How is edge AI used in robotics?

Edge AI in robotics enables robots to process data locally on their hardware instead of relying on cloud-based systems. This approach reduces latency, improves reliability in environments with poor connectivity, and allows robots to make real-time decisions. By embedding AI models directly into the robot’s onboard processors, tasks like object recognition, navigation, or motion planning can be executed faster and with greater autonomy. For example, a warehouse robot using edge AI can identify and sort packages without waiting for remote servers to analyze camera feeds, ensuring continuous operation even if network access is disrupted.

One common application is in autonomous mobile robots (AMRs) used in logistics or manufacturing. These robots rely on edge AI to process sensor data from cameras, LiDAR, or infrared sensors to navigate dynamic environments. For instance, an AMR might use a lightweight neural network running on an embedded GPU to detect obstacles and adjust its path in milliseconds. Similarly, industrial robotic arms equipped with edge AI can perform quality inspections by analyzing visual data from production lines. This eliminates the need to transmit large video streams to external servers, reducing bandwidth costs and enabling faster response times. Edge AI also supports collaborative robots (cobots) that work alongside humans, where real-time safety checks—like detecting a worker’s hand near a moving tool—are critical.

Developers implementing edge AI in robotics face challenges like balancing computational constraints with model accuracy. For example, deploying a complex vision model on a robot with limited power requires optimizing the model through techniques like quantization or pruning. Frameworks such as TensorFlow Lite or ONNX Runtime help convert and deploy models tailored for edge devices like NVIDIA Jetson or Raspberry Pi. Additionally, edge AI systems must handle hardware failures or sensor noise autonomously. A practical approach involves designing fallback mechanisms, such as switching to simpler algorithms if the primary model fails. By focusing on efficient code, hardware-aware model design, and rigorous testing, developers can create robust robotic systems that leverage edge AI effectively.