The objective of this paper is to present the model-based 3D object recognition and fetching by a 7-DoF robot with online obstacle avoidance for factory automation. The robot can fetch the random type of 3D objects with arbitrary pose using a 3D visual camera. Object recognition pipeline based on different descriptors are compared in order to decide the best choice for this scenario. A correct grasp is taught by human operator using programming by touch. As a result, the time of configuring and setting up the robot drastically decreases and thus saves a large amount of preparation time before the robot can actually get to work. Considering the safety issue of the human operators that cooperate with the robot, as well as the uncertainty of the environment such that a moving obstacle might present, or the environment might change for some reason, an online obstacle avoidance algorithm is also integrated into the system. Experimental proof of principles has been successfully implemented.

In contrast to structural timing analysis, functional timing analysis for circuit delay computation is accurate, but computationally expensive in refuting false critical paths. Despite recent progress on satisfiability-based functional timing analysis, the formulation generality and computation efficiency remain room for further improvement. This paper provides a unified view on different notions of timed characteristic functions and efficient transformation for satisfiability solving. Experimental results show that functional timing analysis on industrial designs can be made up to several orders of magnitude faster and more generally applicable than prior methods.

Functional, in contrast to structural, timing analysis is accurate, but computationally expensive in refuting false critical paths. Although satisfiability-based analysis using timed characteristic functions has been proposed, its efficiency and generality remain room for improvement. This paper shows functional timing analysis on industrial designs can be made up to several orders of magnitude faster and more generally applicable than prior methods.