This paper introduces a novel assistive device (AD) control strategy that provides intermittent assistance and is capable of encouraging user effort during AD use. The assistance regulation controller (ARC) presented here is designed to regulate joint torque and can be used for arbitrary joint and limb motions without requiring any a priori information about the user's intended motion. Data from eight healthy subjects who performed a forearm tracking experiment using a single degree-of-freedom powered orthosis demonstrate that the ARC is capable of encouraging user effort. On average, subjects generated 40% more joint torque when performing the tracking experiment with the ARC instead of a conventional torque-amplifying controller. The results also demonstrate that the ARC is safe to use for dynamic orthosis-assisted joint motions which require several transitions into and out of the assistance and no assistance states of the controller. As such, the results also confirm that the ARC is an effective assist-as-needed control strategy suitable for use with ADs.

This paper presents a novel approach to solve multiobjective robotic trajectory planning problems. It proposes to find the Pareto optimal set, rather than a single solution usually obtained through scalarization, e.g., weighting the objective functions. Using the trajectory planning problem for a redundant manipulator as part of a captive trajectory simulation system, the general discrete dynamic programming (DDP) approximation method presented in our previous work is shown to be a promising approach to obtain a close representation of the Pareto optimal set. When compared with the set obtained by varying the weights, the results confirm that the DDP approximation method can find approximate Pareto objective vectors, where the weighting method fails, and can generally provide a closer representation of the actual Pareto optimal set.

This paper studies the problem of generating optimal joint trajectories for redundant manipulators when multiple criteria need to be considered and proposes a novel approach based on dynamic programming and the use of the Pareto optimality condition. The drawbacks of the traditional weighting method in optimization for generating the Pareto optimal set are discussed and an alternate approach using dynamic programming is proposed. The two approaches are implemented on the model of a 7-DOF redundant manipulator with the end-effector moving along a prescribed trajectory, while the joint trajectories are required to minimize two particular criteria. The results illustrate that the dynamic programming approach provides a better approximation of the Pareto optimal set and a more flexible and predictable framework to control the objective vectors.