MARKET CONTEXT
Why Robotics Needs Different Technology
Robotics systems operate under constraints consumer AI solutions never face:
- Cost is critical. Every dollar per processor affects system profitability. You need efficient, affordable computing.
- Real-time performance is required. A 100ms latency in robot control creates safety problems or loses precision.
- Power efficiency is essential. Mobile robots have limited battery. Efficient computing extends operational lifetime.
- Reliability is non-negotiable. Robots operating in factories, hospitals, or hazardous environments can't fail.
- Modularity matters. Different robots need different computing. Small MCUs for simple tasks. Powerful processors for complex perception.
ATLAS solves all of this.
Cost-effective. Deterministic. Modular. Power-efficient.
ATLAS for Robotics
THREE ROBOTICS USE CASES
Use Case 1: Low-Cost Real-Time Visual-Inertial Navigation
Objective: Enable UAVs and AGVs to navigate autonomously in complex environments at minimal cost.
Challenge: Autonomous navigation requires real-time processing of vision + inertial data. Standard solutions (specialized robotics hardware + GPU) cost €3,000–8,000 per robot. You need to reduce this to €500–1,000 per unit for cost-effective mass deployment.
Solution
Eagle navigation stack + Hephaestus robotics processor. Optimized for low cost while maintaining real-time performance.
Implementation
- Low-cost camera (€50) + IMU sensor (€100).
- Hephaestus robotics processor (€300–500 early access pricing).
- Eagle navigation software (included).
- Real-time visual-inertial odometry.
- Obstacle avoidance.
- Path planning.
Features
- Ultra-low system cost (€500–1,000 vs. €3,000–8,000).
- Real-time performance (<50ms latency).
- Works with minimal sensors (just camera + IMU).
- Power efficient (extends flight time).
- Autonomous operation (no ground station required).
Results
- 10–15x cost reduction per robot.
- Mass deployment becomes economically viable.
- Swarm robotics becomes practical.
- GPS-denied autonomous capability guaranteed.
Use Case 2: Predictive Maintenance for Electro-Mechanical Components
Objective: Predict component failures before they happen, reducing downtime and maintenance costs.
Challenge: Robot components (motors, gearboxes, bearings) degrade over time. You want to predict failures before they occur and replace components during planned maintenance windows. Standard approaches require manual inspection or expensive sensor arrays.
Solution
AI-based predictive maintenance using Eagle's time-series analysis on existing robot telemetry (motor current, temperature, vibration).
Implementation
- Collect telemetry from robot motors/gearboxes (current, temperature, vibration).
- Train Eagle model on historical failure data.
- Real-time health scoring on Hephaestus.
- Automatic alerts when component health drops below threshold.
- Planned maintenance scheduling before failure.
Features
- Uses existing robot sensors (no additional hardware).
- Predicts failures 2–4 weeks in advance.
- Real-time monitoring on robot.
- Automatic maintenance scheduling.
- Reduces downtime by 30–50%.
Results
- Fewer unexpected robot failures.
- Planned maintenance vs. emergency repairs.
- 20–40% reduction in maintenance costs.
- Improved production uptime.
- Better component lifecycle management.
Use Case 3: Neural-Based Force Control for Precision Manipulation
Objective: Enable collaborative robots to perform precision manipulation tasks (assembly, delicate object handling) with improved safety and accuracy.
Challenge: Collaborative robots need to sense and respond to forces in real-time. Traditional PID control is rigid and unsafe around humans. AI-based adaptive control can be safer and more responsive.
Solution
Neural network force controller trained on demonstration data, deployed on Hephaestus for real-time inference.
Implementation
- Teach robot desired manipulation task through demonstration.
- Train neural force controller on demonstrations.
- Deploy controller on Hephaestus robotics processor.
- Real-time force feedback from robot end-effector (10kHz sampling).
- Adaptive compliance based on task and environment.
Features
- Safer human-robot collaboration (adaptive force limits).
- More precise manipulation than rigid controllers.
- Adapts to different objects and materials.
- Real-time performance (<100μs latency).
- Deterministic behavior (safety-critical).
Results
- Safer human-robot collaboration.
- Better precision on delicate tasks.
- Faster programming of new tasks (learning from demo).
- Improved product quality.
TECHNOLOGY FOR ROBOTICS
Why ATLAS Wins for Robotics
- Cost: Hephaestus robotics processor costs 5–10x less than GPU-based alternatives. Enables mass deployment.
- Power Efficiency: 3–5x lower power consumption. Extends battery life for mobile robots.
- Real-Time Performance: Deterministic latency. Suitable for real-time control. Safe for human collaboration.
- Modularity: Works with any robot. Any sensor. Any existing control system. Plug-and-play integration.
- Reliability: Fault tolerance. Continues operating even if components fail. Safe for mission-critical applications.
- Sustainability: Lower power consumption reduces operational carbon footprint. Supports Industry 5.0 objectives.
INTEGRATION WITH ROBOTICS PLATFORMS
Works With All Major Robot Platforms
- Industrial Collaborative Robots: Universal Robots, Comau, ABB, FANUC, Yaskawa.
- Autonomous Ground Vehicles: Clearpath, Boston Dynamics, autonomous driving platforms.
- Autonomous Aerial Vehicles: DJI, senseFly, fixed-wing and multi-rotor platforms.
- Autonomous Underwater Vehicles: Marine robotics and autonomous underwater systems.
- Mobile Manipulation Platforms: Research platforms, service robotics, logistics automation.
- Existing Infrastructure: ATLAS integrates without replacing your current system.
INDUSTRY 5.0 & SUSTAINABILITY
Robotics for Sustainable Manufacturing
ATLAS supports Industry 5.0 objectives:
- Human-centered: Safer human-robot collaboration through adaptive force control.
- Sustainable: Lower power consumption reduces operational carbon footprint.
- Resilient: Predictive maintenance and fault tolerance improve system reliability.
- Decentralized: On-robot computation (not cloud-dependent) enables distributed manufacturing.