Safe robot joint brake based on an elastic latch module

As collaborative robots begin to appear on factory floors, there is a need to consider how these robots can best help their human partners. In this paper, we propose an optimization framework that generates task assignments and schedules for a human-robot team with the goal of improving both time and ergonomics and demonstrate its use in six real-world manufacturing processes that are currently performed manually. Using the strain index method to quantify human physical stress, we create a set of solutions with assigned priorities on each goal. The resulting schedules provide engineers with insight into selecting the appropriate level of compromise and integrating the robot in a way that best fits the needs of an individual process.

Recent experiences have shown that one of the main causes of heavy vehicle crashes is the braking performance. In particular, when having to decelerate in an emergency situation, such as when an unexpected object is in the road. Thus, the capability of a vehicle to come to a safe stop is one of the most important factors in preventing more accidents. Safe braking distance is influenced by many factors, such as brake pedal force, the vehicle’s loading conditions, the travel speed and the road surface conditions. The aim of this study was to analyse the effect of the driver’s brake pedal force on braking distance during an emergency situation, allowed for a wide range of heavy vehicle’s GVW and speed. This study is carried out by using a multi-body dynamics simulation of a Single Unit Truck based on the validated vehicle model. Braking performance was estimated in terms of braking distance on a straight road with a wet surface. The findings from the braking distance simulation suggest a non-linear relationship between brake pedal force and braking distance. Finally, it reveals that, depending on the wheel lock-up situation, braking distance increases with increasing brake pedal force, and that the braking distance on a wet road is significantly affected by both the heavy vehicle’s GVW and speed.

Studies show that collaborative robots that can explain in real time what a human is doing and the passive underlying intention are likely to be positively perceived by human workers. In this respect, a seamless flow of information between human and machine systems in realtime is expected. This will push future collaborative robots‘ ability to work with humans at higher collaboration levels, including real cooperation or responsive collaboration with humans. This is crucial for collaborative robot applications to be used not in a similar way traditional industrial robots are used, i.e., rather than replacing humans engaging in real collaboration with them. Therefore, this work presents a bottom-up method for collaborative robot workplace design that uses models of individual human attention, intention, and action for cycle time synchronization to improve the performance of action prediction in planning the collaborative robot workplace design process. Contents Abbreviations and Acronyms VIII Symbols X Abstract XI Zusammenfassung XII 1 Introduction 1 1.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Human individual collaborative ...

Trans-sectoral digital research of man, nature, society and industrial communication allows to create digital twins of social services. Digital duplicates associated with the service sector are created for intelligent process management. Digital twins of man provide services in the social sphere and in space. Training of digital twins in professional competences is carried out on the basis of communicative associative logic of technological thinking by cognitive methods. The study of the effectiveness of machine learning techniques, allowed the use of an approach that allows the combination of artificial intelligence and cognitive psychology. This approach provided pre-preparation of neural networks from accumulated data using existing behaviors. The approach combines existing scientific theories of human behavior with the flexibility of neural networks to make better decisions made by humans in space and in extreme situations. From a practical point of view, this makes it possible to more accurately determine the behavior of the human digital twin in space and in extreme situations. There are a number of socio-economic issues related to human-machine interaction. Complex technologies are not credible on the part of citizens. The coming years will take to improve safety and standardize the creation, application of digital twins and behavior of robots.

Durability prediction of coil spring through multibody-dynamics-based strain generation

The fact that the topic of emergency situations has been neglected almost completely by ethical theory raises the question why normative ethics has had so little to say about extreme situations so far. One can assume that this disinterest is not due to the rarity of emergency situations but rather to their peculiar properties. All ethical theories rest on the premise that moral agents make the most of their decisions under normal circumstances. The aim of the paper is to answer the question whether or not normative ethics is able to adequately evaluate emergency situations. In order to do this, different types of extreme situations must be distinguished. It is argued that, on the one hand, self-defence and agreements by which all the participants refrain from certain of their rights in order to enable some of them to survive are morally unproblematic. On the other hand, there are emergency situations that do not allow for a solution which would be morally acceptable to all of the involved persons. Hence, morality itself can be unacceptable under extreme circumstances.

The use of collaborative robots (cobots) and human robot collaboration has started to increase in diverse industrial areas in the past years. However, cobots are seldom applied in construction work even if there is a growing need for robotics solutions in this area. This study aims to present the possibilities of applying lightweight cobots for single tasks in the construction sector. This paper presents the development of a proof-of-concept dedicated to screwing gypsum board panels to the ceiling of a room. After identifying the challenges related to this task, we present the cobot solution that we implemented for this purpose. Our results show that repetitive screwing work in upright position achieved by cobot can benefit workers by removing ergonomical strain. At the same time, cobot ensures that this task is achieved with constant performance. Future research directions lead to the search for better accuracy in screw positioning and more dynamic ways of collaboration with human workers.

Collaborative robots for industrial tasks: A review

The steep rise of interest in collaborative robots applied to industrial tasks is fallout from the emergence of Industry 4.0 in most industrialized countries. Workcells with both human workers and robots employed together extend the field of use of robotics to medium and small series productions. In a collaborative workplace, robots and humans can exploit their mutual skills and strengths to the best effect. With the goal of supporting collaborative robotics, this chapter points out several issues that may arise by introducing robots in manual production. This chapter’s statement is that the success of collaborative workcells in manufacturing relies on dedicated and accurate design activities and should not be approached by the mere addition of a robot to the existing workplace.

The notion of work goes through major changes caused by the development of technology, and it is assumed that the development of sophisticated robotization and artificial intelligence will undermine the existence of work. Artificial and robotic intelligence will create more jobs, not mass unemployment, as long as innovation is guided responsibly. Cobots, or collaborative robots, are typically intended for physical interaction with people in a common workplace. There is no doubt that the world of collaborative robots is on the rise, so labour law will have to distinguish between non-human workers (dwarfs, industrial robots, etc.) and human workers. Regulations in the field will evolve, meaning that provisions will be needed which will determine, at a minimum, what the relationship between the two categories of workers will be according to the specificity of the activity as well as other aspects. Romania still has a low density of 15 robots per 10,000 employees, with a national interest in the topic, which is a result of the adoption in 2015 of the National Strategy on the Digital Agenda Romania 2020. Replacing human labour with robots is no longer just a discussion, it is a reality; it is not just a sci-fi issue, it is something society should contemplate and anticipate by updating legislation and social protection.

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.

[EMBARGOED UNTIL 8/1/2024] Order picking, which involves retrieving items from storage locations for an internal or external customer, is a core function in warehouses and accounts for up to 65 percent of the total operating cost. It is considered as a crucial driver for supply chain performance as improper planning of picking operations leads to inefficient asset utilization and delayed deliveries, which, in turn, adversely affects customer satisfaction, operating cost, and competitiveness. A majority of warehouses adopt a picker-to-parts system (workers travel through a warehouse to retrieve and transport items from storage to packing station), and the fulfillment speed of such order picking system (OPS) depends on the following key decisions - (i) set of orders to be picked in a tour (order batching decision), (ii) assignment of a batch to a picker and order in which their a processed (batch assignment and sequencing decisions), and (iii) route followed x by the picker to collect the orders in each batch (picker routing decision). However, with the growing customer demand and global labor shortage, warehouses are seeking efficient and less labor-intensive order picking systems. Autonomous mobile robots (AMRs) or collaborative robots (cobots) have the potential to alleviate the strain on human workers and expedite order picking operations. However, there are several key operational challenges to address for ensuring an effective collaborative human-robot order-picking system (CHR-OPS), where humans perform item retrieval tasks, and AMRs handle item transportation to the depot. This research aims to improve the fulfillment efficiency of a picker-to-parts CHROPS by optimizing key decisions associated with two warehouse picking strategies, namely, static picking and dynamic picking. In the case of static picking, the items to be retrieved from storage for a given day are known as apriority. On the other hand, the dynamic picking strategy allows for orders to arrive over time (e.g., e-commerce warehouses), and the pick cycle (or picking plan) can be updated in real time. First, we address the problem of optimizing the following key subproblems of a CHR-OPS with a static picking strategy: (i) order batching (how many items should be collected in each AMR tour?), (ii) batch assignment and sequencing (how to assign batches to AMRs, and in what order should they be processed?), and (iii) picker-robot routing (how should the AMR and picker be routed to coordinate the picking process?). Existing literature has not dealt with the three subproblems, and this work is the first to address them for a picker-to-parts CHR-OPS system employing multiple pickers and AMRs. A mixed integer linear programming model is developed to jointly optimize the three subproblems with the objective of minimizing the total tardiness of all orders. The MILP model is validated and solved to optimality for small instances. However, since the problem under consideration is NP-hard, it is computationally intractable for larger instances. To efficiently handle large instances, we proposed deterministic and stochastic local search algorithms, namely variable neighborhood descent and a new variant of simulated annealing. The numerical experiments demonstrate the superior performance of the proposed solution approach compared to existing methods. Besides, our results also show that the picking efficiency is impacted by human{robot team composition, AMR speed, AMR capacity and warehouse layout. Subsequently, we address the CHR-OPS with dynamic picking and developing interventionist picking algorithms for a multi-robot multi-human setting. Specifically, we propose two interventionist policies for dynamic collaborative order picking, namely, the collaborative human-robot interventionist picking algorithm (CHRIPA) and the collaborative human-robot rule-based interventionist picking algorithm (CHR-RIPA). The evaluation of the policies demonstrates that the proposed rules can improve the overall performance of the system compared to benchmark approaches (human-only dynamic picking and collaborative picking with no intervention). In addition, results indicate CHR-IPA outperforms CHR-RIPA in terms of average tar diness and order completion time, albeit with a slightly higher travel distance for human workers. The results have led to several managerial implications for a collaborative order picking system. Further, the proposed models and algorithms are modular and can be adapted to any warehouse setting by accounting for the relevant parameters such as warehouse layout, AMR capacity and human-robot composition. Finally, the directions for future research have been identified and summarized.

Collaborative robotics in wire harnesses spot taping process

Human-robot collaboration (HRC), as a part of Industry 4.0 strategy, requires a completely new type of robots able to co-work with humans, called collaborative robots or cobots. This kind of collaboration is especially needed in assembly systems, which are known for having a low level of automation. For some assembly tasks human is still an irreplaceable factor. On the other hand, some assembly tasks are monotonous and tiring for humans. Therefore, the different approaches to cope with the challenge of identification and selection of proper task allocation between human worker and cobots are reported by many researchers. It is not an easy task since multiple and often conflicting criteria need to be taken into account. Some kind of artificial decision support is needed, to successfully solve this problem. In this research, task allocation procedure is presented for identification of different improvement options that utilize cobots into the assembly line for different tasks to be performed. The decision support system based on the HUMANT algorithm has been used for selection of the option which represents the best compromise solution. The procedure is experimentally tested on the assembly line with car gearboxes as a real product.

Nowadays, many manufacturing companies are trying to improve the performance of their processes using available innovative technologies such as collaborative robots (cobots). Cobots are robots with whom no safety distance is necessary. Through cooperation with human workers, they can help increase the production speed of existing workstations. The well-known job shop scheduling problem is, therefore, extended with the addition of a cobot to the workstation assignment. The considered objective is to maximize the normalized sum of production costs and makespan. To solve this problem, we propose a hybrid genetic algorithm with a biased random-key encoding and a variable neighborhood search. The hybrid method combines the exploration aspects of a genetic algorithm with the exploitation abilities of a variable neighborhood search. The developed algorithm is applied to real-world data and artificially generated data. To demonstrate the performance of this algorithm, a constraint programming model is implemented and the results are compared. Additionally, benchmark instances from a related problem from the cobot assignment and assembly line balancing, have been solved. The results from the real-world data show how much the objective function can be improved by the deployment of additional robots. The normalized objective function could be improved by up to 54% when using five additional cobots. As a methodological contribution, the biased random-key encoding is compared with a typical integer-based encoding. A comparison with a dataset from the literature shows that the developed algorithm can compete with state-of-the-art methods on benchmark instances.