A Predictive Approach to Redundancy Resolution for Robot Manipulators

Abstract

In this paper, we propose a new method for the online redundancy resolution of robot manipulators, which implements a predictive strategy to calculate the optimal control action. In this way, it is possible to obtain a more efficient handling of the constraints, which represents one of the main issues in online resolution methods. The predictive model has been obtained by considering every joint as a kth-order integral system, and the predictive equations are derived from a continuous-time formulation. This allows the use of an irregular distribution of the prediction and control time instants and, as a consequence, longer prediction and control horizons can be obtained, without increasing the computational complexity of the algorithm. Finally, joint hard bounds are easily included in a linear-model-predictive-like framework, and the optimal control action is calculated by solving a linear quadratic problem. Simulation results for a 4-degree-of-freedom planar arm show the effectiveness of the method compared to purely local resolution techniques.