Joseph A. Falco

Characterizing Task-Based Human–Robot Collaboration Safety in Manufacturing

A new methodology for describing the safety of human-robot collaborations is presented. Taking a task-based perspective, a risk assessment of a collaborative robot system safety can be evaluated offline during the initial design stages. This risk assessment factors in such elements as tooling, the nature and duration of expected contacts, and any amortized transfer of pressures and forces onto a human operator. Risk assessments of example tasks are provided for illustrative purposes.

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.

Development of Standard Test Methods for Unmanned and Manned Industrial Vehicles Used Near Humans

The National Institute of Standards and Technology (NIST) has been researching human-robot-vehicle collaborative environments for automated guided vehicles (AGVs) and manned forklifts. Safety of AGVs and manned vehicles with automated functions (e.g., forklifts that slow/stop automatically in hazardous situations) are the focus of the American National Standards Institute/Industrial Truck Safety Development Foundation (ANSI/ITSDF) B56.5 safety standard. Recently, the NIST Mobile Autonomous Vehicle Obstacle Detection/Avoidance (MAVODA) Project began researching test methods to detect humans or other obstacles entering the vehicle’s path. This causes potential safety hazards in manufacturing facilities where both line-of-sight and non-line-of-sight conditions are prevalent. The test methods described in this paper address both of these conditions. These methods will provide the B56.5 committee with the measurement science basis for sensing systems - both non-contact and contact - that may be used in manufacturing facilities.

Multi-Robot Assembly Strategies and Metrics

We present a survey of multi-robot assembly applications and methods and describe trends and general insights into the multi-robot assembly problem for industrial applications. We focus on fixtureless assembly strategies featuring two or more robotic systems. Such robotic systems include industrial robot arms, dexterous robotic hands, and autonomous mobile platforms, such as automated guided vehicles. In this survey, we identify the types of assemblies that are enabled by utilizing multiple robots, the algorithms that synchronize the motions of the robots to complete the assembly operations, and the metrics used to assess the quality and performance of the assemblies.

Objective test and performance measurement of automotive crash warning systems

The National Institute of Standards and Technology (NIST), under an interagency agreement with the United States Department of Transportation (DOT), is supporting development of objective test and measurement procedures for vehicle-based warning systems intended to warn an inattentive driver of imminent rear-end, road-departure and lane-change crash scenarios. The work includes development of track and on-road test procedures, and development of an independent measurement system, which together provide data for evaluating warning system performance. This paper will provide an overview of DOTs Integrated Vehicle-Based Safety System (IVBSS) program along with a review of the approach for objectively testing and measuring warning system performance.

An Independent Measurement System for Testing Automotive Crash Warning Systems

This report describes the National Institute of Standards and Technologys (NIST) participation in Phase I of the Integrated Vehicle-Based Safety Systems (IVBSS) program, a safety research program sponsored by the U.S. Department of Transportation (U.S. DOT). The goal of this initiative is to determine potential safety benefits and user acceptance of integrated rear-end, lane-change/merge and road departure crash warning systems for light vehicles and heavy commercial trucks. NISTs primary roles in the program included assisting in the development of verification test procedures, the design, construction, and characterization of an independent measurement system, and providing field support for vehicle test activities. The verification tests provide an objective means to evaluate warning system performance in a safe and controlled test-track environment.

Robotic Grasping and Manipulation Competition: Task Pool

A Robot Grasping and Manipulation Competition was held during IROS 2016. The competition provided a common set of robot tasks for researchers focused on the application of robot systems to compare the performance of hand designs as well as autonomous grasping and manipulation solutions. Tracks one and two of the competition were supported by tasks chosen from a predefined pool of tasks. This task pool was assembled by the authors based on the challenges faced in developing robot systems that have the flexibility to grasp and manipulate a wide range of object geometries. This paper provides an overview of the task pool as well as the selection of tasks to support the various stages of the competition.

Robotic Grasping and Manipulation Competition: Competitor Feedback and Lessons Learned

The First Robot Grasping and Manipulation Competition, held during IROS 2016, allowed researchers focused on the application of robot systems to compare the performance of hand designs as well as autonomous grasping and manipulation solutions across a common set of tasks. The competition was comprised of three tracks that included hand-in-hand grasping, fully autonomous grasping, and simulation. The hand-in-hand and fully autonomous tracks used 18 predefined manipulation tasks and 20 objects. Additionally, a bin picking operation was also performed within the hand-in-hand and fully autonomous tracks using a shopping basket and a subset of the objects. The simulation track included two parts. The first was a pick and place operation, where a simulated hand extracted as many objects as possible from a cluttered shelf and placed them randomly in a bin. The second part was a bin picking operation where a simulated robotic hand lifted as many balls as possible from a bin and deposited them into a second bin. This paper presents competitor feedback as well as an analysis of lessons learned towards improvements and advancements for the next competition at IROS 2017.

Measurement science for 6DOF object pose ground truth

Users of perception systems in industrial manufacturing applications need standardized, third party ground truth procedures to validate system performance before deployment. Many manufacturing robotic applications require parts and assemblies to be perceived, inspected or grasped. These applications need accurate perception of object pose to six degrees of freedom (6DOF) in X, Y, Z position with roll, pitch and yaw. A standardized 6DOF ground truth system should include test procedures, algorithms, artifacts, fixtures, and measurement equipment. Each of them must be openly documented so manufacturers, vendors, and researchers can recreate and apply the procedures. This article reports on efforts to develop an industrial standard for 6DOF pose measurement. It includes the design of test methods using a laser-tracker, an aluminum fixture pose fixture, and a modular, medium density fiberboard (MDF) pose fixture.