Edge AI-Based Industrial Robotics Control System

In Industry 4.0, OneData empowers manufacturers with a cutting-edge Edge AI-Based Industrial Robotics Control System that combines IoT connectivity, edge computing, AI/ML and robotics orchestration. This solution enables industrial robots to perform complex tasks with adaptive control, real-time safety monitoring and predictive maintenance while staying connected to the cloud for analytics and oversight.

Edge Robotics & Smart Sensor Integration

OneData builds architecture in which industrial robots (robotic arms, autonomous mobile robots – AMRs, conveyor robots) are equipped with sensors (motion, torque, vision, proximity). These sensors feed into edge compute nodes running AWS IoT Greengrass V2, enabling low-latency, real-time decision-making at the robot/control level.

AI-Driven Movement Planning & Adaptive Control

Using AWS SageMaker Edge, the system runs machine-learning models directly on edge devices to plan robot paths, adapt to changing conditions on the floor (e.g., variation in part placement, dynamic obstacles), and optimise robot motion for efficiency and safety. The models continuously learn from data streams to improve trajectories, minimize collision risk, and reduce idle time.

Robotics Orchestration & Monitoring

With AWS RoboMaker, OneData integrates robot simulation, deployment, orchestration, and fleet management. AWS Kinesis ingests real-time telemetry which is logged, streamed and processed. AWS CloudWatch provides monitoring, logging and alerting for robot health, anomalies, actuator wear and system events.

Predictive Maintenance & Safety Assurance

By streaming actuator, sensor and robot status telemetry, the platform applies predictive analytics to detect early signs of wear, misalignment, or anomaly in robot behavior. This enables maintenance to be scheduled proactively rather than reactively, reducing downtime, extending asset life and improving safety. Safety monitoring features trigger alerts or active mitigation when proximity or collision sensors detect unsafe conditions.

Use Cases

• Robotic Arms: Automating pick-and-place, welding or assembly tasks, with AI-optimised motion paths, dynamic obstacle avoidance and adaptive task switching.

• Autonomous Mobile Robots (AMRs): Managing material transport within factories, optimising routes in dynamic environments, adapting to human/vehicle traffic in real time.

• Assembly Line Robots: Integrating multiple robots on a line, coordinating sequencing, hand-offs between robots and conveyors, balancing workloads in real time.

Benefits

• Smarter Motion Control: Edge AI enables each robot to adapt in real time to changes in the environment or task, making motion smoother and faster.

• Reduced Errors & Rework: Adaptive control reduces mis-picks, collisions, line stoppages, thereby improving quality and throughput.

• Predictive Maintenance for Actuators: Early detection of wear or anomalies in robot parts means fewer unplanned stoppages and extended equipment life.

• Enhanced Safety: Real-time edge monitoring of robot-environment interaction and quick corrective action reduces safety incidents.

• Scalable & Cloud-Connected Architecture: While intelligence is at the edge for fast response, cloud analytics capture fleet-wide data for continuous improvement, analytics and reporting.