This paper presents an optimization of an admittance control model for gait balance assistance offered by a walker-type assistive robot. We previously introduced the notion of quasi-passive physical Human-Robot Interaction (pHRI) where a non-wearable assistive device adaptively achieves supportability for providing physical assistance and operability to follow the user's intuitive operation. Aiming to mitigate the falling risk of elderly people with reduced mobility with our pHRI approach, we propose a hierarchical algorithm to optimize an admittance control model for a walker robot. By employing dynamic trajectories such as Zero Moment Point (ZMP) and Divergent Component of Motion (DCM) with optimization, our controller provides appropriate physical interaction to improve the gait stability while considering intrinsic body dynamics. In the current implementation, based on a model predictive control (MPC) framework, we formulate the optimization problems in the form of quadratic programming (QP), making the optimization suitable for real-time interaction. Through mechanical assessments with passive walking models of compass gait, we demonstrate the feasibility of our proposed optimization framework in stabilizing the limit cycle gait with minimized assistance.

In this paper, we introduce a walking load control strategy with a cane-type robot for gait training. The population of elderly people is increasing in most of the developed countries, and there is a growing demand for caregivers. Assistive robots are expected as a solution to provide aid in order to reduce the burden of caregivers in gait training of elderly people. Our research group has been developing a series of cane-type walking assistive robots named Intelligent Cane on admittance control to provide safe and efficient gait training. We propose an adaptive design method of the admittance control model in order to control user's walking load for the purpose of rapid improvement of user's physical strength. In the proposed method, the cane robot automatically adjusts control parameters of an admittance control model based on a correlation between a set of admittance parameters and a user's walking energy consumption. We conducted experiments to evaluate the proposed adaptive control strategy through a convergence error between estimated walking load and target one while walking training. We confirm the advantage of using the cane robot in rehabilitation.

A cane-type robot called intelligent cane has been developed to support the elderly during walking. By supporting a part of a user's body weight, the cane robot aims to reduce a load applied to a user's affected leg. Therefore, while the user's affected leg is a support leg, it is preferable that the cane robot stops to sufficiently support the user. In our previous work, the cane robot is controlled based on horizontal component of force applied to the cane robot and moment around a vertical axis. In this paper, virtual friction force, which is proportional to vertical component of force, is proposed to improve a walking assistance capability of the cane robot. In addition, virtual frictional coefficients are arranged based on the user's state inferred by a laser range finder. By employing the proposed method, the cane robot moves easily in the both legs support phase, stops in the healthy leg support phase, and supports the user reliably in the affected leg support phase.