Application of Embedded Systems in Industrial Robots

Embedded systems are the "brain" of industrial robots, enabling precision control, real-time decision-making, and seamless automation. They integrate sensors, actuators, and communication modules to perform complex tasks in manufacturing, logistics, and hazardous environments.

Key Applications of Embedded Systems in Industrial Robots

1. Real-Time Motion Control

• Function: Ensures precise movement of robotic arms, grippers, and motors.

• Embedded Components: Microcontrollers (ARM Cortex), DSPs, FPGAs.

• Example:

  • ABB IRB 6700 – Uses embedded controllers to achieve 0.1mm repeatability in automotive assembly.

  • Fanuc R-2000iC – High-speed pick-and-place operations in electronics manufacturing.

2. Sensor Integration & Feedback Systems

• Function: Processes data from force sensors, encoders, LiDAR, and vision systems.

• Embedded Components: ADCs (Analog-to-Digital Converters), signal processors.

• Example:

  • KUKA LBR iiwa – Uses embedded torque sensors for human-robot collaboration (cobots).

  • Universal Robots UR10e – Embedded vision systems for bin-picking in warehouses.

3. Machine Vision & AI-Based Decision Making

• Function: Enables object recognition, defect detection, and path planning.

• Embedded Components: GPU-accelerated SoCs (NVIDIA Jetson, Intel Movidius).

• Example:

  • FANUC M-710iC/50 – Uses embedded AI for real-time quality inspection in production lines.

  • Omron TM Series – Combines OpenCV & embedded AI for sorting tasks.

4. Communication & Industrial IoT (IIoT)

• Function: Connects robots to PLCs, SCADA, and cloud systems for remote monitoring.

• Embedded Components: Ethernet, CAN bus, Wi-Fi/5G modules.

• Example:

  • Yaskawa Motoman HC10 – Embedded OPC UA for Industry 4.0 integration.

  • Siemens SIMATIC Robot Integration – Links robots to digital twin environments.

5. Safety & Fault Detection

• Function: Monitors collisions, overheating, and emergency stops.

• Embedded Components: Safety-certified PLCs (SIL 3/PL e).

• Example:

  • ABB YuMi – Embedded dual-CPU safety system for human collaboration.

  • Kawasaki RS007N – Real-time force feedback to prevent accidents.

6. Energy Efficiency & Power Management

• Function: Optimizes power usage in servo motors and actuators.

• Embedded Components: Power management ICs (PMICs), regenerative braking controllers.

• Example:

  • FANUC Robodrill – Embedded energy-saving algorithms reduce 30% power consumption.

7. Multi-Axis Synchronization

• Function: Coordinates 6+ axes for complex movements (e.g., welding, 3D printing).

• Embedded Components: Real-time OS (RTOS), EtherCAT/PROFINET.

• Example:

  • Stäubli TX2-160 – Embedded EtherCAT for nanosecond-level synchronization.

Future Trends in Embedded Robotics

✔ Edge AI – Faster decision-making without cloud dependency (e.g., NVIDIA Isaac).
✔ 5G-enabled robots – Ultra-low latency for remote control.
✔ Digital twins – Virtual models for predictive maintenance.
✔ Self-learning robots – Reinforcement learning in embedded systems.

Conclusion

Embedded systems make industrial robots smarter, safer, and more efficient, driving automation in automotive, electronics, and logistics. As AI and IoT evolve, embedded robotics will play an even bigger role in Industry 4.0.

In-Depth: Embedded System Architecture in Industrial Robots Using the KUKA LBR iiwa as an Example

1. Hardware Architecture

The KUKA LBR iiwa (Lightweight Robot) employs a modular embedded system architecture with the following core components:

• Main Controller:

  • Xilinx Zynq UltraScale+ MPSoC (Dual-Core ARM Cortex-A53 + FPGA)

  • Responsible for real-time motion planning and safety functions (SIL3)

• Joint-Level Embedded Systems:

  • Each of the 7 joints contains a dedicated STM32H7 microcontroller

  • Executes local control loops (PID) at a 1kHz update rate

• Sensor Network:

  • TI ADS8881 ADCs (24-bit) for high-precision torque measurement

  • iC-Haus joint encoders with 19-bit resolution

2. Real-Time Software Stack

| Layer                 | Technology                  | Cycle Time  |
|-----------------------|-----------------------------|-------------|
| Safety Layer          | KUKA Sunrise.OS (SIL3)      | 2ms         |
| Real-Time Motion      | Xenomai Linux Patch         | 250µs       |
| Hardware Abstraction  | ROS2 Industrial (modified)  | 1ms         |
| Application Logic     | Java/KRL                    | 8ms         |

3. Communication Protocols

• EtherCAT (100Mbit/s):

  • Synchronization jitter <1µs for joint control

  • Topology: Master (Main Controller) → 7 Slave Nodes (Joint Controllers)

• Safety-over-EtherCAT (FSoE):

  • Complies with IEC 61784-3-3 for emergency stop functions

  • Certified response time <15ms

4. Machine Vision Integration

The iiwa can be optionally equipped with an embedded vision system:

• NVIDIA Jetson AGX Orin (32 TOPS AI performance)

• Stereo Camera Baseline: 2x Sony IMX428 (Global Shutter, 12MP)

• Latency:

  • 8ms for object detection (quantized YOLOv5s)

  • 2ms for depth estimation (SGM algorithm on FPGA)

5. Power Management System

• Dynamic Voltage Regulation:

  • Infineon OPTIGA Trust M for secure power monitoring

  • 48V→12V DC/DC converter with 94% efficiency

• Regenerative Braking:

  • Recovers up to 150W to the grid

  • Supercapacitor buffer (Maxwell 48V module)

6. Safety Mechanisms

• Dual-Core Lockstep (Cortex-R5):

  • Compares instructions in real time

  • Error detection in <50ns

• Joint Torque Monitoring:

  • 3 redundant measurement paths per joint

  • ISO 13849 PL e Cat. 3 certified

7. Development Tools

• KUKA Sunrise.Workbench:

  • Eclipse-based IDE with real-time debugging

  • Hardware-in-the-Loop simulation with QNX Momentics

• ROS2 Interface:

  • Custom nodes for real-time data streaming

  • Latency-optimized DDS configuration (Cyclone DDS)

Industrial Use Case: Electric Motor Assembly

• Requirements:

  • Precision: ±0.03mm positioning accuracy

  • Force control: 5N±0.2N

• Implementation:

  1. Main controller calculates trajectory planning (5th order)

  2. FPGA accelerates Jacobian matrix computations

  3. Joint controllers compensate for friction using the LuGre model

• Results:

  • 22% improvement in cycle time compared to conventional robots

  • Zero defects in 250,000 cycles (VDA 6.3 audit)