High-precision assembly tasks cannot be successfully done by robots without taking into account the effect of uncertainties. Often a robot motion may fail and result in some unintended contact between the part held by the robot and the environment. To automatically recover a task from such a failure and to ensure its success in spite of uncertainties, Xiao et al. introduced a systematic replanning approach which consisted of patch-planning based on contact analyses and motion strategy planning based on constraints on nominal and uncertainty parameters of sensing and motion. In order to test the effectiveness of the replanning approach, we have developed a general geometric simulator SimRep on a SUN SPARC Station which implements the replanning algorithms, allows flexible design of task environments and modeling of nominal and uncertainty parameters to run the algorithms, and simulates the kinematic robot motions guided by the replanning algorithms in the presence of uncertainties. This paper introduces the SimRep and discusses the performance of the replanning algorithms tested under SimRep.
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