Collaborative Tasks Research Articles

Sequential Task Allocation of More Scalable Artificial Dragonfly Swarms Considering Dubins Trajectory

With the rapid advancement of UAV technology and the increasing complexity of tasks, multi-UAV systems face growing challenges in task execution. Traditional task allocation algorithms often perform poorly when dealing with issues such as local optima, slow convergence speed, and low convergence accuracy, making it difficult to meet the demands for efficiency and practicality in real-world applications. To address these problems, this paper focuses on collaborative task allocation technology for multi-UAV. It proposes a collaborative task allocation strategy for multi-UAV in a multi-target environment, which comprehensively considers various complex constraints in practical application scenarios. The strategy utilizes Dubins curves for trajectory planning and constructs a multi-UAV collaborative task allocation model, with targets including the shortest total distance index, the minimum time index, and the trajectory coordination index. Each UAV is set as an artificial dragonfly by modifying the traditional dragonfly algorithm, incorporating differential evolution algorithms and their crossover, mutation, and selection operations to bring UAV swarms closer to the characteristics of biological dragonflies. The modifications can enhance the global scalability of artificial dragonfly swarms (ADSs), including wider search capacity, wider speed range, and more diverse search accuracy. Meanwhile, potential solutions with global convergence properties are stored to better support real-time adjustments to task allocation. The simulation results show that the proposed strategy can generate a conflict-free task execution scheme and plan the trajectory, which has advantages in changing the data scale of the UAV and the target and improves the reliability of the system to a certain extent.

A hybrid evolutionary algorithm integrated with specific neighborhood search for collaborative task planning of heterogeneous multi-UAVs under SEAD scenarios

A hybrid evolutionary algorithm integrated with specific neighborhood search for collaborative task planning of heterogeneous multi-UAVs under SEAD scenarios

Engaging Mixed-Age Learners with Personal Interests in ESL Beginners

This study explores the effectiveness of using personal hobbies and daily routines to engage mixed-age learners (ages 12-17) at A1-A2 English proficiency levels over a 34-week course designed to help them progress toward B1 proficiency. The research focuses on how integrating personal interests into lessons increases motivation, participation, and language acquisition. A class of 18 students was divided into sub-groups based on age and proficiency, with activities tailored to meet their developmental needs. The findings show that connecting lessons to students' lives significantly improves engagement, vocabulary retention, and oral communication. Collaborative tasks involving both younger and older students fostered peer learning, creating a supportive and inclusive classroom environment. This approach proves beneficial in mixed-level classrooms and could be adapted for higher proficiency levels and larger class sizes.

Collaborative timber joint assembly: Augmented reality for multi-level human-robot interaction

This research introduces an innovative Augmented Reality (AR) workflow for Human-Robot Interaction (HRI) in timber construction. The approach leverages human dexterity and adaptability alongside the strength and precision of robotic arms to assemble timber structures connected by wood-wood connections. While research in the field of automated construction generally focuses on singular interactions, such as robot agents carrying components and human agents attaching them, this paper explores multiple degrees of interaction involving cooperation or collaboration between agents. A new algorithmic framework is developed to automate the generation of holographic instructions and allocate assembly tasks to human and robot agents according to their abilities. The application to a full-scale demonstrator reveals that certain elements necessitate collaboration for assembly, while others can exclusively be assembled manually or robotically. Ultimately, the research also highlights the benefits of AR in assisting manual assembly, simulating robot trajectories, and increasing safety during collaborative tasks.

Communicating robots’ intent through visual cues enhances human anticipatory behavior in human–dual robot collaboration

The present study aims at exploring the effect of communicating robots’ intent through visual cues, to the human on a complex human-robot collaborative task. Specifically, it aims to investigate (i) whether the use of such “anticipatory cues” will have a positive effect on task efficiency, human safety and collaborating fluency, (ii) the degree of this effect with varying robots’ speed and (iii) whether a retention effect will be observed after the removal of the cues. For exploring these issues, a human - dual robot industrial assembly task was designed in a Virtual Reality simulation environment and testing was carried out by 64 volunteer participants. Results showed that communicating robots’ intent through visual cues enhanced human anticipatory behavior, resulting in a significant improvement in human safety, team efficiency and collaborative fluency, in conjunction with a favorable subjective tendency towards the robots. However, the positive effect of the anticipatory cues was not found to increase with higher robot speed. Finally, the findings suggest that prior exposure to the cues made participants more confident in coordinating with the robots, even when the cues were removed from them, thus retaining their prior efficiency but with a negative effect on safety. In summary, the study provides evidence that use of anticipatory visual cues accelerates the legibility of robot movement and fosters human confidence and familiarization. The use of anticipatory cues seems promising for high-pace, non-repetitive interactions with collaborative robots or as a training aid in more repetitive human-robot collaborative tasks.

Social presence and collaborative creativity in leaner media

As people increasingly rely on virtual spaces for both personal and professional exchanges, the dynamics of how we connect, share ideas, and work together have evolved. Online interactions often engage in modality switching where communication starts in one modality, such as talking, then switches to another, such as texting. We examined how modality switching affected creativity and social presence when collaborating using computer mediated communication. Participants first met via an audio or text modality, then switched to the other modality. They then engaged in a collaborative story creation task. Using the Social Information Processing Theory for our theoretical framework, we found that switching from text to audio benefitted creativity. We found no differences between texted and audio communication in social presence, desire for future interaction, or creativity. Replicating others’ results, we did find differences in conversational balance: Texted conversations were more balanced than audio conversations. We newly discovered that modality switching affected balance. Audio conversations were more balanced when they followed text interactions rather than preceded them. Ultimately, more balanced conversations – regardless of modality – led to greater creativity in dyads.

A simple method and case study of task planning for collaborative design based on FDSM

In multiperson collaborative mechanical product design, unreasonable task allocation can result in excessive coupling between tasks, complex design communication, and rework, particularly in small enterprises and teams that rely primarily on experience. Hence, this study proposes a simple and efficient task-planning method based on a fuzzy design structure matrix (FDSM), which involves single-level task decomposition, three-level number assignment of the coupling relationship, decoupling of the structural tree algorithm, and validation of the optimization results based on the G-value. Design tasks are promptly allocated into a single level based on the functional structure module of the product. The task-correlation degree is quantified based on three levels of numerical values through personal experience to construct an FDSM, the latter of which is decoupled by the correlation degree of the structural tree algorithm to obtain the optimized sequence and allocation of the collaborative design task. Finally, the effectiveness of the planning is verified by calculating the tightness of the corresponding matrix before and after the planning. A task-planning case of a riveting-machine design for capacitor cover plates is established to validate this method.

Confidential outsourced support vector machine learning based on well-separated structure

Support Vector Machine (SVM) has revolutionized various domains and achieved remarkable successes. This progress relies on subtle algorithms and more on large training samples. However, the massive data collection introduces security concerns. To facilitate secure integration of data efficiently for building an accurate SVM classifier, we present a non-interactive protocol for privacy-preserving SVM, named NPSVMT. Specifically, we define a new well-separated structure for computing gradients that can decouple the fusion matter between user data and model parameters, allowing data providers to outsource the collaborative learning task to the cloud. As a result, NPSVMT is capable of removing the multiple communications and eliminating the straggler’s effect (waiting for the last), thereby going beyond those developed with interactive methods, e.g., federated learning. To further decrease the data traffic, we introduce a high-efficient coding method to compress and parse training data. In addition, unlike outsourced schemes based on homomorphic encryption or secret sharing, NPSVMT exploits functional encryption to maintain the data confidentiality, achieving dropout-tolerant secure aggregation. The implementations verify that NPSVMT is faster by orders of magnitude than the existing privacy-preserving SVM schemes on benchmark datasets.

Facilitating active learning of sectional anatomy with technology-enhanced small-group tasks: Assessment of knowledge gains, technology usability, and students' perceptions.

Learning 2D sectional anatomy facilitates the comprehension of 3D anatomical structures, anatomical relationships, and radiological anatomy. However, the efficacy of technology-enhanced collaborative instructional activities in sectional anatomy remains unclear, especially if theoretical frameworks, namely the Cognitive Theory of Multimedia Learning (CTML), are applied in instructional design. Thus, this study compared the educational impact of distinct 45-min-long technology-enhanced collaborative learning tasks in sectional anatomy. A sample of 115 first-year medical students was randomly divided into three experimental groups that used different supporting technologies to learn the sectional anatomy of the chest: IMAIOS e-learning platform and Microsoft Surface Hub (n = 37); anatomage table (n = 38); anatomage table with CTML-based presets (n = 40). Prelearning and postlearning tests revealed that significant knowledge gains in sectional anatomy were obtained by all groups even though no inter-group differences were found. Moreover, a five-point Likert scale questionnaire showed that the learning session was highly valued by all participants and that users of the anatomage with CTML-based presets reported higher enjoyment than users of the IMAIOS system (mean difference = 0.400; p = 0.037). In addition, students using the IMAIOS system and the anatomage with CTML-based presets provided System Usability Scale (SUS) scores of 67.64 and 67.69, respectively, reaching the benchmark of usability. By contrast, students using the anatomage table without presets awarded a SUS score of 64.14. These results suggest that the integration of multimedia technologies in anatomy teaching and learning should be grounded on CTML principles of instructional design. Otherwise, students' perceptions of ed-tech usability are potentially hindered.

A multi-stage approach for desired part grasping under complex backgrounds in human-robot collaborative assembly

Human-robot collaborative assembly can leverage the unique capabilities of humans and robots to provide a flexible and efficient way for complex tasks. In this context, robots are expected to be endowed with the perception ability to collaborate with humans. For flexible processes, robotic grasping for a desired assembly part from variable multi-parts is essential but remains challenging, especially in situations of complex backgrounds including disordered placement, occlusion, similarity, and large-scale differences in size. To improve the perception and decision-making abilities of robots in collaborative assembly, firstly, a multi-stage approach integrating 2D part recognition and 6D pose estimation with perspective-focusing is proposed for desired part grasping under complex backgrounds. Secondly, to efficiently and accurately recognize a desired part from variable multi-parts under complex backgrounds, based on RGB data, an enhanced detector is adopted, which lays the foundation for the subsequent pose estimation. Thirdly, based on point cloud data, a pose estimation method based on perspective-focusing is proposed to enhance the success rate and efficiency of pose acquisition. Additionally, a modular autonomous robotic grasping system is designed. Finally, taking decelerator assembly as a case, the proposed approaches are comprehensively validated and compared on a real collaborative robot platform. Under complex backgrounds with occlusion, our approach shows stable performance with an average grasping success rate of 90.0 % and an average cycle time of 6.4175 s. Our work is expected to improve robots’ perception and decision-making capabilities while further reducing the cycle time of collaborative assembly tasks.

Hybrid Orientation and Position Collaborative Motion Generation Scheme for a Multiple Mobile Redundant Manipulator System Synthesized by a Recurrent Neural Network.

To enable distributed multiple mobile manipulator systems to complete collaborative tasks safely and stably, this article investigates and presents a motion generation scheme that considers both orientation and position coordination based on a distributed recurrent neural network. Moreover, physical limits are also considered. Specifically, the orientation and position coordination constraints and physical limits are modeled separately as equality and inequality constraints with coupled variables. Subsequently, a motion generation scheme for multiple mobile manipulators based on quadratic programming is established. Finally, a distributed linear variational inequality-based primal-dual neural network is constructed to solve the motion generation scheme and obtain the motion trajectories of all the mobile manipulators. The simulation results demonstrate that the hybrid orientation and position collaboration motion generation scheme effectively addresses the position and orientation coordination problem for multiple mobile manipulator systems. Compared to other schemes, the proposed scheme based on a distributed computing structure greatly enhances the stability of the system. Additionally, the proposed approach introduces orientation coordination and physical limits, which increases the practicality of the system.

Motion priority optimization framework towards automated and teleoperated robot cooperation in industrial recovery scenarios

In this study, we introduce an optimization framework to enhance the efficiency of motion priority design in scenarios involving automated and teleoperated robots within an industrial recovery context. The increasing utilization of industrial robots at manufacturing sites has been instrumental in reducing human workload. Nevertheless, achieving effective human–robot collaboration/cooperation (HRC) remains a challenge, especially when human workers and robots share a workspace for collaborative tasks. For instance, when an industrial robot encounters a failure, such as dropping an assembling part, it triggers the suspension of the corresponding factory cell for safe recovery. Given the limited capacity of pre-programmed robots to rectify such failures, human intervention becomes imperative, requiring entry into the robot workspace to address the dropped object while the robot system is halted. This discontinuous manufacturing process results in productivity loss. Robotic teleoperation has emerged as a promising technology enabling human workers to undertake high-risk tasks remotely and safely. Our study advocates for the incorporation of robotic teleoperation in the recovery process during manufacturing failure scenarios, which is referred to as “Cooperative Tele-Recovery”. Our proposed approach involves formulating priority rules designed to facilitate collision avoidance between manufacturing and recovery robots. This, in turn, ensures a continuous manufacturing process with minimal production loss within a configurable risk limitation. We present a comprehensive motion priority optimization framework composed of an HRC simulator and a cooperative multi-robot controller to identify optimal parameters for the priority function. The framework dynamically adjusts the allocation of motion priorities for manufacturing and recovery robots while adhering to predefined risk limitations. Through quantitative and qualitative assessments, we validate the novelty of our concept and demonstrate its feasibility.

A collaborative navigation algorithm for UAV Ad Hoc network based on improved sequence quadratic programming and unscented Kalman filtering in GNSS denied area

In global navigation satellite system (GNSS) denied area, collaborative positioning precision is important for unmanned aerial vehicle (UAV) to perform collaborative tasks in Ad Hoc network. Continuous optimization of algorithm complexity and parameter setting has long been a focal point in the field of collaborative positioning research. An algorithm of UAVs collaborative positioning based on improved sequential quadratic programming (SQP) and unscented kalman filter (UKF) is proposed. By optimizing the parameter constraints of SQP, parameters are controlled within certain conditions. The convergence speed of SQP is improved, and more accurate filtering parameters are obtained. The improved SQP and UKF are combined to optimize the location data of UAVs, which improves the precision of collaborative positioning. The simulation results show that the positioning precision can be greatly enhanced by approximately 25% when using the proposed algorithm, as compared to the traditional algorithm. The influence of the collaborative UAV number also is simulated. The positioning performance improve slowly when more than five UAVs are used for collaborative positioning. Therefore, the positioning performance cannot be improved only by increasing the number of UAVs, but also by improving the performance of collaborative positioning algorithm.

Traffic Road Congestion System Using by the Internet of Vehicles (IoV): A Systematic Literature Review

Traffic problems have increased in modern life due to the significant number of vehicles, urbanization, and non-compliance with traffic regulations. Vehicular ad hoc networks (VANETs) have made improvements to the traffic system in the past, playing a crucial role in establishing efficient traffic control systems in large cities. However, VANETs alone cannot effectively address certain problems under specific conditions. Presently, the development of new Internet of Things (IoT) technologies has enabled collaborative and efficient task execution. This technology has been implemented in the transportation system, transforming it into an intelligent transportation system (ITS), known as the Internet of Vehicles (IoV). This study investigates traffic issues within the traditional system and explores the benefits, enhancements, and reasons for improving IoV through a comprehensive Systematic Literature Review (SLR). The SLR approach involved targeted searches using multiple search phrases, including 25 articles published between 2016 and 2023. Furthermore, discussion on the necessary IoV technologies and tools required to establish IoV and address specific traffic challenges. Simulation of Urban Mobility (SUMO) is employed for the design and simulation of road traffic and we aim to contribute to the development of an optimal traffic control system. This paper analyzes two vehicular congestion control models, selects the most optimized and efficient model, and provides evidence for its effectiveness through a Systematic Literature Review (SLR)-based investigation based on its efficient features, in the end, we propose IoV based on vehicular clouds as a superior model, surpassing the capabilities of the traditional model and enhancing the network system.

An action-observation method for studying social perception: a mini-review.

An important aspect of any social interaction involves inferring other people's mental states, intentions, and their likely next actions, by way of facial expression, body posture, eye gaze, and limb movements. An actor's production of actions during social interactions and the observer's perception of these actions are thus closely linked. In this review, we outline an action-observation methodology, which not only allows for separate analyses of production and perception, but also promotes the study of the dynamic interaction between these two sides of every social exchange. We review two lines of research that have benefited from its application. The first line focuses on individuals performing tasks alone and the observation of their actions by other individuals in order to make inferences about their attentional states. The second line of study focused on pairs of individuals performing collaborative tasks in naturalistic settings and the observation of these performances by other individuals. We offer several suggestions for how this methodology can be extended to improve on the limitations of the present studies, as well as some suggestions of how to use this methodology to venture into new territory. Our aim is to inspire future research applications of this methodology in order to advance our understanding of social action production and perception.

Collaborative Policymaking: a qualitative systematic review of advice for policymakers

Background Complex policy problems are not amenable to simple solutions by a few powerful policy actors in one central government. They require collaboration across government and between actors inside and outside of government. However, this requirement for collaboration is no guarantee of collective action. Further, it is difficult to know how to collaborate effectively. We searched the academic and grey literature for advice on how to foster collaborative policymaking. Methods We conducted a qualitative systematic review (2024) of peer reviewed journal articles (Web of Science) and grey literature reports (Policy Commons). Each article or report had to inform advice on collaborative policymaking. We used an immersive and inductive approach to identify key themes and relate the results to well-established insights from policy theories. Results 86 texts meet the inclusion criteria (49 Web of Science, 37 Policy Commons). Most provide broad definitions of collaborative policymaking, which are similar to definitions of collaborative governance (and connected aims such as policy co-creation). Many assert or assume that greater collaboration, across and inside/outside of government, will improve policymaking and policy. Few individual studies give advice on how to collaborate effectively, but they combine to identify common features of collaboration. Conclusions We synthesise the available advice to identify five main features of collaborative policymaking: plan and prepare to collaborate, such as by designing rules and allocating resources; create a sense of collective purpose, such as by setting a boundary around the collaboration and co-producing a common vision; foster creative methods to visualise collaboration and design policy; create new forums to supplement formal collaboration; and clarify the roles and skills essential to each collaborative task.

Multi-Robot Path Planning Algorithm for Collaborative Mapping under Communication Constraints

In extensive outdoor collaborative exploration tasks, multiple robots require efficient path planning methods to ensure rapid and comprehensive map construction. However, current collaborative mapping algorithms often integrate poorly with path planning, especially under limited communication conditions. Such conditions can complicate data exchange, leading to inefficiencies and missed areas in real-world environments. This paper introduces a path planning approach specifically designed for distributed collaborative mapping tasks, aimed at enhancing map completeness, mapping efficiency, and communication robustness under communication constraints. We frame the entire task as a k-Chinese Postman Problem (k-CPP) and optimize it using a genetic algorithm (GA). This method fully leverages topology maps to efficiently plan subpaths for multiple robots, ensuring thorough coverage of the mapping area without the need for prior navigation maps. Additionally, we incorporate communication constraints into our path planning to ensure stable data exchange among robots in environments with only short-range communication capabilities. Field experiment results highlight the superior performance of our method in terms of stability, efficiency, and robust inter-robot communication.

Peering into the team role kaleidoscope: the interplay of personal characteristics and verbal interactions in collaborative problem solving.

The objective of this study is to explore the relationship between personality and peer-rated team role behavior on the one hand and team role behavior and verbal behavior on the other hand. To achieve this, different data types were collected in fifteen professional teams of four members (N = 60) from various private and public organizations in Flanders, Belgium. Participants' personalities were assessed using a workplace-contextualized personality questionnaire based on the Big Five, including domains and facets. Typical team role behavior was assessed by the team members using the Team Role Experience and Orientation peer rating system. Verbal interactions of nine of the teams (n = 36) were recorded in an educational lab setting, where participants performed several collaborative problem-solving tasks as part of a training. To process these audio data, a coding scheme for collaborative problem solving and linguistic inquiry and word count were used. We identified robust links and logical correlation patterns between personality traits and typical team role behaviors, complementing prior research that only focused on self-reported team behavior. For instance, a relatively strong correlation was found between Altruism and the Team builder role. Next, the study reveals that role taking within teams is associated with specific verbal interaction patterns. For example, members identified as Organizers were more engaged in responding to others' ideas and monitoring execution.

Who benefits from virtual collaboration? The interplay of team member expertness and Big Five personality traits

This research applies and integrates transactive memory systems (TMS) theory and the Big Five personality traits model to investigate the performance dynamics of dyadic teams engaged in virtual collaborative problem-solving (CPS). Specifically, this study examines how the personal attributes of team members, including their expertness and Big Five personality traits (extraversion, agreeableness, openness, conscientiousness, and neuroticism), as well as the resultant diversity in expertness and Big Five personality traits within teams, influence both team-level and individual-level performance gain from virtual collaboration. Studying 377 dyadic teams composed of 754 individuals working on an online collaborative intellective task, this research found that dyads with high expertness diversity had greater performance gain from virtual collaboration than dyads with low expertness diversity. Further, dyads, where both members scored low on agreeableness, showed the most significant improvement in team performance. At the individual level, a team member who had a low expertness level but was paired with a high-expertness teammate demonstrated the greatest performance gain from virtual collaboration. The integration of TMS theory and the Big Five personality traits model provides a richer and more nuanced understanding of how individual attributes and team dynamics contribute to successful virtual CPS outcomes.

Understanding Human-Robot Interactions with a Humanoid Robot

Human responses towards robots vary from excitement and acceptance to complete rejection, and as robots become more prevalent in personal and public spaces, understanding the factors that elicit those responses is important. This research explores the factors influencing these responses, specifically in the context of human-robot collaborative tasks. Under mechatronics and robotics, the study focuses on identifying behavioural features (like triggers or positive/negative eliciting actions) and human-designed robot algorithms that elicit different human reactions. It also examines how these features interact to shape human behaviour and emotions in stressful and calm situations. I developed a study in a simulated medical setting where participants are tasked with developing dialogue scripts for a social robot. The study utilizes the NAO robot and a programmable platform called the Robot Management System developed by RobotLab. Participants will program NAO to interact with a patient, perform a checkup, and decide on actions like writing a prescription or referring the patient to medical professionals. The experiment is designed to observe how the participant interacts with the robot to complete tasks and how the robot’s design impacts the participants' perception and interaction following and during tasks. While no experimental data has been collected due to pending ethics approval, the research will provide insights into human-robot interaction, including algorithm design. This study aims to enhance our understanding of human-robot relationships, contributing to developing robotic systems that can be effectively integrated into social environments, such as healthcare settings or similar environments. These insights may inform the design and deployment of robots to ensure they support rather than disrupt social dynamics.