This paper describes a concept for measuring the reproducible performance of mobile manipulators to be used for assembly or other similar tasks. An automatic guided vehicle with an onboard robot arm was programmed to repeatedly move to and stop at a novel, reconfigurable mobile manipulator artifact (RMMA), sense the RMMA, and detect targets on the RMMA. The manipulator moved a laser retroreflective sensor to detect small reflectors that can be reconfigured to measure various manipulator positions and orientations (poses). This paper describes calibration of a multi-camera, motion capture system using a 6 degree-of-freedom metrology bar and then using the camera system as a ground truth measurement device for validation of the reproducible mobile manipulator’s experiments and test method. Static performance measurement of a mobile manipulator using the RMMA has proved useful for relatively high tolerance pose estimation and other metrics that support standard test method development for indexed and dynamic mobile manipulator applications.
Future smart manufacturing systems will include more complex coordination of mobile manipulators (i.e., robot arms
mounted on mobile bases). The National Institute of Standards and Technology (NIST) conducts research on the safety
and performance of multiple collaborating robots using a mobile platform, an automatic guided vehicle (AGV) with an
onboard manipulator. Safety standards for robots and industrial vehicles each mandate their failsafe control, but there is
little overlap between the standards that can be relied on when the two systems are combined and their independent
controllers make collaborative decisions for safe movement. This paper briefly discusses previously uncovered gaps
between AGV and manipulator standards and details decision sharing for when manipulators and AGVs are combined
into a collaborative, mobile manipulator system. Tests using the NIST mobile manipulator with various control methods
were performed and are described along with test results and plans for further, more complex tests of implicit and
explicit coordination control of the mobile manipulator.
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
Low-cost 3D depth and range sensors are steadily becoming more widely available and affordable, and thus popular for
robotics enthusiasts. As basic research tools, however, their accuracy and performance are relatively unknown. In this
paper, we describe a framework for performance evaluation and measurement error analysis for 6 degrees of freedom
pose estimation systems using traceable ground truth instruments. Characterizing sensor drift and variance, and
quantifying range, spatial and angular accuracy, our framework focuses on artifact surface fitting and static pose analysis,
reporting testing and environmental conditions in compliance with the upcoming ASTM E57.02 standard.