collaborative task
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2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Hong Wang

Aiming at the problem that traditional fixed base stations cannot provide good signal coverage due to geographical factors, which may reduce the efficiency of task offloading, a collaborate task offloading strategy using improved genetic algorithm in mobile edge computing (MEC) is proposed by introducing the unmanned aerial vehicle (UAV) cluster. First, for the scenario of the UAV cluster serving multiple ground terminals, a collaborative task offloading model is formulated to offload the tasks to UAVs or the base station selectively. Then, an objective function and related constraints are put forward to minimize the time delay and energy consumption by analysis of those in the communication and computing process in the system while considering many factors. Then, the improved genetic algorithm is introduced to solve the optimization problem, obtaining the optimal collaborative task offloading strategy. To verify the performance of the proposed method, simulations are conducted on MATLAB. Simulation results showed that the joint utilization of UAV and MEC improves the offloading efficiency of the proposed strategy. When the number of UAVs is 12, the total utility is up to 1.83 and the task completion time does not exceed 110 ms. In this case, the task can be reasonably offloaded to UAVs or accomplished locally.


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Weilei Shen ◽  
Qiangqiang Jiang ◽  
Yang Yang

Purpose The purpose of this paper is to construct a task assignment model for U-shaped production lines with collaborative task, which is optimized by minimizing the number of workers and balancing the workload of the operators. The ultimate goal is to increase productivity by increasing the U-line balance and balancing the load on the operators. Design/methodology/approach First, task selection and update mechanism are analyzed and the task selection mechanism suitable for collaborative task is proposed. Second, M-COMOSAL is obtained by improving the original COMOSAL. Finally, The M-COMOSAL algorithm and the COMAOSAL algorithm are used to perform job assignment on the double-acting clutch U-shaped assembly line. Findings According to the allocation scheme obtained by M-COMSOAL, the beat can be adjusted according to the change of order demand. The final allocation scheme is superior to the COMSOAL algorithm in terms of number of workers, working time, production tempo and balance rate. In particular, compared with the old scheme, the new scheme showed a decrease of 16.7% in the number of employees and a 18.8% increase in the production line balance rate. Thus, the method is helpful to reduce the number of operators and balance the workload. Originality/value The new algorithm proposed in this paper for the assignment of collaborative task can minimize the number of workers and balance the load of operators, which is of great significance for improving the balance rate of U-shaped production lines and the utilization of personnel or equipment.


2021 ◽  
Author(s):  
Ali Noormohammadi ◽  
Abhinav Dahiya ◽  
Alexander Mois Aroyo ◽  
Stephen L. Smith ◽  
Kerstin Dautenhahn

Author(s):  
Eetu Haataja ◽  
Jonna Malmberg ◽  
Muhterem Dindar ◽  
Sanna Järvelä

AbstractBeing aware of the progress towards one’s goals is considered one of the main characteristics of the self-regulation process. This is also the case for collaborative problem solving, which invites group members to metacognitively monitor the progress with their goals and externalize it in social interactions while solving a problem. Monitoring challenges can activate group members to control the situation together, which can be seen as adjustments on different systemic levels (physiological, psychological, and interpersonal) of a collaborative group. This study examines how the pivotal role of monitoring for collaborative problem solving is reflected in interactions, performance, and interpersonal physiology. The study has foci in two central characteristics of monitoring interactions that facilitate groups’ regulation in reaching their goals. First is valence of monitoring, indicating whether the group members think they are progressing towards their goal or not. Second is equality of participation in monitoring interactions between group members. Participants of the study were volunteering higher education students (N = 57), randomly assigned to groups of three members whose collaborative task was to learn to run a business simulation. The collaborative task was video recorded, and the physiological arousal of each participant was recorded from their electrodermal activity. The results of the study suggest that both the valence and equality of participation are identifiable in monitoring interactions and they both positively predict groups’ performance in the task. Equality of participation to monitoring was not related to the interpersonal physiology. However, valence of monitoring was related to interpersonal physiology in terms of physiological synchrony and arousal. The findings support the view that characteristics of monitoring interactions make a difference to task performance in collaborative problem solving and that interpersonal physiology relates to these characteristics.


2021 ◽  
Vol 33 (5) ◽  
pp. 1104-1116
Author(s):  
Yoshihiro Tanaka ◽  
Shogo Shiraki ◽  
Kazuki Katayama ◽  
Kouta Minamizawa ◽  
Domenico Prattichizzo ◽  
...  

Tactile sensations are crucial for achieving precise operations. A haptic connection between a human operator and a robot has the potential to promote smooth human-robot collaboration (HRC). In this study, we assemble a bilaterally shared haptic system for grasping operations, such as both hands of humans using a bottle cap-opening task. A robot arm controls the grasping force according to the tactile information from the human that opens the cap with a finger-attached acceleration sensor. Then, the grasping force of the robot arm is fed back to the human using a wearable squeezing display. Three experiments are conducted: measurement of the just noticeable difference in the tactile display, a collaborative task with different bottles under two conditions, with and without tactile feedback, including psychological evaluations using a questionnaire, and a collaborative task under an explicit strategy. The results obtained showed that the tactile feedback provided the confidence that the cooperative robot was adjusting its action and improved the stability of the task with the explicit strategy. The results indicate the effectiveness of the tactile feedback and the requirement for an explicit strategy of operators, providing insight into the design of an HRC with bilaterally shared haptic perception.


2021 ◽  
Author(s):  
Paul Léné ◽  
Alexander J. Karran ◽  
Elise Labonté‐Lemoyne ◽  
Sylvain Sénécal ◽  
Marc Fredette ◽  
...  

2021 ◽  
Author(s):  
Jianji Ren ◽  
Tingting Hou ◽  
Haichao Wang ◽  
Huanhuan Tian ◽  
Huihui Wei ◽  
...  

2021 ◽  
Vol 4 ◽  
Author(s):  
Ryo Nakahashi ◽  
Seiji Yamada

The human-agent team, which is a problem in which humans and autonomous agents collaborate to achieve one task, is typical in human-AI collaboration. For effective collaboration, humans want to have an effective plan, but in realistic situations, they might have difficulty calculating the best plan due to cognitive limitations. In this case, guidance from an agent that has many computational resources may be useful. However, if an agent guides the human behavior explicitly, the human may feel that they have lost autonomy and are being controlled by the agent. We therefore investigated implicit guidance offered by means of an agent’s behavior. With this type of guidance, the agent acts in a way that makes it easy for the human to find an effective plan for a collaborative task, and the human can then improve the plan. Since the human improves their plan voluntarily, he or she maintains autonomy. We modeled a collaborative agent with implicit guidance by integrating the Bayesian Theory of Mind into existing collaborative-planning algorithms and demonstrated through a behavioral experiment that implicit guidance is effective for enabling humans to maintain a balance between improving their plans and retaining autonomy.


Author(s):  
Ali Ayub ◽  
Huiqing Hu ◽  
Guangwei Zhou ◽  
Carter Fendley ◽  
Crystal Ramsay ◽  
...  

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