The application of continuum manipulators as assistive robots is discussed and tested through the use of Bendy ARM, a tendon-driven continuum manipulator prototype. Two rounds of user testing were completed to evaluate the potential of this robot to aid disabled individuals in the completion of activities of daily living. In the first round of user testing, 14 able-bodied subjects successfully completed the prescribed task (pick-and-place) using multiple control schemes after being given a brief introduction and one minute of practice with each scheme. In the second round of user testing, subjects chosen from the first round of testing (n = 3) demonstrated between 29.45 and 48.91 percent improvement in completion time across three sessions (twelve trials total) of a peg-in-hole task, and between 8.39 and 33.81 percent improvement across two sessions (six trials total) of a task involving opening and closing a drawer. Based on these results, it is posited that continuum manipulators merit further consideration as a safer and more cost-effective alternative to existing commercially available assistive robotic manipulators.
A manufacturing paradigm shift from conventional control pyramids to decentralized, service-oriented, and cyber-physical systems (CPSs) is taking place in today’s 4th industrial revolution. Generally accepted roles and implementation recipes of cyber systems are expected to be standardized in the future of manufacturing industry. The authors intend to develop a novel CPS-enabled control architecture that accommodates: (1) intelligent information systems involving domain knowledge, empirical model, and simulation; (2) fast and secured industrial communication networks; (3) cognitive automation by rapid signal analytics and machine learning (ML) based feature extraction; (4) interoperability between machine and human. Semantic integration of process indicators is fundamental to the success of such implementation. This work proposes an automated semantic integration of data-intensive process signals that is deployable to industrial signal-based control loops. The proposed system rapidly infers manufacturing events from image-based data feeds, and hence triggers process control signals. Two image inference approaches are implemented: cloud-based ML model query and edge-end object shape detection. Depending on use cases and task requirements, these two approaches can be designated with different event detection tasks to provide a comprehensive system self-awareness. Coupled with conventional industrial sensor signals, machine vision system can rapidly understand manufacturing scenes, and feed extracted semantic information to a manufacturing ontology developed by either expert or ML-enabled cyber systems. Moreover, extracted signals are interpreted by Programmable Logical Controllers (PLCs) and field devices for cognitive automation towards fully autonomous industrial systems.
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