Karl Van Wyk

Grasping the Performance: Facilitating Replicable Performance Measures via Benchmarking and Standardized Methodologies

It is comparatively easy to make computers exhibit adult-level performance on intelligence tests or playing checkers, and difficult to give them the skills of a one-year-old when it comes to perception and dexterity. More than 15 years after it was first stated, Moravecs paradox still holds true today. Fueled by vigorous research in machine learning, the gap has consistently narrowed on the perception side. However, most of the fine manual motor skills displayed by a toddler are, to date, far beyond what robots can do. It is true that many valuable tasks involving physical interaction with objects can be solved by contemporary robots as indicated by a thriving industrial robotics sector. However, in the future, robots are expected to work side by side with humans in unstructured environments, and the ability to reliably grasp and manipulate objects used in everyday activities will be an unavoidable requirement. Todays robots are far from being ready for this challenge.

Simplified framework for robot coordinate registration for manufacturing applications

A simplified framework is introduced for automatically and quickly registering the Cartesian coordinate systems of industrial robots to any other arbitrary coordinate system. This framework includes both explicit and implicit (sensor-based) registration techniques using as few as three reference poses per robot, and presents different methods for measuring registration uncertainty. Driven by the guiding principles of simplifying the registration process to enable rapid installation by non-expert users, a mathematical basis for fast system registration is presented. We also present methods for quickly and inexpensively approximating the registration errors, and outline mechanisms for improving registration performance. Several case study examples are provided in which the registration performance is captured across four different registration methods, and two different robots. A reference motion capture system is used to capture post-registration positioning accuracy of the robots, a sampling-based registration estimation technique is assessed, and results are systematically quantified.