Recent Advances in Robot Trajectory Planning in a Dynamic Environment

Background: Trajectory planning is important to research in robotics. As the application environment changes rapidly, robot trajectory planning in a static environment can no longer meet actual needs. Therefore, a lot of research has turned to robot trajectory planning in a dynamic environment. Objective: This paper aims at providing references for researchers from related fields by reviewing recent advances in robot trajectory planning in a dynamic environment. Methods: This paper reviews the latest patents and current representative articles related to robot trajectory planning in a dynamic environment and introduces some key methods of references from the aspects of algorithm, innovation and principle. Results: In this paper, we classified the researches related to robot trajectory planning in a dynamic environment in the last 10 years, introduced and analyzed the advantages of different algorithms in these patents and articles, and the future developments and potential problems in this field are discussed. Conclusion: Trajectory planning in a dynamic environment can help robots to accomplish tasks in a complex environment, improving robots’ intelligence, work efficiency and adaptability to the environment. Current research focuses on dynamic obstacle avoidance, parameter optimization, real-time planning, and efficient work, which can be used to solve robot trajectory planning in a dynamic environment. In terms of the combination of multiple algorithms, multi-sensor information fusion, the combination of local planning and global planning, and multi-robot and multi-task collaboration, more improvements and innovations are needed. It should create more patents on robot trajectory planning in a dynamic environment.

Teaching game for understanding model: increasing motivation and students’ physical fitness

This study aims to describe the effect of Teaching Game for Understanding (TGfU) in increasing the students’ motivation and physical fitness. TGfU is a training concept of sport based on game. It is not only have meaning as strengthening educational praxis in fields of sport-game separately, but rather to develop an educational approach to the problems solving in tactical strategy in a competition of the sport-game, and also about attitude, motivation, responsibility for the task, collaboration, etc. Method of this study uses a qualitative approach with classroom action research. The results of the study reported that method of TGfU can increase students’ motivation and physical fitness especially in the second cycle of teaching and learning in the field. For sport teacher is advised to use this model in order to improve students’ participation, motivation, tactical cognitive and strategy and sport achievement of their physical fitness, especially for the subject matter of sport required the game approach.

Multi-Task Collaboration Deep Learning Framework for Infrared Precipitation Estimation

Infrared observation is an all-weather, real-time, large-scale precipitation observation method with high spatio-temporal resolution. A high-precision deep learning algorithm of infrared precipitation estimation can provide powerful data support for precipitation nowcasting and other hydrological studies with high timeliness requirements. The “classification-estimation” two-stage framework is widely used for balancing the data distribution in precipitation estimation algorithms, but still has the error accumulation issue due to its simple series-wound combination mode. In this paper, we propose a multi-task collaboration framework (MTCF), i.e., a novel combination mode of the classification and estimation model, which alleviates the error accumulation and retains the ability to improve the data balance. Specifically, we design a novel positive information feedback loop composed of a consistency constraint mechanism, which largely improves the information abundance and the prediction accuracy of the classification branch, and a cross-branch interaction module (CBIM), which realizes the soft feature transformation between branches via the soft spatial attention mechanism. In addition, we also model and analyze the importance of the input infrared bands, which lay a foundation for further optimizing the input and improving the generalization of the model on other infrared data. Extensive experiments based on Himawari-8 demonstrate that compared with the baseline model, our MTCF obtains a significant improvement by 3.2%, 3.71%, 5.13%, 4.04% in F1-score when the precipitation intensity is 0.5, 2, 5, 10 mm/h, respectively. Moreover, it also has a satisfactory performance in identifying precipitation spatial distribution details and small-scale precipitation, and strong stability to the extreme-precipitation of typhoons.

The Working Alliance and Cross-Cultural Competence

The purpose of this chapter it to review the research-based concept of the working alliance, characterized by the forming of an emotional bond, worker/client goal consensus, and task collaboration, and to illustrate how it can be established and monitored by social workers. A related concept, that of cross-cultural competence, is also discussed. It refers to a social worker’s capacity and willingness to be attuned to a client’s culturally imbedded experiences, investigate the relevant details of that client’s experiences, and share with the client some of the social worker’s own relevant cultural experiences, to create mutual understanding. Cross-cultural competence facilitates the establishment of a working alliance.

It’s a Match: Task Assignment in Human–Robot Collaboration Depends on Mind Perception

Robots are becoming more available for workplace collaboration, but many questions remain. Are people actually willing to assign collaborative tasks to robots? And if so, exactly which tasks will they assign to what kinds of robots? Here we leverage psychological theories on person-job fit and mind perception to investigate task assignment in human–robot collaborative work. We propose that people will assign robots to jobs based on their “perceived mind,” and also that people will show predictable social biases in their collaboration decisions. In this study, participants performed an arithmetic (i.e., calculating differences) and a social (i.e., judging emotional states) task, either alone or by collaborating with one of two robots: an emotionally capable robot or an emotionally incapable robot. Decisions to collaborate (i.e., to assign the robots to generate the answer) rates were high across all trials, especially for tasks that participants found challenging (i.e., the arithmetic task). Collaboration was predicted by perceived robot-task fit, such that the emotional robot was assigned the social task. Interestingly, the arithmetic task was assigned more to the emotionally incapable robot, despite the emotionally capable robot being equally capable of computation. This is consistent with social biases (e.g., gender bias) in mind perception and person-job fit. The theoretical and practical implications of this work for HRI are being discussed.

Motivations of enterprise social media feature set usage

PurposeEnterprise social media (ESM) are widely adopted in the organizations with the aim of improving employees' work performance. The purpose of this paper is to investigate the moderating effects of work context characteristics on the relationships between proactive motivations and usage behaviors of ESM communication and task collaboration feature sets.Design/methodology/approachQuestionnaire surveys were used to gather data from 307 randomly chosen Chinese employees, who were also ESM users. The research model was examined using linear regression methods with the SPSS 24 software package.FindingsThe results indicate that work context characteristics (i.e. task interdependence and perceived organizational support) had significant moderating effects on the relationships between proactive motivations and usage behaviors.Originality/valueBy extending the model of proactive motivation to the ESM context, this study directly addresses how users can be energized to use ESM features.

KONSEP DASAR IPA BERBASIS STEM PJBL PADA KETERCAPAIAN DOMAIN AFEKTIF MAHASISWA PGMI STIT SUNAN GIRI BIMA

This study aims to describe the effect of learning the basic concepts of science-based STEM in the affective domain on student achievement of PGMI. STEM-based education is a new education concept. STEM is not only have meaning as strengthening educational praxis in fields of STEM separately, but rather to develop an educational approach to the problems solving in daily life, and also about attitude, motivation, responsibility for the task, collaboration, etc. Affective Domain is an important domain that should be raised because they relate to the experiences of learners in the learning environment. Method of this study uses a qualitative approach with classroom action research (CAR). The results of the study in the first cycle resulted the scores for each stage was 80% in average with students’ lowest score of 60 and 45. In the second cycle score increases become very good about 90% with students’ highest score was 85 and the lowest score was 75