Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: SLAM algorithm applied to robotics assistance for navigation in unknown environments | Auat Cheein et al. Journal of NeuroEngineering and Rehabilitation 2010 7 10 http content 7 1 10 l dl JOURNAL OF NEUROENGINEERING NCR AND REHABILITATION RESEARCH Open Access SLAM algorithm applied to robotics assistance for navigation in unknown environments Fernando A Auat Cheein1 Natalia Lopez2 Carlos M Soria1 Fernando A di Sciascio1 Fernando Lobo Pereira3 Ricardo Carelli1 Abstract Background The combination of robotic tools with assistance technology determines a slightly explored area of applications and advantages for disability or elder people in their daily tasks. Autonomous motorized wheelchair navigation inside an environment behaviour based control of orthopaedic arms or user s preference learning from a friendly interface are some examples of this new field. In this paper a Simultaneous Localization and Mapping SLAM algorithm is implemented to allow the environmental learning by a mobile robot while its navigation is governed by electromyographic signals. The entire system is part autonomous and part user-decision dependent semi-autonomous . The environmental learning executed by the SLAM algorithm and the low level behaviourbased reactions of the mobile robot are robotic autonomous tasks whereas the mobile robot navigation inside an environment is commanded by a Muscle-Computer Interface MCI . Methods In this paper a sequential Extended Kalman Filter EKF feature-based SLAM algorithm is implemented. The features correspond to lines and corners -concave and convex- of the environment. From the SLAM architecture a global metric map of the environment is derived. The electromyographic signals that command the robot s movements can be adapted to the patient s disabilities. For mobile robot navigation purposes five commands were obtained from the MCI turn to the left turn to the right stop start and exit. A kinematic controller to control the mobile robot was implemented. A low level behavior strategy was also implemented to avoid robot
