Brian Randolph Carlisle

A pivoting gripper for feeding industrial parts

To be cost effective and highly precise, many industrial assembly robots have only four degrees of freedom (DOF) plus a binary pneumatic gripper. Such robots commonly permit parts to be rotated only about a vertical axis. However it is often necessary to reorient parts about other axes prior to assembly. In this paper the authors describe a way to orient parts about an arbitrary axis by introducing a rotating bearing between the jaws of a simple gripper. Based on this mechanism, the authors are developing a rapidly configurable vision-based system for feeding parts. In this system, a camera determines initial part pose; the robot then reorients the part to achieve a desired final pose. The authors have implemented a prototype version in their laboratory using a commercially-available robot system.<<ETX>>

Estimating pose statistics for robotic part feeders

In automated assembly lines, part feeders often impose a bottleneck that restricts throughput. To facilitate the design of parts and assembly lines, the authors estimate feedrates based on CAD models of parts. A previous paper (Golberg and Craig, 1995) described how to predict throughput for a vision-based robotic part feeder given the distribution of part poses when parts are randomly dropped on a conveyor belt. Estimating this distribution is also useful for the design of traditional feeders such as vibratory bowls. In this paper the authors describe three algorithms for estimating pose distributions. The authors review the quasi-static estimate reported in Wiegley et al. (1992) and introduce a refinement that takes into account some measure of dynamic stability. The perturbed quasi-static estimate can be computed very rapidly and is more accurate than the quasi-static. Still more accurate are estimates based on Monte Carlo simulation using Impulse, although the latter comes at the penalty of increased computation time. The authors compare estimates from all three algorithms with physical experiments.

Part pose statistics: estimators and experiments

Many of the most fundamental examples in probability involve the pose statistics of coins and dice as they are dropped on a flat surface. For these parts, the probability assigned to each stable face is justified based on part symmetry, although most gamblers are familiar with the possibility of loaded dice. In industrial part feeding, parts also arrive in random orientations. We consider the following problem: given part geometry and parameters such as center of mass, estimate the probability of encountering each stable pose of the part. We describe three estimators for solving this problem for polyhedral parts with known center of mass. The first estimator uses a quasistatic motion model that is computed in time O(n log n) for a part with n vertices. The second estimator has the same time complexity but takes into account a measure of dynamic stability based on perturbation. The third estimator uses repeated Monte Carlo experiments with a mechanics simulation package. To evaluate these estimators, we used a robot and computer vision system to record the pose statistics based on 3595 physical drop experiments with four different parts. We compare this data to the results from each estimator. We believe this is the first paper to systematically compare alternative estimators and to correlate their performance with statistically significant experiments on industrial parts.

Estimating Throughput for a Flexible Part Feeder

To rapidly feed industrial parts on an assembly line, Carlisle et. al. [4] proposed a flexible part feeding system that drops parts on a flat conveyor belt, determines their pose (position and orientation) with a vision system and uses a high-speed scara robot to move them to a pallet in a desired pose. Such a feeder can be rapidly configured and reconfigured to handle a variety of parts. To facilitate rapid set-up of assembly lines, a simulator can provide visualization and realistic timing estimates. This paper focuses on estimating feeder throughput, which determines the “heartbeat” of an assembly line.

I'm trying to think, but nothing happens! [robotics]

Summary form only given as follows. Much of the recent research in robotics has focused on improving the speed and control of robots. Todays robots blindly follow orders that have been prepared in advance. In order to make further progress in the acceptance of robots on the factory floor and in new applications such as surgery or service, robots must be able to sense and reason about their environment. We need to devote research to reasoning about geometry and processes. We need better ways of acquiring models than laborious programming in CAD systems. We need methods to represent errors and plan recoveries. These capabilities will be necessary for the autonomous robots of tomorrow.