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모빌리오 NavigateX – 디지털 트윈 및 AI 내비게이션 플랫폼

NavigateX 는 모빌리오의 차세대 디지털 트윈 내비게이션 플랫폼으로, LiDAR, IMU, 360° 카메라 데이터 를 결합하여 고정밀 3D 지도와 실시간 경로 최적화를 제공합니다.

  • 디지털 트윈 매핑: 3D 디지털 트윈으로 공장, 물류 센터, 조선소, 공항을 시각화합니다.

  • AI 경로 계획AI 기반 경로 최적화와 자율 주행을 제공합니다

  • BIM & GIS 통합: Connects seamlessly with BIM models and platforms like Google or Naver Maps

  • Cloud & Edge Ready: Enables remote management, monitoring, and collaborative mapping

Applications: Smart logistics, shipbuilding & construction, hazardous zone safety, infrastructure management

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Mobilio 360 Twin - Digtal Twin

360 Twin uses 360° cameras and sensor data to reproduce immersive, street-view–like digital twins of real-world environments.

  • Immersive Mapping: Captures and stores every angle of the site in 360°

  • BIM Comparison: Accurately compares construction drawings with on-site progress

  • Remote Inspection: Enables real-time site monitoring from anywhere

  • 인공지능 기반 인식: Supports object recognition, asset management, and anomaly detection

Applications: Construction & plant management, safety inspection, facility asset management, remote collaboration

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Autonomous Robots

  • Autonomous Robots are machines capable of making decisions and moving independently using a combination of sensors, AI, and navigation algorithms.

  • Core functions:

    • Perception – sensing and understanding the environment

    • Decision-making – AI-driven path planning and task selection

    • Control – executing movement and actions via motors/actuators

  • Applications: logistics (delivery robots), manufacturing (smart factories), construction & shipbuilding (hazard inspection), disaster response (search & rescue).

[모빌리오 로봇 솔루션]img 4017

SLAM & Navigation

  • SLAM (Simultaneous Localization and Mapping) enables robots to localize themselves while building a map of the environment at the same time.

  • It leverages LiDAR, cameras, IMUs, and sensors to allow robots to operate even in GPS-denied or complex environments.

  • Navigation is the process of using SLAM-generated maps to plan optimal paths, avoid obstacles, and reach destinations safely.

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AI Logis

Mobilio Logis is an intelligent robotic solution that automates transportation, sorting, and loading tasks in logistics environments.

  • 자율 주행: LiDAR- and SLAM-based navigation for safe movement in complex warehouses

  • Smart Handling: Integration with robotic arms for automated picking, stacking, and unloading

  • 스마트 IoT 연결: Links with sensors and WMS (Warehouse Management Systems) for real-time inventory and flow control

  • Cloud Management: Remote monitoring and data-driven operational optimization

  • 활용 분야: Smart warehouses, in-factory material transport, e-commerce fulfillment centers, large-scale distribution networks

Wireless IIoT Sensor

  • Mobilio’s IoT sensor solutions provide real-time data collection and management across diverse environments.

    • Environmental Sensors: Gas, temperature, humidity, oxygen monitoring

    • 360° Camera & LiDAR: Digital twin creation of physical spaces

    • Vibration & Position Sensors: Equipment monitoring and predictive maintenance

    • 활용 분야: Smart factories, safety management, remote monitoring, asset management

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AI Insightor

Mobilio’s AI solutions empower robots and digital twins with intelligent decision-making and automation.

  • AI Vision Recognition: Object detection, obstacle avoidance, safety monitoring

  • AI Navigation: SLAM-based path planning and autonomous driving

  • AI Analytics Platform: Equipment anomaly detection, risk prediction, productivity optimization

  • 활용 분야: Smart logistics, construction & shipbuilding, energy plants, disaster response

 

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Mobilio Reinforcement Learning

Mobilio leverages Reinforcement Learning (RL) to enable robots to self-learn and optimize decision-making strategies.

  • Simulation-Based Training: Safe and rapid learning through thousands of trials in digital twin environments

  • Reward-Driven Optimization: Rewards for successful actions and penalties for failures to improve performance

  • Sim-to-Real Transfer: Applying simulation-trained models directly to physical robots for faster adaptation

  • 활용 분야:

    • Path optimization in logistics robots

    • Balance and obstacle negotiation in quadruped robots

    • Task execution in humanoid robots

    • Risk-avoidance behaviors in disaster-response robots

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