With increasing populations of the elderly in our society, robot technology will play an important role in providing functional mobility to humans. From the perspective of human safety, it is desirable that controllers for walk-assist robots be dissipative, i.e., the energy is supplied from the human to the walker, while the controller modulates this energy. The simplest form of a dissipating controller is a brake, where resistive torques are applied to the wheels proportional to their speeds. The fundamental question that we ask in this brief is how to modulate these proportional gains over time for the two wheels so that the walker can perform point-to-point motions. The unique contribution of this brief is a novel way in which the theory of differential flatness is used to plan the trajectory of these braking gains. Since the user input forces are not known a priori, the trajectory of the braking gain is computed iteratively during the motion. Simulation and experimental results show that the walkassist robot, along with the structure of this proposed control scheme, can guide the user to reach the goal.
With the increase of aging population, robot walking helpers have been developed to assist the elderly in their daily life. Based on our previous work, a passive robot walking helper (named i-Go) equipped with a guidance strategy, in this paper, we further propose a navigation scheme for obstacle avoidance. The proposed scheme first locates the waypoints in the collision-free areas, and then guides the i-Go to reach the desired target with the specified orientation. Simulations and experiments are performed to verify the effectiveness of the proposed scheme. The results demonstrate that the scheme can guide the user to avoid the obstacles and successfully approach the target with the specified orientation.
A simple yet effective method to reduce the dimensions of the input variables and is adaptive to various users for intelligent controllers is proposed. The method has been developed specifically to address the challenge due to fuzziness in the system inputs, especially when studying the relationship of a large mapping between input variables and system response outputs. The proposed method exploits the principal components analysis to reduce the number of inputs and uses a fuzzy c-means technique to cluster them. The objective is to extract significant principal components for adaptive neural fuzzy inference systems (ANFIS) learning. The method has been applied to a robotic walker system for elderly movement assistance. Experimental results demonstrate the feasibility of the proposed method.
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