Mobility Performance of Robotic Systems

Summary

Mobile Robotic Systems, which include mobile bases (wheeled or legged) and onboard systems (manipulators, sensors, end effectors (tools, grippers, laser, etc.)) and wearable robots (active, passive and 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, agile and highly accurate in dynamic environments. One example is the ability to move the manipulator arm to different workstations as needed without needing outside assistance to relocalize the robot to itself and to be able to coordinate its base 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 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 enable disabled workers to perform additional job functions. Some manufacturing companies are even requiring wearable robots as personal protective equipment. The project will be developing and integrating metrics, sensors, testbed infrastructure, test methods, and algorithms to evaluate how the exoskeletons impact the users’ mobility, metabolic responses, kinematics, and kinetics to validate performance test methods for active, passive and hybrid wearable robots. The knowledge learned will inform ASTM F48 standards development.

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 (airplane wings, fuselage, )  in unstructured, dynamically changing spaces. It is important to clearly communicate performance and safety capabilities to stakeholders (users, manufacturers, and standards organizations). 

Description

Provide the measurement science to develop standard test methods for intelligent industrial mobility systems, including mobile robots, mobile manipulators, and exoskeletons.

New 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 systems intelligence and autonomy, the project approach will: 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, obstacle avoidance). 

Wearable Robots

Building on prior research methodology for designing metrics and test methods for binary (on/off) passive wearable robots will be advanced into metrics and test methods for active and hybrid systems – i.e., the current industry request for systems that will change the amount of assistance needed as required by the end user (continuously changing force/pressure wearable robot control). 

A more responsive wearable robot with closed loop sensory control would enable continual assistance and situational awareness (e.g., lift position, predictive muscle activity, endurance) when needed by the user. Wearable robot manufacturers are already marketing these advanced systems (hybrid, active) yet test methods are lacking for these new systems.  Evaluation of adaptive wearable robots requires continuous, synchronous measurements of both human and 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 compute measurements of human-wearable robot performance. The optical tracking will be used as ground truth to evaluate the uncertainty of the 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 a robot’s capabilities.    Utilizing a well established and highly successful partnership with ASTM F45/F48 we 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, we will create and validate new test methods to characterize the performance of hybrid, passive and active 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, we 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. We will create novel test methods that leverage vision-based sensing to evaluate mobile robotic systems performance within these environments. We will work to systematically understand the uncertainty and improve the measurement repeatability and accuracy of the optical tracking system (OTS). The OTS will serve as the ground truth to evaluate integrated, on-board sensing capabilities such as cameras and inertial measurement units. We 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. 

We will assess the localization ability of loaded and unloaded mobile robotics 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. We 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 we will address conflicts and gaps in current and future mobile robot performance standards.