Technology
What Powers Our Systems
Every layer of our stack is chosen for performance, reliability, and real-time constraints.
Languages & Runtime
Core Stack
Flight Control & Embedded
Low-level autopilot logic, sensor fusion (EKF, complementary filters), and real-time control loops running on ARM Cortex-M4 and STM32 microcontrollers. We write bare-metal and FreeRTOS tasks for deterministic timing.
class FlightController {
void updateAttitude();
float pid(0.01);
};
Backend & Fleet Services
Mission orchestration, telemetry ingestion, and secure REST APIs for multi-vehicle fleet management. We use Java for its robust concurrency model and mature ecosystem in enterprise environments.
public class FleetManager {
List<UAV> activeFleet;
TelemetryStream stream;
}
AI / ML & Vision Pipelines
Neural network training, data preprocessing, and computer vision pipelines using PyTorch, OpenCV, and NumPy. We train on workstations and deploy quantized models to edge devices.
def train(epochs=200):
model.fit(data, labels)
torch.save(checkpoint)
Autonomy & Intelligence
Intelligence Layer
ROS2 & Middleware
Node-based autonomy architecture with DDS communication, SLAM (RTAB-Map, ORB-SLAM3), and behavior trees for deterministic mission execution. We use Nav2 for path planning and Foxglove for visualization.
Neural Networks
Deep learning models for object detection (YOLO, RT-DETR), semantic segmentation, and anomaly detection. Models are quantized to INT8 and deployed on Jetson Nano, Xavier NX, and Coral Edge TPU.
Computer Vision
Real-time object tracking (DeepSORT, ByteTrack), optical flow, stereo depth estimation, and visual-inertial odometry for GPS-denied navigation. OpenCV and CUDA-accelerated pipelines.
Algorithms
Pathfinding & Decision Making
Graph-Based Pathfinding
Dijkstra and A-star for global path planning. Probabilistic Roadmaps (PRM) and Rapidly-exploring Random Trees (RRT) for high-dimensional configuration spaces. We also implement custom heuristics for UAV-specific constraints like battery range and no-fly zones.
Object Avoidance
Multi-sensor fusion for obstacle detection: 2D LiDAR, depth cameras (Intel RealSense, OAK-D), and ultrasonic arrays. Reactive trajectory replanning using potential fields and dynamic window approach (DWA) for real-time collision avoidance.
Architecture
System Overview
How the layers connect from sensors to cloud.
Perception
Camera / LiDAR
Depth Sensor
IMU / GPS
Edge Compute
Jetson / TPU
NN Inference
SLAM / VIO
Autonomy
ROS2 Nodes
Path Planner
Flight Controller
Cloud
Fleet Manager
Telemetry DB
Mission Control
↓ Data Flow ↓