Abstract

This research presents a flexible and dynamic implementation of Speed and Separation Monitoring (SSM) safety measure that optimizes the productivity of a task while ensuring human safety during Human-Robot Collaboration (HRC).

Unlike the standard static/fixed demarcated 2D safety zones based on 2D scanning LiDARs, this research presents a dynamic sensor setup that changes the safety zones based on the robot pose and motion.

The focus of this research is the implementation of a dynamic SSM safety configuration using Time-of-Flight (ToF) laser-ranging sensor arrays placed around the centers of the links of a robot arm. It investigates the viability of on-robot exteroceptive sensors for implementing SSM as a safety measure. Here the implementation of varying dynamic SSM safety configurations based on approaches of measuring human-robot separation distance and relative speeds using the sensor modalities of ToF sensor arrays, a motion-capture system, and a 2D LiDAR is shown.

This study presents a comparative analysis of the dynamic SSM safety configurations in terms of safety, performance, and productivity. A system of systems (cyber-physical system) architecture for conducting and analyzing the HRC experiments was proposed and implemented. The robots, objects, and human operators sharing the workspace are represented virtually as part of the system by using a digital-twin setup. This system was capable of controlling the robot motion, monitoring human physiological response, and tracking the progress of the collaborative task.

This research conducted experiments with human subjects performing a task while sharing the robot workspace under the proposed dynamic SSM safety configurations. The experiment results showed a preference for the use of ToF sensors and motion capture rather than the 2D LiDAR currently used in the industry. The human subjects felt safe and comfortable using the proposed dynamic SSM safety configuration with ToF sensor arrays. The results for a standard pick and place task showed up to a 40% increase in productivity in comparison to a 2D LiDAR.

Publication Date

3-2020

Document Type

Dissertation

Student Type

Graduate

Degree Name

Engineering (Ph.D.)

Department, Program, or Center

Electrical Engineering (KGCOE)

Advisor

Ferat Sahin

Advisor/Committee Member

Sohail A. Dianat

Advisor/Committee Member

Esa Rantanen

SKumarSupplement.pdf (10430 kB)
Supplement

Campus

RIT – Main Campus

Share

COinS