This paper presents a finite state-based control system for a powered transfemoral prosthesis that provides stair ascent and descent capability. The control system was implemented on a powered prosthesis and evaluated by a unilateral, transfemoral amputee subject. The ability of the powered prosthesis to provide stair ascent and descent capability was assessed by comparing the gait kinematics, as recorded by a motion capture system, with the kinematics provided by a passive prosthesis, in addition to those recorded from a set of healthy subjects. The results indicate that the powered prosthesis provides gait kinematics that are considerably more representative of healthy gait, relative to the passive prosthesis, for both stair ascent and descent.
Engineers have long employed control systems utilizing models and feedback loops to control real-world systems. Limitations of model-based control led to a generation of intelligent control techniques such as adaptive and fuzzy control. The human brain, on the other hand, is known to process a variety of inputs in parallel, and shift between different levels of cognitive activities while ignoring distractions to focus on the task in hand. This process, known as cognitive control in psychology, is unique to humans and a handful of animals. We are interested in implementing such cognitive control functionalities for our humanoid robot ISAC. This paper outlines the features of multiagent-based cognitive architecture for a humanoid robot and the progress made toward the realization of cognitive control functionalities using attention, working memory and internal rehearsal. Several experiments have been conducted to show that the implementation of an integrated cognitive robot architecture is feasible.
This paper proposes an extension of the original Dynamic Movement Primitive (DMP) algorithm proposed by S. Schaal to imitation learning for object avoidance in a dynamic environment. A potential field was incorporated into the original DMP algorithm by using a virtual goal position which is calculated using a potential field. A humanoid robot ISAC was trained in simulation to learn how to generate movements similar to the demonstrated movements when an obstacle is placed in the environment. This proposed extension provides robots more robust and flexible movement generation when an obstacle exists. Simulations were performed to verify the effectiveness of the method.
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