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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
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Why Robotics & Edge AI Demands a Different Standard

01

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.

02

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.

03

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.

04

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

1

Requirements & Safety Analysis

2-3 wk

Hazard analysis, safety case, CONOPS

2

Architecture & Hardware Selection

2-3 wk

Edge compute selection, sensor stack design

3

Simulation & Digital Twin

3-4 wk

IsaacSim / Gazebo, offline validation

4

Hardware Integration & Testing

6-16 wk

HIL testing, field trials, safety validation

5

Deployment & Mission Support

Ongoing

OTA updates, telemetry monitoring, fleet management

Technologies We Use

Stack spanning perception, inference, embedded systems, and fleet operations

ROS2PythonC++RustCUDATensorRTOpenVINONVIDIA JetsonCoral TPUFreeRTOSZephyrPCLOpenCVPyTorchIsaacSimGazeboMuJoCoKubernetesKafkaInfluxDBGrafanaAWS IoT GreengrassAzure 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.

Contact Robotics Division

Frequently Asked Questions

Everything you need to know about our services

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.
Yes. We integrate with commercial-off-the-shelf platforms (Boston Dynamics, Universal Robots, DJI enterprise) as well as fully custom hardware builds.
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.
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.
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).
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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