The kinematic redundancy in a robot leads to an infinite number of solutions for inverse kinematics, which implies the possibility to select a "best" solution according to an optimization criterion. In this paper, two optimization objective functions are proposed, aiming at either minimizing extra degrees of freedom (DOFs) or minimizing the total potential energy of a multilink redundant robot. Physical constraints of either equality or inequality types are taken into consideration in the objective functions. Since the closed-form solutions do not exist in general for highly nonlinear and constrained optimization problems, we adopt and develop two numerical methods, which are verified to be effective and precise in solving the two optimization problems associated with the redundant inverse kinematics. We first verify that the well established trajectory following method can precisely solve the two optimization problems, but is computation intensive. To reduce the computation time, a sequential approach that combines the sequential quadratic programming and iterative Newton-Raphson algorithm is developed. A 4-DOF Fujitsu Hoap-1 humanoid robot arm is used as a prototype to validate the effectiveness of the proposed optimization solutions.
In this paper we develop a dynamic model to mimic and simulate human falling process when tripping over an obstacle. The work consists of three parts. First a five-link gait model is established and the joint trajectories of knee and hip are generated. Next several constraints including the physical, physiological and environmental constraints are taken into consideration. Hence the fall process can be modelled in a realistic manner. Next a number of important factors that characterize the fall process, such as reaction time, inclining angle, stable range, initial speed, are investigated. The whole falling process is formulated according to biological characteristics and stability analysis, which offers an effective way to identify and distinguish falling from normal activities, so that the falling can be predicted and a prevention device can be activated in advance.
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