Metodologia para desenvolvimento de sistemas robóticos: estudo de caso em dispositivos de reabilitação de marcha

Abstract

Robotics has advanced with innovations in mechanics, computing, and electronics, expanding its applications. However, software development for robots still faces challenges due to the complexity of tasks and the diversity of hardware and environments. Although tools like ROS facilitate research, it is essential to structure software and hardware architectures systematically. This work proposes a methodology to standardize the development of robotic systems for gait rehabilitation and assistance. The methodology was validated in two case studies. In the first case study, the proposed methodology was applied to two robotic walkers (UFES vWalker and UFES WalkerXR), using ROS and ROS2, for validation and evaluation based on technical analyses and user experience. Positive results were obtained for the technical analysis, with a maximum standard deviation in ROS publishing frequency of 13.24% for the vWalker and 7.43% for the WalkerXR within the specified frequency. Additionally, the device acceptability level was satisfactory according to the System Usability Scale (SUS) , with scores of 77 for the vWalker and 86 for the WalkerXR. In the second case study, a telepresence system was developed by integrating a mobile robot, a 360° camera, and the Discover2Walk device. Mapping tests confirmed the system’s accuracy, with an average error of 0.52% in measured distances. Furthermore, it was possible to synchronize the movement of the mobile robot with the Discover2Walk treadmill, ensuring an immersive experience for the remote user. The proposed methodology demonstrated effectiveness by providing a more organized and predictable development process. The results confirm that the approach favors the creation of robust and adaptable solutions. However, the need for testing in new scenarios should still be explored. Future work suggests applying the methodology to other areas of robotics, conducting long-term studies with real users, and incorporating artificial intelligence to personalize assistive systems.