Comparative performance of human and mobile robotic assistants in collaborative fetch-and-deliver tasks

There is an emerging desire across manufacturing industries to deploy robots that support people in their manual work, rather than replace human workers. This paper explores one such opportunity, which is to field a mobile robotic assistant that travels between part carts and the automotive final assembly line, delivering tools and materials to the human workers. We compare the performance of a mobile robotic assistant to that of a human assistant to gain a better understanding of the factors that impact its effectiveness. Statistically significant differences emerge based on type of assistant, human or robot. Interaction times and idle times are statistically significantly higher for the robotic assistant than the human assistant. We report additional differences in participant's subjective response regarding team fluency, situational awareness, comfort and safety. Finally, we discuss how results from the experiment inform the design of a more effective assistant.BMW Grou

Challenges in Developing a Collaborative Robotic Assistant for Automotive Assembly Lines

Industrial robots are on the verge of emerging from their cages, and entering the final assembly to work along side humans. Towards this we are developing a collaborative robot capable of assisting humans in the final automotive assembly. Several algorithmic as well as design challenges exist when the robots enter the unpredictable, human-centric and time-critical environment of final assembly. In this work, we briefly discuss a few of these challenges along with developed solutions and proposed methodologies, and their implications for improving human-robot collaboration

Analyzing the Effects of Human-Aware Motion Planning on Close-Proximity Human-Robot Collaboration

Objective: The objective of this work was to examine human response to motion-level robot adaptation to determine its effect on team fluency, human satisfaction, and perceived safety and comfort. Background: The evaluation of human response to adaptive robotic assistants has been limited, particularly in the realm of motion-level adaptation. The lack of true human-in-the-loop evaluation has made it impossible to determine whether such adaptation would lead to efficient and satisfying human–robot interaction. Method: We conducted an experiment in which participants worked with a robot to perform a collaborative task. Participants worked with an adaptive robot incorporating human-aware motion planning and with a baseline robot using shortest-path motions. Team fluency was evaluated through a set of quantitative metrics, and human satisfaction and perceived safety and comfort were evaluated through questionnaires. Results: When working with the adaptive robot, participants completed the task 5.57% faster, with 19.9% more concurrent motion, 2.96% less human idle time, 17.3% less robot idle time, and a 15.1% greater separation distance. Questionnaire responses indicated that participants felt safer and more comfortable when working with an adaptive robot and were more satisfied with it as a teammate than with the standard robot. Conclusion: People respond well to motion-level robot adaptation, and significant benefits can be achieved from its use in terms of both human–robot team fluency and human worker satisfaction. Application: Our conclusion supports the development of technologies that could be used to implement human-aware motion planning in collaborative robots and the use of this technique for close-proximity human–robot collaboration

Investigating the effects of signal light position on human workload and reaction time in human-robot collaboration tasks

Critical to a seamless working relationship in human-robot collaborative environments is effective and frequent communication. This study looked to assess whether placing a light source on a robot was more effective for informing the human operator of the status of the robot than conventional human-machine interfaces for industrial system signaling such as light towers. Participants completed an assembly task while monitoring a robot and changes to the light sources: either from one of two light towers or LED strip lights attached to the robot. Workload was assessed by measuring reaction times to light changes and by counting number of completed assemblies. Although both the ANOVA and Friedman tests returned none significant results, total misses per condition showed that the participants did not miss any of the robot lights, whereas signals were missed for the light towers

Human Robot Collaborative Assembly Planning: An Answer Set Programming Approach

For planning an assembly of a product from a given set of parts, robots necessitate certain cognitive skills: high-level planning is needed to decide the order of actuation actions, while geometric reasoning is needed to check the feasibility of these actions. For collaborative assembly tasks with humans, robots require further cognitive capabilities, such as commonsense reasoning, sensing, and communication skills, not only to cope with the uncertainty caused by incomplete knowledge about the humans' behaviors but also to ensure safer collaborations. We propose a novel method for collaborative assembly planning under uncertainty, that utilizes hybrid conditional planning extended with commonsense reasoning and a rich set of communication actions for collaborative tasks. Our method is based on answer set programming. We show the applicability of our approach in a real-world assembly domain, where a bi-manual Baxter robot collaborates with a human teammate to assemble furniture. This manuscript is under consideration for acceptance in TPLP.Comment: 36th International Conference on Logic Programming (ICLP 2020), University Of Calabria, Rende (CS), Italy, September 2020, 15 page

Development of a human factors roadmap for the successful implementation of industrial human-robot collaboration

The concept of industrial human-robot collaboration (HRC) is becoming increasingly integrated into manufacturing production lines as a means for enhancing productivity and product quality. However, developments have focused primarily on the technology and, until recently, little research has been geared to understand the key human factors (HF) that need to be considered to enable successful implementation of industrial HRC. Recent work by the authors has led to the identification of key organisational and individual level HF. The purpose of this paper is to draw together the evidence from their studies and propose a HF roadmap for the successful implementation of industrial HRC. The roadmap will have profound implications as it enables automation specialists and manufacturing system engineers to understand the key HF that need to be considered optimise the efficiency and productivity of the collaboration between humans and industrial robots

Putting people and robots together in manufacturing: are we ready?

Traditionally, industrial robots have been completely segregated from people in manufacturing systems to mitigate the dangers posed by their operational speeds and heavy payloads. Putting human operators together with large-scale industrial robots is now becoming increasingly possible with the development of integrated safety monitoring systems, and with smaller force-limited robotics that are now being produced with sufficient robustness for industry. However, with long-standing perceptions of robots as hazardous, we do not yet know how manufacturing workforces will accept collaborative systems with either large or small scale robotics and there is a need to identify and define new ethical and safety standard requirements for integrating people and robots to work collaboratively in industrial assembly tasks. To date there is little or no attention to ethical issues or psychological safety in the industrial safety standards that govern robotics and automated work systems. This paper describes the current situation and specific ways in which human-robot collaboration will significantly improve efficiency and flexibility, and outlines some early work that is being performed to identify the requirements that will be needed in order to facilitate this new way of bringing people and robots together in manufacturing. It presents a brief summary of initial findings that support the need for ethical issues to be considered as a candidate for new and / or revised safety standards

Application of speed and separation monitoring method in human-robot collaboration: industrial case study

Application of human-robot-collaboration techniques in automotive industries has many advantages on productivity, production quality, and workers’ ergonomy, however workers’ safety aspects play the key role during this collaboration. In this paper, results of the ongoing research about the development of a manufacturing cell for the automotive brake disc assembly that is based on the human-robot collaboration are presented. Operational speed and worker-robot separation monitoring methodology (SSM) as one of the available method to reduce the risk of injury according to the ISO technical specification 15066 on collaborative robot in sharing space with human, has been applied. Virtual environment simulation has been used, considering different percentages of robot maximum speed, to determine the SSM algorithm parameters for estimating the minimum protective distance between the robot and operator. Using ISO/TS 15066 and virtual environment simulation, the minimum separation distance between operator and robot has been estimated. Using human-robot collaboration along with the safety issues specified by SSM system has increased the safety of operation and reduced the operator fatigue during the assembly process

Designing an AR interface to improve trust in Human-Robots collaboration

In a global, e-commerce marketplace, product customisation is driven towards manufacturing flexibility. Conventional caged robots are designed for high volume and low mix production cannot always comply with the increasing low volume and high customisation requirements. In this scenario, the interest in collaborative robots is growing. A critical aspect of Human-Robot Collaboration (HRC) is human trust in robots. This research focuses on increasing the human confidence and trust in robots by designing an Augmented Reality (AR) interface for HRC. The variable affecting the trust involved in HRC have been estimated. These have been utilised for designing the AR-HRC. The proposed design aims to provide situational awareness and spatial dialog. The AR-HRC developed has been tested on 15 participants which have performed a “pick-and-place” task. The results show that the utilisation of AR in the proposed scenario positively affects the human trust in robot. The human-robot collaboration enhanced by AR are more natural and effective. The trust has been measured through an empirical psychometric method also presented in this paper