ASSIGNING AUTOMATRONS MANUAL

Kim acknowledges that some will question why robots should be treated respectfully in the first place. 'Artificial intelligence (AI) imitates human intelligence, so for robots to develop as rites bearers, they must be powered by a type of AI that can imitate humans' capacity to recognize and execute team activities - and a machine can learn that ability in various ways.' 'Assigning role obligations to robots encourages teamwork, which triggers an understanding that fulfilling those obligations should be done harmoniously,' explains Kim. The concept of rights is often adversarial and competitive, and potential conflict between humans and robots is concerning. When considering robots, Kim suggests that the Confucian alternative of assigning rites - or what he calls role obligations - to robots is more appropriate than giving robots rights. This, in turn, requires a unique perspective on rites, with people enhancing themselves morally by participating in proper rituals. Confucianism, an ancient Chinese belief system, focuses on the social value of achieving harmony individuals are made distinctively human by their ability to conceive of interests not purely in terms of personal self-interest, but in terms that include a relational and a communal self. 'Granting rights is not the only way to address the moral status of robots: Envisioning robots as rites bearers - not a rights bearers - could work better.'Īlthough many believe that respecting robots should lead to granting them rights, Kim argues for a different approach. 'People are worried about the risks of granting rights to robots,' notes Tae Wan Kim, Associate Professor of Business Ethics at CMU's Tepper School of Business, who conducted the analysis. Experimental results demonstrate the effectiveness of these methods in stochastic warehouse systems.The analysis, by a researcher at Carnegie Mellon University (CMU), appears in Communications of the ACM, published by the Association for Computing Machinery. The third topic is a deep reinforcement learning (DRL) model that is developed to solve task assignment and path planning problems, simultaneously. The second topic, Risk Interval Path Planning (RIPP), is an algorithm designed to avoid conflicts among AMRs considering uncertainties in robot motion. The first topic in this dissertation is the development of a discrete event simulation modeling framework that can be used to evaluate alternative traffic control rules, task assignment methods, and path planning algorithms. The goals are to maximize system productivity by avoiding AMR traffic and reducing travel time.

This dissertation addresses the task assignment and path planning (TAPP) problem for autonomous mobile robots (AMR) in a warehouse environment. Two open questions in AMR management are task assignment and path planning. Autonomous mobile robots (AMRs) are being widely implemented to replace manually operated forklifts in warehouse systems to fulfil large shipping demand, extend warehouse operating hours, and mitigate safety concerns.

ASSIGNING AUTOMATRONS MANUAL

The material handling industry is in the middle of a transformation from manual operations to automation due to the rapid growth in e-commerce.

Assigning automatrons