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Orienting parts that measure only a few micrometers in diameter introduces several challenges that need not be considered at the macro‐scale. First, there are several kinds of sticking effects due to Van der Waals forces and static electricity, which complicate hand‐off motions and release of a part. Second, the degrees of freedom of micro‐manipulators are limited. This paper proposes a pair of manipulation primitives and a complete algorithm that addresses these challenges. We will show that a sequence of these two manipulation primitives can uniquely orient any asymmetric part while maintaining contact without sensing. This allows us to apply the same plan to many (identical) parts simultaneously. For asymmetric parts we can find a plan of length O(n) in O(n) time that orients the part, where n is the number of vertices.

The following article is a “Q&A interview” conducted by Joanne Pransky of Industrial Robot Journal as a method to impart the combined technological, business, and personal experience of a prominent, robotic industry PhD and inventor regarding his pioneering efforts and the commercialization of bringing a technological invention to market. The paper aims to discuss these issues.

The interviewee is Dr Ken Goldberg, an inventor working at the intersection of art, robotics, and social media. He joined the UC Berkeley faculty in 1995 where he is the UC Berkeley William S. Floyd Jr Distinguished Chair in Engineering and recently served as Chair of the Industrial Engineering and Operations Research Department. He has secondary appointments in UC Berkeley’s Electrical Engineering/Computer Science, Art Practice and the School of Information. Goldberg also holds an appointment at the UC San Francisco Medical School’s Department of Radiation Oncology where he pursues research in medical robotics. Goldberg is Director of the CITRIS “People and Robots” Initiative and the UC Berkeley’s Laboratory for Automation Science and Engineering (AUTOLAB) where he and his students research machine learning for robotics and automation in warehouses, homes, and operating rooms. In this interview, Goldberg shares some of his personal and business perspectives from his career-long pursuit of making robots less clumsy.

Goldberg earned dual BS degrees in Electrical Engineering and Economics from the University of Pennsylvania in 1984, and MS and PhD degrees in Computer Science from Carnegie Mellon University in 1990. Goldberg also studied at Edinburgh University and the Technion. From 1991-95 he taught at the University of Southern California, and in fall 2000, he was visiting faculty at the MIT Media Lab. Goldberg and his students pursue research in three primary areas: Geometric Algorithms for Automation, Cloud Robotics, and Robot Learning.

Goldberg developed the first complete algorithms for part feeding and part fixturing, and developed the first robot on the Internet. His inventions have been awarded nine US Patents. Goldberg has published over 250 peer-reviewed technical papers and edited four books. He co-founded and served as Editor-in-Chief of the IEEE Transactions on Automation Science and Engineering (T-ASE). He is also Co-Founder of the Berkeley AI Research (BAIR) Lab, the Berkeley Center for New Media (BCNM), the African Robotics Network (AFRON), the Center for Automation and Learning for Medical Robotics (CAL-MR), the CITRIS Data and Democracy Initiative (DDI), Hybrid Wisdom Labs, and Moxie Institute. He has presented over four hundred keynote and invited lectures. Goldberg's artwork, closely linked with his research, has appeared in over seventy venues. Ken was awarded the Presidential Faculty Fellowship in 1995 by Bill Clinton, the Joseph Engelberger Robotics Award in 2000, elected IEEE Fellow in 2005, and selected by the IEEE Robotics and Automation Society for the George Saridis Leadership Award in 2016.

Reviews Peshkin and Sanderson (1988) who showed that parts can be aligned as they move on a conveyor belt against a passive sequence of fences. Describes the first complete algorithm to design such sequences for a given convex polygonal part. The algorithm is complete in the sense that it is guaranteed to find a design if one exists and to terminate with a negative report otherwise. Based on an exact breadth‐first search of the design space, the algorithm is also guaranteed to find the design requiring the fewest fences. Describes the algorithm and compares results with those previously reported. Conjectures that a fence design exists to orient any convex polygonal part defined by a sequence of rational vertices.

This paper aims to study a commercially available industrial part feeder that uses an industrial robot arm and computer vision system. Three conveyor belts are arranged to singulate and circulate parts, bringing them under a camera where their pose is recognized and subsequently manipulated by the robot arm. The problem is addressed of optimizing belt speeds and hence throughput of this feeder that avoid: starvation, where no parts are visible to the camera and saturation, where too many parts prevent part pose detection or grasping.

Models are developed for intermittent and continuous motion feeding based on a 2D Poisson process. Renewal theory is applied to model intermittent motion and an M/G/1 queue with customer impatience to model continuous motion feeding. These models are verified using discrete event simulation.

The models predict and optimize feeder behaviour very accurately and it is possible to compute optimal settings for different part sizes and throughput sensitivity.

Feeder belt velocities are currently estimated based on intuition and ad hoc trial and error. The results provide a scientific alternative. The models are straightforward to implement and can provide velocity settings for feeders in industrial use.

This paper advances the scientific understanding of automation and part feeding.

Examines the available sources for current research into improving the performance of mechanical part feeders.

Fixtures and feeders are important components of automated assembly systems: fixtures accurately hold parts and feeders move parts into alignment. These components can fail when part shape varies. Parametric tolerance classes specify how much variation is allowable. In this paper we consider fixturing convex polygonal parts using right‐angle brackets and feeding polygonal parts on conveyor belts using sequences of vertical fences. For both cases, we define new tolerance classes and give algorithms for computing the parameter specifications such that the fixture or feeder will work for all parts in the tolerance class. For fixturing we give an O(1) algorithm to compute the dimensions of rectangular tolerance zones. For feeding we give an O(n²) algorithm to compute the radius of the largest allowable tolerance zone around each vertex. For each, we give an O(n) time algorithm for testing if an n‐sided part is in the tolerance class.

We study a programmable robotic part feeder that relies on a sequence of three conveyor belts to singulate and re‐circulate parts. In industrial practice, belt speeds are set in an ad hoc fashion. Experience with real feeders reveals that throughput can suffer owing to: starvation where no parts are visible to the camera; and saturation, where too many parts are visible, which prevents identifying part pose or grasping due to obstruction by nearby parts. This motivates our search for a systematic approach to setting belt speeds. Our goal is to optimize throughput, measured in terms of how many parts per second are delivered from the robotic feeder. We describe a 1D model of the belts with a Poisson arrival process to stochastically model how belt speeds affect throughput. Initially, we study the finite case where N parts are placed into the feeder and re‐circulated until they are all delivered by the robot. Our first insight is that the vision belt should be run at maximum achievable velocity. We run simulations to empirically determine optimal buffer belt velocity as a function of lot size. Finally, we develop a theoretical model for the case where N = ∞ which approximates common usage where the buffer is replenished before it becomes empty. From this model, we derive the optimal buffer belt velocity and show that it produces throughput five times greater than that achieved with ad hoc settings.

Proposes a simple bowl feeder primitive, consisting of one horizontally mounted convex polygonal metal “blade” that can feed a broad class of three‐dimensional polyhedral parts by reorienting and rejecting all but those in a desired orientation. Owing to its simplicity, the proposed primitive allows for the development of methods to automate its design process.

Presents a computational geometric approach to construct the solution space for a given part and then use this space to report all designs that feed the part.

Given a polyhedral part and its center of mass as input, the complete algorithm identifies all single blade solutions that feed the part. The output is either the set of all valid blade designs or a notification that the part cannot be fed using a single blade.

Aims to take a first step in the design of complete algorithms for three‐dimensional parts in the context of vibratory bowls. Future research encompasses the relaxation of several simplifying assumptions with regard to the physical modeling of the motion and interaction with the part.

Algorithms like the one proposed can be applied to generate an initial vibratory bowl design. The strength of our algorithm lies in its completeness which means that it identifies the complete universe of all possible designs. Such a rigorous exploration can neither be accomplished through human trail‐and‐error nor through heuristic approaches to automated design.

Proposes the first complete algorithm for automated design of a 3D part manipulator for vibratory bowls, which may serve as a building block for fully automated bowl design.

Semiconductor manufacturing industry requires highly accurate robot operation with short install/setup downtime.

We develop a fast, low cost and easy‐to‐operate calibration system for wafer‐handling robots. The system is defined by a fixture and a simple compensation algorithm. Given robot repeatability, end effector uncertainties, and the tolerance requirements of wafer placement points, we derive fixture design and placement specifications based on a statistical tolerance model.

By employing the fixture‐based calibration, we successfully relax the tolerance requirement of the end effector by 20 times.

Semiconductor manufacturing requires fast and easy‐to‐operate calibration systems for wafer‐handling robots. In this paper, we describe a new methodology to solve this problem using fixtures. We develop fixture design criteria and a simple compensate algorithm to satisfy calibration requirements. We also verify our approach by a physical example.