Cobotics Overview

Komal Kambale System Science and Industrial Engineering Department

State University of New York at Binghamton

Outline Introduction Why we need cobots? Cobotic systems

Characteristics Constraints Applications

Cobots Vs. robots Case study Review of leading cobots Ongoing research at peer universities Viable research options @BU Conclusion

Introduction

An apparatus and method for direct physical interaction between a person and a general purpose manipulator controlled by a computer

Cobot= collaborative + robot

Pioneers & Inventors

James E. Colgate

Michael A. Peshkin

Rodney Brooks

 Esben Østergaard

Need

Mass production will require both men and machine Injuries caused to operators due to repetitive tasks Safety risks for operators working in closed contact with

robots

Taxonomy of assist devices

Shortfalls of Existing Solutions

Provides high precision and speed, but only on a large scale Safety as design feature is rarely available Lack of tactile sense solutions are often too standardized and not generic End users struggle to adapt with cutting edge technology Lack of trust from end users

Objectives of Cobot

Collaborate with humans without the dangers and limitations of other assisted devices

Cope up with different situations and tasks

Direct the motion of human operator or payload without the use of powerful actuators ABB Cobot FRIDA demo [3]

Components of Cobotic System

Cobot Coworker Environment of Workstation

Cobotic System Characteristic

Safe, by design Trained, not programmed Adaptive Flexible and re-deployable Easily integrated Affordable Extensible

Cobotic System Constraints

Slower Less powerful Payload High precision

Applications

CNC machining Quality inspection PCB handling Gluing and welding Machine tending Pick and place

Understands people and environment Safer compared to robots Flexible and easy to use Tasks are performed similar to human

way Can be trained by demonstration No/minimum integration required Affordable

Unaware of surroundings Potential danger to human safety High precision and repeatability Definite operations for task

completion are required Need expert programmers Integration is costly Expensive

Cobot Vs.

Robot

Case Study Human-robot collaboration in manufacturing: quantitative

evaluation of predictable, convergent joint action Problem statement: computing entropy rate in Markov chain to

assess the convergence of robot computational teaming model and coworkers mental model

Experiment setting: simple pick and place task Coworker: place screws in one of the three available

positions Cobot: drill each screw

Case Study Continued

Learning method Cross training Reinforcement leaning

Post hoc subjects which change in strategy or inconsistent execution

Case Study continued

Entropy rate is sensitive to coworkers strategy Using probabilistic distributions of coworkers next actions to

compute his presence at various locations in robots vicinity When coworker deviates from high probabilistic workspace

change motion parameters

ABB- Yumi

Initially built with keeping electronics industry in mind Application industry

Small part assembly Consumer products Toy industry Watch industry

Not suitable for Paint industry Food grade Clean room applications

ABB- YuMi Continued

Technical specifications Payload: 0.5 kg per arm Reach: 559 mm Accuracy: 0.02 mm Weight: 38 kg

Cost : $40,000 approx

Universal Robots-UR3 Primarily developed for small

format assembly tasks Application industry

Automotive Food and agriculture Furniture and equipment Electronics technology Metal machining Plastic and polymer Picking and placing cream bottles

in Johnson and Johnson Greece factory

Universal Robots-UR3 Continued

Technical specifications Payload: 3 kg Reach: 500 mm Repeatability: ±0.1 mm Weight: 11 kg

Cost : $23,000 approx

Baxter and Research Continued

Learning trajectory preferences for manipulators via iterative improvement

Explore computer vision, machine learning, and artificial intelligence by training Baxters to pour water into a moving jar, learn to cook by watching youTube, and work with other robots

Baxter and Research Continued

Assistive robots for blind travelers Accessible interfaces Assistive interaction Effective cooperation

Baxter and Research Continued

Closed-loop global motion planning for reactive execution of learned tasks

Baxter and Research Continued

Improving Soft pneumatic actuator fingers through integration of soft sensors, position and force control, and rigid fingernails

Baxter and Research Continued

Program Baxter in such a way that the robot can perform in a magic show on stage alongside Tempest What is the affect space for a human-

magician performance? How to improve on current robot

animation techniques? What are challenges and opportunities

when designing human-robot performances?

Viable Research Options @BU

Cobot + quality assurance Statitical analysis of particular

manufacturing task performed by Baxter

Adaptive learning Exploring cobot response to various

training techniques

Safety Determine weather or not a certain action

is safe on a human

Conclusion

Current research level at peer universities involves vision control study and human-robot interaction

Cobots provide opportunity to automate tasks that were previously automated

Baxter research robot provides unique opportunity to various universities for R & D