Mission-Critical Physical Systems
Robotics & Edge AI
Autonomous physical systems and real-time AI inference at the edge. Built for environments where cloud latency is not an option. Reliable. Deterministic. Field-proven.
- Autonomous Ground & Aerial Vehicles
- Edge Inference Engines
- Computer Vision at the Edge
- ROS2 / RTOS Integration
Why Robotics & Edge AI Demands a Different Standard
Real-Time Constraints
Cloud AI responds in hundreds of milliseconds. Edge AI must respond in under 10ms. We build inference pipelines optimized for latency-critical physical systems — where a delayed response means a failed mission or a safety incident.
Sensor Fusion & Perception
Autonomous systems navigate the world through sensor fusion — combining LiDAR, IMU, camera, radar, and GPS into a coherent world model. We build the perception stacks that give machines situational awareness.
Autonomy in Denied Environments
GPS-denied. Comms-degraded. Extreme temperature. Dust. Vibration. Our systems are designed to operate when the environment gives no guarantees — exactly the conditions where autonomous capability matters most.
Hardware-Software Co-Design
Edge AI requires tight integration between the model architecture, the inference hardware (NVIDIA Jetson, Coral, custom ASICs), and the real-time OS. We engineer across the full stack, not just the software layer.
Robotics & Edge AI Capabilities
Physical systems, edge inference, and field-proven autonomy
Autonomous Vehicle Systems
- Ground UGV & Aerial UAV development
- Path planning & obstacle avoidance
- SLAM (Simultaneous Localization & Mapping)
- Multi-vehicle coordination
- Fail-safe & emergency stop systems
- GPS-denied navigation
Edge Inference Engines
- Sub-10ms inference pipeline design
- Model quantization & pruning
- NVIDIA Jetson / Coral / custom ASIC deployment
- TensorRT & OpenVINO optimization
- Batched inference scheduling
- On-device model update pipelines
Computer Vision at the Edge
- Real-time object detection & tracking
- Semantic segmentation for navigation
- Depth estimation from mono/stereo cameras
- Thermal & multispectral imaging integration
- Person & vehicle identification
- Scene understanding pipelines
ROS2 & RTOS Integration
- ROS2 node architecture & lifecycle management
- Custom RTOS (FreeRTOS, Zephyr, NuttX)
- Hardware abstraction layers
- CAN bus & serial protocol integration
- Real-time telemetry streaming
- Over-the-air update systems
Sensor Fusion
- LiDAR + IMU + Camera fusion
- Extended Kalman Filter & particle filters
- Point cloud processing (PCL)
- Ground truth calibration pipelines
- Multi-modal sensor synchronization
- Degraded sensor fallback logic
Physical AI Systems
- Humanoid & industrial robotic arm control
- Force & torque feedback loops
- Imitation learning from human demonstration
- Reinforcement learning in simulation (IsaacGym / MuJoCo)
- Sim-to-real transfer pipelines
- Dexterous manipulation systems
Compliance & Engineering Standards
We align robotics and edge deployments with applicable safety and regulatory frameworks.
DO-178C
Airborne software
IEC 61508
Functional safety
ISO 26262
Automotive safety, if applicable
MIL-STD-810
Environmental engineering
NDAA compliance
Export control
EAR/ITAR awareness
Our Robotics & Edge AI Development Process
From safety analysis through field deployment and fleet operations
Requirements & Safety Analysis
2-3 wk
Hazard analysis, safety case, CONOPS
Architecture & Hardware Selection
2-3 wk
Edge compute selection, sensor stack design
Simulation & Digital Twin
3-4 wk
IsaacSim / Gazebo, offline validation
Hardware Integration & Testing
6-16 wk
HIL testing, field trials, safety validation
Deployment & Mission Support
Ongoing
OTA updates, telemetry monitoring, fleet management
Technologies We Use
Stack spanning perception, inference, embedded systems, and fleet operations
- ROS2
- Python
- C++
- Rust
- CUDA
- TensorRT
- OpenVINO
- NVIDIA Jetson
- Coral TPU
- FreeRTOS
- Zephyr
- PCL
- OpenCV
- PyTorch
- IsaacSim
- Gazebo
- MuJoCo
- Kubernetes
- Kafka
- InfluxDB
- Grafana
- AWS IoT Greengrass
- Azure IoT Edge
Classified Deployments
Many of our robotics and edge AI deployments are for defense and intelligence clients and are not publicly disclosed. For capability inquiries, contact our robotics division.
Common Questions
- What edge hardware do you target?
- Primarily NVIDIA Jetson (Orin, AGX, NX), Google Coral, and custom FPGA/ASIC designs for lowest-latency applications. Hardware selection is driven by SWAP-C constraints (Size, Weight, Power, Cost) and required inference throughput.
- Can you work with existing robotic platforms?
- Yes. We integrate with commercial-off-the-shelf platforms (Boston Dynamics, Universal Robots, DJI enterprise) as well as fully custom hardware builds.
- What simulation environments do you use?
- NVIDIA IsaacSim for physical AI and manipulation, Gazebo/ROS2 for autonomous vehicles, AirSim for aerial systems. We build digital twins that allow continuous validation without field time.
- How do you handle safety-critical systems?
- We follow functional safety standards (IEC 61508, DO-178C) including hazard analysis, safety case documentation, and independent safety validation. All autonomous systems include configurable safety envelopes and emergency stop logic.
- What is your experience with GPS-denied navigation?
- We have deployed SLAM-based navigation systems in underground, indoor, and RF-contested environments using LiDAR-inertial odometry (LIO-SAM, FAST-LIO2) and visual-inertial odometry (VINS-Fusion, ORB-SLAM3).
- Do you offer ongoing fleet management?
- Yes. We build and operate telemetry dashboards, OTA update pipelines, and health monitoring systems for deployed robotic fleets. Mission-critical SLAs available.
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