Key Challenges in Modern Robotic Systems
Although modern robotic systems have achieved remarkable progress in perception and computational capabilities, they continue to suffer from a fundamental limitation — the absence of time and data synchronization. The industry faces three foundational challenges.
Problem 1: Communication Architecture Limitations
As robots integrate an increasing number of sensors and actuators, existing communication architectures — such as Ethernet, CAN, and serial interfaces — often fail to deliver deterministic, real-time data transfer. This leads to latency and instability in motion control
Problem 2: Sensor Data Synchronization
A wide range of sensors — including IMUs, cameras, ultrasonic sensors, radar, and 2D/3D LiDAR — operate on different interfaces and clocks, making it challenging to precisely align their data on a unified, reliable timeline. When robots collect data from these sensors, inconsistencies in data acquisition timing require significant software effort to synchronize the information. However, even after extensive processing, millisecond-level errors may remain, leading to potential misjudgments by the robot. This misalignment directly affects localization and AI perception accuracy.
Problem 3: Fragmented Data Flow to AI Computing
Moreover, the data flow from sensors to AI computing units is often lengthy and fragmented, reducing responsiveness and decision-making efficiency. Since the collected data from each sensor fragment is not time-synchronized, when the AI executes tasks such as VLM (Visual Language Model), MLM (Motion Language Model), or other language models, it can result in prediction, perception, and decision-making errors. Ultimately, robots fail not because of weak AI, but because the system is not synchronized.
