A Human Reaching Movement Model for Myoelectric Prosthesis Control

This paper proposes a reaching movement model for the generation of desired trajectories within a myoelectric prosthesis training system. First, an experiment was performed to observe reaching movements with a non-impaired subject and a myoelectric prosthesis user. Reaching movements made by the prosthesis user were then adopted to construct a model based on a logistic function. The proposed model can be used to generate three trajectory types with a bell-shaped speed profile with the adjustment of only a few parameters.This work was partially supported by a Grant-in-Aid for Young Scientists B Number 26730111

An artificial potential field based mobile robot navigation method to prevent from deadlock

Artificial Potential Filed (APF) is the most well-known method that is used in mobile robot path planning, however, the shortcoming is that the local minima. To overcome this issue, we present a deadlock free APF based path planning algorithm for mobile robot navigation. The Proposed-APF (P-APF) algorithm searches the goal point in unknown 2D environments. This method is capable of escaping from deadlock and non-reachability problems of mobile robot navigation. In this method, the effective front-face obstacle information associated with the velocity direction is used to modify the Traditional APF (T-APF) algorithm. This modification solves the deadlock problem that the T-APF algorithm often converges to local minima. The proposed algorithm is explained in details and to show the effectiveness of the proposed approach, the simulation experiments were carried out in the MATLAB environment. Furthermore, the numerical analysis of the proposed approach is given to prove a deadlock free motion of the mobile robot