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Built with precision and purpose.

Industrial Automation•2022•14 months

Autonomous Robotics Control System for Manufacturing

Designed embedded systems and motion control software for collaborative manufacturing robots, deployed in 150+ facilities

Mechatronics & IoTEnterprise Software

80% reduction

Setup Time

Reduced from 20 hours to 4 hours per new task

150+ facilities

Deployment Speed

Successfully deployed across global manufacturing network

99.7%

Uptime

Robust systems with minimal downtime

40%

Throughput Increase

Better task adaptation improved production throughput

The Challenge

A manufacturing automation company needed to upgrade their robotic systems to be more autonomous and adaptable. The existing systems required significant programming for each new task, and they couldn't handle unexpected environmental changes. The company wanted to add vision-based task adaptation, real-time obstacle avoidance, and easier task programming.

Our Solution

We developed a complete software stack combining embedded C++ for real-time control, Python for ML-based perception, and a novel programming interface: - Real-time motion control with 1ms cycle times and <5ms response latency - Vision-based task adaptation using computer vision and reinforcement learning - Automated obstacle detection and avoidance - Simplified task programming interface reducing development time by 80%

Background

Manufacturing automation requires careful coordination of mechanical systems, electronics, and software. Our client manufactured collaborative robots (cobots) designed to work alongside human workers. Their previous generation required extensive programming for each task, limiting adaptability.

The Challenge

Three key problems limited the system's potential:

1. **Task Programming**: Adding a new task took 15-20 hours of engineering time, limiting flexibility 2. **Adaptation**: The robots couldn't adapt to environmental changes (lighting, object variations, layout changes) 3. **Safety**: Real-time obstacle detection and collision avoidance were limited

Our Solution

We architected a multi-layered system:

**Layer 1: Real-Time Control**

  • • Rewrote motion control in C++ with 1ms cycle times

  • • Implemented force-limiting algorithms for safe human-robot interaction

  • • Added real-time obstacle detection with <5ms response time

    **Layer 2: Vision & Perception**

  • • Integrated depth cameras for 3D scene understanding

  • • Built computer vision pipeline for object detection and tracking

  • • Implemented grasp point calculation using ML-trained models

    **Layer 3: Task Programming**

  • • Created visual programming interface (drag-and-drop task creation)

  • • Automated motion planning reducing setup from 20 hours to 4 hours

  • • Enabled non-programmers to define new tasks

    Deployment & Scale

    Successfully deployed across 150+ customer facilities globally:

  • • Comprehensive training program for customer engineers

  • • Remote diagnostics and over-the-air updates

  • • 24/7 support infrastructure built into the system

    Results

    The new system unlocked significant business value:

  • • Customers could adapt to new tasks in hours instead of days

  • • Production flexibility increased, enabling batch-of-one manufacturing

  • • Safety incidents decreased 60% due to improved real-time perception

  • • Customer satisfaction improved dramatically with reduced downtime and faster adaptation
  • Client

    Advanced Manufacturing Equipment OEM

    Industry

    Industrial Automation

    Technologies

    C++
    Python
    CUDA
    ROS
    OpenCV
    CAN bus
    Real-time Linux
    TensorRT

    Key Results

    • 80% reduction

      Setup Time

    • 150+ facilities

      Deployment Speed

    • 99.7%

      Uptime

    • 40%

      Throughput Increase

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