Experience Digiphusion: Live & Interactive

This interactive demo showcases a simulated environment powered by Digiphusion. Explore real-time data, basic controls, and a glimpse of digital twin capabilities.

Robot Overview

3D visualization of robotic arm on mobile base

Robot Controls

Simulated control interface

Battery87.0%

Speed0.0 m/s

Joint Angles

45.0°

30.0°

60.0°

20.0°

10.0°

0.0°

Live Telemetry

Real-time position and sensor data

System Data

Events and connected device data

[10:00:23]Robot initialized
[10:01:05]Connected to Digiphusion framework
[10:01:12]Sensors calibrated

Robot Vision System

Digiphusion's integrated computer vision capabilities enable real-time object detection, depth sensing, and environmental awareness for robotic systems.

Robot Perception

AI-powered computer vision with multiple sensing modes

RGBDepthThermalEdge

Vision Capabilities

Advanced perception for industrial environments

Multi-Modal Sensing: Toggle between RGB, depth, thermal, and edge detection modes to adapt to different environmental conditions and tasks.
Real-Time Object Detection: Identify objects, track their positions, and estimate confidence levels for industrial components, tools, humans, and more.
ROS 2 Integration: Seamlessly publish vision data to ROS 2 topics for consumption by control algorithms, path planners, and manipulation systems.
Extensible Vision Pipeline: Build custom vision processing stages with Digiphusion's plugin architecture for calibration, filtering, and specialized detectors.

AI Integration

Pre-built connectors for vision models

Digiphusion's vision system includes pre-built connectors for popular machine learning frameworks and models:

TensorFlow/PyTorch

YOLO Object Detection

NVIDIA Isaac

OpenCV AI Kit

Pose Estimation

Segmentation Models

Robot Learning

Watch a robot learn to navigate through an environment using reinforcement learning, a core technique in autonomous robotic systems.

Q-Learning Simulation

Reinforcement learning demonstration showing how robots learn optimal policies through trial and error

Episode: 0 / 100

Steps: 0

Reward: 0.00

Learning Rate: 0.10

Discount Factor: 0.90

Exploration Rate: 0.30

Max Episodes: 100

Episode Rewards

Learning Metrics

Average Reward

0.00

Success Rate

Episodes to Convergence

Total Episodes

Q-Learning Algorithm

Q[s,a] = Q[s,a] + α * (r + γ * max(Q[s',a']) - Q[s,a])

Where:
α (alpha): Learning rate = 0.10
γ (gamma): Discount factor = 0.90
ε (epsilon): Exploration rate = 0.30

How It Works

1
Exploration: The robot tries different actions to discover the environment and collect rewards.
2
Learning: As it explores, the robot updates its Q-table (knowledge base) about which actions are best in each state.
3
Optimization: Over time, the robot learns the optimal path from start to goal, avoiding obstacles efficiently.
4
Convergence: Finally, the robot's policy stabilizes, and it consistently takes the best actions to maximize total reward.

Key Parameters

α
Learning Rate: Controls how quickly the robot incorporates new information, adjusting between fast adaptation and stability.
ε
Exploration Rate: Balances exploration (trying new actions) vs. exploitation (using known good actions).
γ
Discount Factor: Determines how much the robot values future rewards compared to immediate rewards.

Applications in Digiphusion

Digiphusion integrates reinforcement learning to enable robots to:

Learn optimal navigation paths in warehouses
Optimize pick-and-place operations
Adapt to changing environments
Fine-tune motion control for precision tasks
Develop energy-efficient behavior policies

Industrial Automation

Experience how Digiphusion seamlessly integrates with industrial automation systems, connecting robotics with PLCs, sensors, and industrial protocols for comprehensive factory floor control.

Rockwell PLC Interface

Real-time monitoring and control of PLC systems with bidirectional data flow to ROS 2

Allen-Bradley ControlLogix 1756-L83E

IP: 192.168.1.10Response: 8.0msLoss: 0.0%CONNECTED

ConveyorSpeed

Main conveyor belt speed

N7:0 (REAL)

45.50 m/min

Updated: 3:29:59 AM

TankLevel

Primary tank fluid level

N7:1 (REAL)

68.30 %

Updated: 3:29:59 AM

MotorRunning

Main motor status

B3:0/0 (BOOL)

Updated: 3:29:59 AM

EmergencyStop

E-stop button status

I:1/0 (BOOL)

Updated: 3:29:59 AM

BatchCounter

Current production count

C5:0.ACC (DINT)

1247

Updated: 3:29:59 AM

SystemStatus

Current system status

ST9:0 (STRING)

RUNNING

Updated: 3:29:59 AM

TemperatureSetpoint

Temperature controller setpoint

N7:2 (REAL)

85.00 °C

Updated: 3:29:59 AM

PressureSensor

System pressure reading

I:2.0 (REAL)

23.40 Bar

Updated: 3:29:59 AM

Industrial Integration Features

Multi-Protocol Support: Connect to any industrial system with native support for EtherNet/IP, Modbus TCP/RTU, OPC-UA, and MQTT.
Bidirectional Communication: Not just read data but also write commands and setpoints back to industrial equipment from your ROS 2 applications.
Automatic Tag Mapping: Map PLC tags directly to ROS 2 topics with type conversion handled automatically.
Historian Integration: Connect to time-series databases for long-term storage and analysis of industrial data.

Industrial Use Cases

1
Robot-PLC Coordination: Synchronize robot movements with conveyor systems, actuators, and other PLC-controlled equipment.
2
Quality Control: Integrate vision systems with industrial equipment for automated inspection and defect detection.
3
Predictive Maintenance: Monitor equipment performance data and use AI to predict maintenance needs before failures occur.
4
Factory Digital Twin: Create comprehensive digital replicas of entire manufacturing facilities for simulation and optimization.

This is a simulated environment for demonstration purposes. Actual performance may vary.

Ready to create your own digital twin?

Digiphusion makes it easy to connect your physical hardware to powerful digital representations. Visualize data, monitor performance, and control your systems remotely with our powerful ROS 2 framework.