Currently, the robot is playing an increasingly significant role in managing a warehouse. This paper proposes an optimal method to help a warehouse robot make task decisions, which aims at minimizing the whole cost of completing multiple tasks. Firstly, Abstract Transition System (ATS) is used to model the warehouse environment, and Linear Temporal Logic (LTL) formula is used to formulate the tasks of warehouse robot. Then based on the ATS and the Büchi automaton translated from the LTL formula, a Min-cost Task Decision Algorithm is proposed to obtain the task decision for the warehouse robot. The decision points out the optimal order and path for the robot to do its tasks. The effectiveness of the proposed method is validated through case studies with two kinds of tasks.
Background Healthcare-associated infections (HAI) are infections acquired by patients during treatment in various healthcare institutions. These infections significantly increase morbidity, mortality, and healthcare costs. Enhancing HAI education for nurses can improve patient safety and medical quality. Aim The study aimed to assess the effectiveness of the new conceive-design-implement-operate (CDIO) teaching model on nursing students’ HAI learning outcomes and compare it with the traditional LBL model, providing valuable insights for future HAI education in nursing. Methods A total of 110 nursing students were randomly assigned to one of two groups for HAI training during the 2022–2023 academic year: a group that engaged in the CDIO model and another that received traditional lecture-based learning (LBL). The effectiveness of these pedagogical approaches was evaluated by comparing pre-and post-training test scores, and we used the Course Experience Questionnaire (CEQ) to collect students’ feedback on the course and teaching. Results Compared to traditional LBL method, the CDIO model significantly improved the overall scores and practical application scores of nursing students in the HAI course, with these advantages still retained after 24 weeks. Additionally, preliminary results show that students in the CDIO model scored higher on CEQ categories such as good teaching, clear goals and standards, appropriate assessment, generic skills, and independence, but they also reported an increased workload. Conclusion Our research is the first to apply the CDIO framework to nursing education in HAI courses, enhancing nursing students’ practical application skills, particularly in the sustained retention in this area. Our study indicates that the CDIO teaching model has significant advantages in enhancing course experience and teaching effectiveness.
With the rapid development of inspection techniques, more emphases should be placed on the improvement of the reliability, safety and intelligence of the robot system. In this paper, a framework for the patrol robot that automatically finishes complex task and motion planning in the indoor substation is proposed. To realize real-time response to the environmental changes, the proposed framework keeps an ongoing interaction with the environment as a Reactive System (RS). The RS employs the Transition System (TS) and Nondeterministic Biichi Automaton (NBA) to create a discrete controller that bounds the acts of the patrol robot in the safe and reasonable specifications. What's more, the environment signals are treated as the trigger condition of task switching. If a new environment information is detected, our approach can automatically give a feasible plan. Then, the sensor-based mechanism of continuous controllers is guided by the discrete controller, which results in a hybrid system satisfying the high-level specification. The experiment within the LTLMoP toolkit verifies the proposed framework.
In this paper, we consider a new unmanned aerial vehicle (UAV)-assisted oblique image acquisition system where a UAV is dispatched to take images of multiple ground targets (GTs). To study the three-dimensional (3D) UAV trajectory design for image acquisition, we first propose a novel UAV-assisted oblique photography model, which characterizes the image resolution with respect to the UAV's 3D image-taking location. Then, we formulate a 3D UAV trajectory optimization problem to minimize the UAV's traveling distance subject to the image resolution constraints. The formulated problem is shown to be equivalent to a modified 3D traveling salesman problem with neighbourhoods, which is NP-hard in general. To tackle this difficult problem, we propose an iterative algorithm to obtain a high-quality suboptimal solution efficiently, by alternately optimizing the UAV's 3D image-taking waypoints and its visiting order for the GTs. Numerical results show that the proposed algorithm significantly reduces the UAV's traveling distance as compared to various benchmark schemes, while meeting the image resolution requirement.
Track initiation is the primary problem in multi-target tracking. The logical method of track initiation is a sequential processing technology, with the advantages of small calculation and high efficiency, and a modified logic method can be used to further improve the efficiency of track initiation. This paper optimizes the logic method from three aspects: the selection of logical rules, the limitation of signal-to-noise ratio (SNR) and the screening of point in wave gate, based on a vehicle radar, combined with the application scenarios of radar. The actual test results show that the false track can be reduced by about half, when the influence on the target track is small.
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