In this work, we propose a method able to find user-oriented gait trajectories that can be used in powered lower limb orthosis applications. Most research related to active orthotic devices focuses on solving hardware issues. However, the problem of generating a set of joint trajectories that are user-oriented still persists. The proposed method uses principal component analysis to extract shared features from a gait dataset, taking into consideration gait-related variables such as joint angle information and the user's anthropometric features, used directly in an orthosis application. The trajectories of joint angles used by the model are represented by a given number of harmonics according to their respective Fourier series analyses. This representation allows better performance of the model, whose capability to generate gait information is validated through experiments using a real active orthotic device, analysing both joint motor energy consumption and user metabolic effort.

This paper deals with the design of the control structure of a lower-limbs rehabilitation device in closed muscular chain called Sys-Reeduc. This control structure aims at providing a safe behavior to the user when performing rehabilitation exercises. It is based on two levels. The first level is concerned with the robust trajectory tracking of robotic device and has been the subject of previous studies. Nevertheless, it does not allow, by itself, the user to voluntarily drive the device. Therefore, a trajectory generator constituting the second level is presented in this paper to complete the whole control structure. This high-level control layer is described by a set of dedicated discrete state machines that provide the appropriate sequencing of elementary rehabilitation movements. These elementary movements are dynamically characterized so that clinician may choose the required trajectory parameters to adapt rehabilitation protocols and training to each individual. To realize a complete rehabilitation exercise, the sequence of elementary movements is triggered by thresholds relative to the measurement of the efforts applied by the user on the device. This allows the user to play an active role in its rehabilitation exercises and safely drive the machine at his/her own initiative. The design of the main exercises (isokinetic, isometric, and isotonic) used in the context of lower limbs rehabilitation is described, and simulation results illustrate the effectiveness of the proposed trajectory generator-based control approach.

An algorithm is presented for the on-line generation of minimum-time trajectories for robot manipulators. The algorithm is designed for intelligent robots with advanced on-board sensory equipment which can provide the position and orientation of the end-effector. Planning is performed in the configuration (joint) space by the use of optimised combined polynomial splines, along with a search technique to identify the best minimum-time trajectory. The method proposed considers all physical and dynamical limitations inherent in the manipulator design, in addition to any geometric path constraints. Meeting the demands of the heavy computations involved lead to a distributed formulation on a multiprocessor system, for which an intelligent control unit has been created to supervise its proper and practical implementation. Simulation results of a proposed case study are presented for a PUMA 560 robot manipulator.

This paper considers a dynamic and adaptive trajectory generator for negotiating paths using S-curves. Applying constant jerk transitions between the constant acceleration and deceleration periods of the trajectory, the trajectory will optimally transition to the desired speed setting. Optimal is defined to be the minimum time to transition from the current speed to the set speed for the move segment when jerk and acceleration are limited. The S-curve equations will adapt to instantaneous changes in speed setting and path length. An integrated motion planner will determine allowable speeds and transitional profiles based on the remaining move distance.