Intelligent Navigation

Use AI and sensors to enable safe, efficient autonomous navigation in dynamic environments

Business impact

• Operational efficiency — Optimizes routes and reduces delays to improve overall process speed
• Safety incidents — Minimizes accidents through precise obstacle detection and navigation control
• Automation adoption rate — Facilitates transition to automated workflows by reliable autonomous navigation
• Throughput — Increases material and delivery handling capacity via optimized navigation
• Customer satisfaction — Improves user experience with reliable, convenient autonomous navigation services

Data requirements

• LIDAR sensors (Numeric) — Provide precise 3D spatial data for obstacle detection and mapping
• Cameras (Image) — Capture visual information for environment recognition and navigation
• GPS and IMU (Numeric) — Supply location and motion data for positioning and route planning
• Fleet management software logs (Structured) — Offer operational data for analytics and optimization
• User interaction data (Text) — Enable adaptive UX and personalized navigation assistance

AI methods and techniques

• Predictive AI — Forecasts obstacles and traffic patterns to optimize routes proactively
• Agentic AI — Enables autonomous decision-making for navigation and obstacle avoidance
• Generative AI — Simulates scenarios for training and improving navigation models

AI models and model families

NVIDIA Isaac Sim, GPT-4, Claude, Llama 2

Industries

Real-world evidence

23 documented case studies on record.

Companies using this: A & K Robotics, Brain Corp, Coop Himmelb, Cruise, Daifuku Co Ltd, Diligent Robotics, EROAD, Hanwha Robotics, John Bean Technologies Corp, KUKA AG, Navee, Peer Robotics, Port Rotterdam, Roborock, Robot and 8 more.