Abstract
Geometric uncertainties may jeopardize the performance of parallel manipulators, especially during motion planning. Recent research demonstrated that, during motion planning and due to uncertainties, manipulators may accidentally assume low performance or singular configurations. Thus, reliable motion planning algorithms are required. Very few algorithms were proposed to avoid such problem in parallel manipulators. This paper presents a reliable motion planning technique. First, failure modes are defined. Then, a Monte Carlo simulation is used to provide information on how the manipulator’s uncertainties affect its conditioning. Based on this simulation, probabilities of failure are computed for several manipulator workspace configurations. After that, an artificial neural network metamodel is trained to overcome Monte Carlo’s computational inefficiency on the failure probability estimation. This metamodel is assessed by an iterative strategy that exploits genetic operators to compute optimal trajectories avoiding regions that are considerably affected by uncertainties. Due to its modular methodology, the technique can be easily adapted for different applications. A 3RRR manipulator is used as a case study.
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