Magnetically driven microrobots moving in a flow:a review

Magnetically driven microrobots hold great potential to perform specific tasks more locally and less invasively in the human body.To reach the lesion area in vivo,microrobots should usually be navigated in flowing blood,which is much more complex than static liquid.Therefore,it is more challenging to design a corresponding precise control scheme.A considerable amount of work has been done regarding control of magnetic microrobots in a flow and the corresponding theories.In this paper,we review and summarize the state-of-the-art research progress concerning magnetic microrobots in blood flow,including the establishment of flow systems,dynamics modeling of motion,and control methods.In addition,current challenges and limitations are discussed.We hope this work can shed light on the efficient control of microrobots in complex flow environments and accelerate the study of microrobots for clinical use.

Crowdmodeling based on purposiveness and a destination-driven analysis method

This study focuses on the multiphase flow properties of crowd motions.Stability is a crucial forewarning factor for the crowd.To evaluate the behaviors of newly arriving pedestrians and the stability of a crowd,a novel motion structure analysis model is established based on purposiveness,and is used to describe the continuity of pedestrians’pursuing their own goals.We represent the crowd with self-driven particles using a destination-driven analysis method.These self-driven particles are trackable feature points detected from human bodies.Then we use trajectories to calculate these self-driven particles’purposiveness and select trajectories with high purposiveness to estimate the common destinations and the inherent structure of the crowd.Finally,we use these common destinations and the crowd structure to evaluate the behavior of newly arriving pedestrians and crowd stability.Our studies show that the purposiveness parameter is a suitable descriptor for middle-density human crowds,and that the proposed destination-driven analysis method is capable of representing complex crowd motion behaviors.Experiments using synthetic and real data and videos of both human and animal crowds have been conducted to validate the proposed method.

Applying project‐based learning in artificial intelligence and marine discipline:An evaluation study on a robotic sailboat platform

intelligence is penetrating various fields.The demand for interdisciplinary talent is increasingly important,while interdisciplinary educational activities for high school students are lagging behind.Project‐based learning(PBL)in artificial intelligence(AI)and robotic education activities supported by a robotic sailboat platform,the sailboat test arena(STAr),has been shown to popularise AI and robotic knowledge in young students.In the implementation of the programme,PBL was provided for students,and gamification pedagogy was applied to increase participants'learning motivation and engagement.The results show that the proposed STAr‐based programme is capable of delivering the desired knowledge and skills to students at high school levels.The assessment results suggest that most students achieve learning outcomes on average.Students showed more interest in AI and marine disciplines and were willing to participate in more such educational programs.The findings fill the research gap that few existing education platforms have facilitated the teaching and learning of AI and marine disciplines for high school students.