In context
As electric quadruped robots push toward sustained high-load operation, thermal management has become a bottleneck. Overheating protection typically shuts down motors when temperature thresholds are reached, limiting continuous work. Accurate thermal models are essential for proactive temperature control, yet existing models often ignore heat coupling among multiple heat sources in compact robot structures.
What was reported
Researchers from Huazhong University of Science and Technology and partner institutions developed a whole-body thermal model for quadruped robots using the lumped-parameter thermal network (LPTN) method. The model integrates motor self-heating, heat exchange between motors and the onboard computer, joint friction heat, and forced convective cooling during locomotion. Parameters were identified via least squares on data from coil stator temperature sensors and ambient temperature sensors.
Experiments on a quadruped platform across various speeds, gaits, and load conditions showed that predicted joint temperatures matched actual measurements with a maximum error of less than 5°C, significantly outperforming single-motor thermal models that neglect heat coupling.
The model enables three applications: integrating into simulation environments for online temperature analysis, providing redundant temperature information for motor fault detection, and predicting time-to-threshold for proactive decision-making.
Why it mattered
This work addresses a critical gap in thermal management for legged robots, where compact designs intensify thermal coupling. Accurate whole-body temperature prediction supports longer continuous operation, better control strategies, and more realistic simulation, advancing the practicality of quadruped robots in industrial and field applications.
“The whole-body thermal model prediction error is within 5°C, and its accuracy is significantly improved compared to the single-node motor thermal model.”
Source: 《机器人》期刊 (robot.sia.cn) · Published 2025-03-15 · “基于集总参数热网络法的四足机器人温度分布预测”