Abstract: The exoskeleton robot has achieved success in multiple laboratory environments, but there are still a series of challenges related to the controller hardware in real-world deployment, including large controller size, insufficient real-time performance, and computational resource bottlenecks. To address these issues, a scalable hardware circuit system for the exoskeleton robot, balancing both real-time performance and high computational power characteristics is developel. By modeling the signal flow of the controller hardware, optimizing communication lines and traffic distribution, the system’s communication delay is reduced. Based on the varying computational power demands of the exoskeleton robot in space and time, a gait-based real-time task scheduling algorithm is designed to optimize the allocation of computational resources. The computational accelerator is designed using parallel pipelining and module folding to effectively reduce hardware resource consumption and improve system performance. Real-time performance tests, computational power tests, and load-walking experiments with the exoskeleton robot are conducted. The tests showed that the designed controller’s master-slave communication delay is less than 20 μs, and the computational power efficiency is 15 GOPS/W, demonstrating low latency and high computational power compared to mainstream controller architectures. The exoskeleton robot demonstrated excellent tracking performance during weighted walking on flat ground and uphill, laying the hardware foundation for its broader application.

Key words: exoskeleton robots, controller hardware, real-time performance, task scheduling, computational optimization