control of UAVs located hundreds of kilometers away.

Data processing pipelines for digital twins implementation

The cloud layer is often used for resource-intensive data post-processing. Automated pipelines that transform thousands of orthophotos or LiDAR scans into digital twins of objects come to the rescue here. At the same time, the use of distributed computing in the cloud reduces processing time from days to hours.

Our Case Study: FieldSense System with AI Analytics for Scalable Agricultural Monitoring

To demonstrate the effectiveness of an integrated approach to drone software development, we invite you to consider the FieldSense project, implemented by our team for the US agritech company. The client, managing a fleet of hundreds of drones, faced a classic problem: an excess of raw data and a lack of operational insights.

The solution was based on a modular architecture based on FastAPI microservices and a distributed Celery task queue. This enabled the system to process up to 8,000 aerial images in a single session without performance degradation. We also implemented the RT-DETRv2 architecture for vegetation anomaly detection, achieving an F1-score of 0.78 in stress zone recognition. Crop yield forecasting was implemented using XGBoost regression models that analyze vegetation indices in conjunction with soil data. Finally, we ensured seamless system integration with the client's ERP and GIS platforms via a REST API and support for GeoTIFF/SHP formats.

Thanks to the implementation of Transfer Learning methods, retraining models for new regions now requires fewer than 500 newly labeled samples, reducing system adaptation time by 70%. An inference speed of 10 FPS enabled the client to transition to near-real-time analytics, transforming aerial photography into a highly accurate agricultural risk management tool with measurable ROI.

Fleet Management and Remote Operations

For a major UAV manufacturer, the transition from individual control to centralized drone fleet management software is a crucial stage.

Multi-drone coordination

At this stage, development should focus on creating decentralized interaction protocols. This involves implementing mid-air collision avoidance algorithms based on data exchange protocols between drones and creating a software layer that automatically distributes flight tasks among available UAVs (taking into account their current battery charge, technical condition, and distance from the target).

Remote diagnostics and updates

Scalable production is impossible without a remote health monitoring system, which, in turn, requires the implementation of comprehensive telemetry. This makes it possible to monitor the health of each drone in real time during missions. It's also important to ensure predictive maintenance – for example, through vibration harmonic analysis and temperature logs. And yes, don't forget to implement a global software rollout mechanism to enable mass firmware updates via encrypted communication channels, thereby minimizing equipment downtime.

Data storage and analytics

The volume of data generated during a single drone flight can reach tens of gigabytes. This is where AI-based analysis comes in handy, as it detects anomalies in flight logs. Also, you should consider structured storage based on a data lake architecture – this can then be used to train new computer vision models.

Scalability Challenges for Drone Manufacturers

Scaling up production involves both increasing the number of drones and developing a platform that is immune to changes in supply chains and automating quality control.

From prototype to mass production

When a company moves from building ten prototypes to producing thousands of units, it faces the need to implement a hardware abstraction layer between the flight logic and the hardware. This ensures code portability: thanks to it, replacing a microcontroller doesn't require rewriting the stabilization or navigation algorithms – instead, updating the low-level drivers will suffice.

It's also important to ensure peripheral standardization for the rapid certification of new UAV component suppliers – usually, it’s achieved through the implementation of standardized software interfaces for working with speed controllers, IMU sensors, and communication modules.

Finally, using systems like ROS 2 makes sense, as it enables the creation of modular applications that simplify enriching production models with new payloads.

Supporting thousands of drones in the field

During mass production, human error during testing becomes an unacceptable risk. To avoid this, it's crucial to initially create a digital twin of the drone, taking into account its mass, inertia, motor thrust, and other physical characteristics, so that every code change is automatically tested in a virtual environment. 

Next, you have to connect the flight controller to the simulator to collect synthetic sensor data and identify bugs related to processor timings and interrupts. At the end of the testing cycle, every commit to the code repository should undergo automated performance and stability tests. This will ensure that the update received by thousands of users won't cause hardware failures.

Security, Compliance, and Safety Considerations

A vulnerability in software code can lead to data leakage and/or physical damage. Therefore, it's important to consider the following aspects during development.

Encryption and secure communication

To eliminate system compromising, developers have to ensure end-to-end encryption (particularly using AES-256-GCM algorithms); in addition, development teams should also use elliptic curve cryptography for key exchange. Additionally, to prevent spoofing and hijacking, it makes sense to use cross-verification algorithms that compare GNSS data with visual odometry and inertial sensors and, if an anomaly is detected, switch the software to autonomous landing or return-to-home mode.

Finally, it’s essential to implement trusted boot mechanisms, ensuring that the flight controller only executes code signed by the manufacturer's digital certificate. This eliminates the possibility of launching malware in the event of physical access to the device.

Regulatory compliance (aviation authorities and data privacy)

UAV manufacturers' software must be ready for certification. This primarily requires remote ID support for ASTM F3411 and ASD-STAN ED-269 standards, which require drones to transmit their data via Bluetooth or Wi-Fi. GDPR compliance is also typically required – it can be achieved through edge privacy algorithms that can automatically detect and blur faces or license plates before the video stream is transmitted to the cloud.

Finally, your software may be subject to local security standards, which require the elimination of dangerous code (such as dynamic memory allocation or uncontrolled pointers) to prevent system freezes in flight.

Custom vs Off-the-Shelf Drone Software

For a startup, using open-source solutions like ArduPilot or PX4, followed by customization of the business logic, is perfectly acceptable. However, for a manufacturer striving to ensure product uniqueness, off-the-shelf solutions impose some limitations:

• Code redundancy. Universal firmware supports hundreds of sensor types, which introduces additional code and, as a result, slows down processor cycles and introduces potential vulnerabilities in unused modules.
• Difficulty of customization. Attempting to implement a unique obstacle avoidance algorithm into a rigidly structured open-source system often leads to conflicts in the task scheduler.
• Community dependence. Since you don't control the product roadmap, a critical feature can be left in the community backlog for years.

At the same time, with custom development, you gain the benefits of complete control over the stack and intellectual property, code optimization for specific microcontrollers, and a UX/UI tailored to your needs and business processes.

Future Trends in Drone Software Development

We're closely monitoring the trends most likely to become standard in the coming years.

Autonomous drones and AI

The UAVs’ future lies in minimizing human intervention in drone missions. This is achieved through semantic mapping, a technology that enables drones to recognize specific obstacles and make appropriate decisions, as well as edge AI, which brings neural network inference onboard, enabling decision-making in milliseconds without relying on the cloud. 

Swarm intelligence

This intelligence requires the development of self-organization protocols where drones can autonomously assign roles (typically reconnaissance, relay, strike, etc.) to each other, responding to changing situations without human intervention.

Integration with enterprise systems

UAVs will soon cease to be isolated devices and become integrated into ERP and/or CRM systems. They will be able to automate document flow (for example, by inspecting objects, transmitting this data to the system, generating damage reports, and scheduling repairs) and ensure continuous synchronization of collected data with a digital twin of the object in real time.

Are you ready to discuss the architecture and business objectives of your UAV-based project? Feel free to contact us, and we’ll transform your ideas into reliable and scalable code.

FAQ