Mobility Performance of Robotic Systems

Summary

Mobile Robotic Systems, which include mobile bases (wheeled or legged) and onboard systems (manipulators, sensors, and end effectors such as tools, grippers, and lasers) and wearable robots (active, passive, or hybrid), are expanding their capabilities well beyond the traditional roles as they attain greater onboard intelligence and control. To improve industrial processes, companies are requiring robots to become more adaptable, faster, and highly accurate in dynamic environments. One example is the ability to move a manipulator arm to different workstations as needed without needing outside assistance to register the robot to the workstation, and to be able to coordinate its mobile base motions with its arm to perform continuous operations. The project will be integrating advanced closed-loop control and measurement methods using a selection of onboard sensors combined with model-based predictive control and AI algorithms to improve localization measurement and evaluation methods. The project will also be developing metrics, testbed infrastructure, and measurement systems to validate performance test methods for mobile bases.

Wearable robots (a.k.a. exoskeletons or exosuits) are becoming more advanced with closed-loop sensory active control. Technology is advancing to enable increasingly adaptable and more accurate control, to prevent worker fatigue and improve longevity. Wearable robots may also enable disabled workers to perform additional job functions. Some manufacturing companies are even requiring wearable robots as personal protective equipment. The project will develop and integrate metrics, sensors, testbed infrastructure, test methods, and algorithms to evaluate how active, passive, and hybrid wearable robots impact the users’ mobility, metabolic responses, kinematics, and kinetics. The knowledge learned will inform standards development efforts within ASTM International Committee F48 on Exoskeletons and Exosuits.

The commonality among the mobile robotic systems is the metrology needed to accurately and comprehensively characterize devices that perform complex movements, such as the robot's interactions with large workpieces (e.g., airplane wings and fuselage) in unstructured, dynamically changing spaces. It is important to clearly communicate performance and safety capabilities to stakeholders (users, manufacturers, and standards organizations).

Description

Objective
Provide the measurement science to develop standard test methods for intelligent industrial mobility systems, including mobile and wearable robotic systems.

Technical Idea
This project will expand prior work on performance test methods for mobile robotic systems and wearable robots under a unified framework, which leverages common reference measurements and evaluation techniques.

Mobile Robotic Systems. Building on current research and development on mobile robotic system localization and closed-loop control, the project’s next phase is to research and develop improved localization methods using a selection of onboard sensors combined with model-based predictive control and Machine Learning/Artificial Intelligence (ML/AI) algorithms. Some of the common issues encountered in prior localization tests conducted in previous research that can significantly degrade robot localization performance include slippage, uneven flooring, dynamic obstacles, vibrations, heavy loads, and forcible delocalization (after the robot builds its map, the operator changes the physical orientation and or location of the robot). Considering significant advances in mobile robotic system intelligence and autonomy, the technical idea is to: 1) measure performance of mobile robotic system control and onboard sensing using manual mapping (joystick navigation to map prior to autonomous control); 2) measure performance of mobile robotic system control and onboard sensing using learning (exploring the environment autonomously to develop a map); and 3) integrate external optical tracking with mobile robotic systems to evaluate localization in dynamic, unstructured environments (e.g., discrete and continuous workpiece movement and obstacle avoidance).

Wearable Robots. Building on prior research methodology for designing metrics and test methods for binary (on/off) passive wearable robots, the project will advance into metrics and test methods for active and hybrid systems (i.e., systems that change the amount of assistance provided as needed by the end user). A more responsive wearable robot with closed-loop sensory control would enable continual assistance and situational awareness (e.g., lift position, predictive muscle activity, and endurance) when needed by the user. Wearable robot manufacturers are already marketing these advanced systems (hybrid and active) yet test methods are lacking for these new systems. Evaluation of adaptive wearable robots requires continuous, synchronous measurements of both the humans and the wearable robots including, but not limited to, joint angles, velocities, accelerations, and torques. The project will utilize both advanced optical tracking capabilities and low-cost hardware augmented with ML/AI to measure human-wearable robot performance.  Optical tracking methods will be used as ground truth to evaluate the uncertainty of low cost, fieldable measurements.

Research Plan
New measurement methods are needed to evaluate the performance of dynamic localization of mobile robotic systems and wearable robots with continuous, adaptive control and situational awareness. The research plan will develop new metrics, test methods, artifacts, and datasets that measure the performance characteristics for these devices. Position estimation and tracking the kinematics of mobile manipulators, mobile robots, and wearable robots are essential to quantifying these robots’ capabilities. Utilizing a well-established and highly successful partnership with ASTM International committees F45 on on Robotics, Automation, and Autonomous Systems and F48 on Exoskeletons and Exosuits, the project will continue producing draft testing methodologies, test artifact designs, and experimental results for further development and balloting. The project will develop concepts for characterizing the levels of intelligence and autonomy-based capabilities for mobile robotic systems as guidance for the research and user community and as input to standards roadmaps.

Over the next five years, the project will create and validate new test methods to characterize the performance of passive, active, and hybrid wearable robots. The wearable robot test method development will include evaluation methods for tool control, mobility, and force applications in typical manufacturing-inspired tasks performed in industrial environments.

In addition to test method development, a framework will be developed for the ergonomic and industrial impacts of active and hybrid wearable robots in industrial environments. Lastly, the project will address gaps in current and developing safety standards for these systems.

There is a critical need for mobile robotic systems to operate in unstructured and dynamic environments. The project will create novel test methods that leverage vision-based sensing to evaluate mobile robotic systems performance within these environments. The project will work to systematically understand the uncertainty and improve the measurement repeatability and accuracy of optical tracking systems (OTS). An OTS will serve as the ground truth to evaluate integrated, on-board sensing capabilities such as cameras and inertial measurement units. The project will define performance characteristics and test methods for mobile robotic systems interacting with workpieces and the environment, such as discrete and continuous docking, manipulation, and assembly operations.

Finally, the project will assess the localization ability of loaded and unloaded mobile robotic systems in both structured and unstructured environments. The integration of sensory interactive control for mobile robotic systems will improve situational awareness and onboard decision-making capabilities for adaptive localization. The project will develop a methodology for evaluating onboard sensing capabilities and enhanced algorithms relative to ground truth systems. By providing guidance for safe operation of mobile robotic systems the project will address conflicts and gaps in current and future mobile robot performance standards.

Major Accomplishments

• Completed implementation of a closed-loop mobile manipulator control system for testing performance with workpiece disturbances.
• Completed implementation of performance test methods for small obstacle avoidance, weighted driving and docking, driving and docking on ramps, and load stability of A-UGVs.
• Helped establish ASTM International Subcommittee F45.05 on Grasping and Manipulation

Published Standards

• ASTM F48
  • F3581 Standard Test Method for Exoskeleton Use: Hurdles
  • F3582 Standard Test Method for Exoskeleton Use: Gaps
  • F3583 Standard Test Method for Exoskeleton Use: Beams
  • F3584 Standard Test Method for Exoskeleton Use: Obstacle Avoidance: Walking
  • F3613 Standard Practice for Recording the Exoskeleton Fit to the User
  • F3614 Standard Practice for Recording the Exoskeleton User Information
• ASTM F45  
  • F3200 Standard Terminology for Robotics, Automation, and Autonomous Systems
  • F3218 Standard Practice for Documenting Environmental Conditions for Utilization with A-UGV Test Methods
  • F3243 Standard Practice for Implementing Communications Impairments on A-UGV Systems
  • F3244 Standard Test Method for Navigation: Defined Area
  • F3327 Standard Practice for Recording the A-UGV Test Configuration
  • F3499 Standard Test Method for Confirming the Docking Performance of A-UGVs
  • F3588 Standard Guide for Set of Objects used with A-UGVs

Datasets

• Continuous mobile manipulator performance experiment 02-01-2022. https://doi.org/10.18434/mds2-3061
• Exoskeleton Performance Data (2021) https://doi.org/10.18434/mds2-2429