During robot data acquisition, frame loss is almost an unavoidable hurdle. Whether it's the encoders on a wheeled chassis, the torque sensors in a robotic arm's joints, or the cameras used for visual navigation, any gap in the data stream can cause trajectory calculation errors at best, or lead to incorrect control logic decisions at worst. Many people's first reaction is to "switch to better hardware," but upon closer inspection, the root cause of many frame loss issues lies not in the sensors themselves, but in a easily overlooked link within the acquisition chain.
The most common cause is the acquisition thread being preempted by higher-priority tasks. Robot systems typically run multiple real-time processes simultaneously, such as motion control, path planning, and state estimation. If the priority of the data acquisition thread is not properly configured, or if the real-time kernel scheduling policy is not enabled, the acquisition thread can be "pushed aside" when the system gets busy. By the time it regains CPU time slices, the data in the buffer may already be overwritten by new data, or the timestamps may be scrambled. The solution is straightforward: bind the acquisition thread to a dedicated CPU core, assign it a sufficiently high real-time priority, and ensure that the receiving queue has enough depth to buffer short-term scheduling jitter.
Another easily overlooked pitfall is the mismatch between bus bandwidth and transmission protocol. For example, when multiple sensors share the same physical link via CAN bus or serial ports, the arbitration mechanism will actively discard lower-priority data frames when the bus traffic approaches saturation. In such cases, relying solely on software to "fill in the gaps" won't work. A better approach is to adjust the sampling frequency and frame format of each sensor, or upgrade to a transmission solution with more ample bandwidth, such as switching from CAN 2.0 to CAN FD, or adopting a real-time Ethernet protocol like EtherCAT.
There is also a type of frame loss that is actually a "false frame loss" caused by incorrect timestamps. The sensor data itself arrives, but the driver assigns timestamps to the frames uniformly before enqueuing them, causing multi-channel data to be misaligned in time. The downstream fusion algorithm then mistakenly believes that a certain frame is missing. In this case, it is necessary to check whether each driver uses hardware timestamps, or to embed the sensor's own clock in the frame header, followed by time synchronization calibration.
The worst way to handle frame loss is to "wait rigidly" at the application layer. Instead of blocking the control cycle, it is better to design a state machine: when the continuous frame loss rate is below a certain threshold, continue running; when it exceeds the threshold, switch to a degraded mode, such as reducing motion speed and pausing certain non-critical functions, while recording the timestamps and context of frame loss for post-event analysis. This way, the robot will not put itself in danger in pursuit of perfect data.
In the final analysis, frame loss is an inevitable non-ideal state in robot systems. Our goal is not to completely eliminate it, but to ensure that the system remains safe and controllable when frame loss occurs. This requires joint efforts across multiple layers, including task scheduling, transmission links, time synchronization, and application logic. Each step is not complicated, but every step must be done solidly and correctly.