Improving the weight coefficients of DWA algorithm.

Improving the weight coefficients of DWA algorithm.</p

Improved A* algorithm flowchart.

The current robot path planning methods only use global or local methods, which is difficult to meet the real-time and integrity requirements, and can not avoid dynamic obstacles. Based on this, this study will use the improved A-star global planning algorithm to design a hybrid robot obstacle avoidance path planning algorithm that integrates sliding window local planning methods to solve related problems. Specifically, A-star is optimized by evaluation function, sub node selection mode and path smoothness, and fuzzy control is introduced to optimize the sliding window algorithm. The study conducted algorithm validation on the TurtleBot3 mobile robot, with data sourced from experimental data from a certain college. The results showed that hybrid algorithm enabled the planned path to effectively navigate around dynamic obstacles and reach the target point accurately. When compared with traditional methods, path length reduced by 9.6%, path planning time decreased by 29% with an approximate 26.7% increase in the average speed of the robot. Compared with the traditional methods, the research algorithm has greatly improved in avoiding dynamic obstacles, path planning efficiency, model adaptability and so on, which has important value for relevant research. It can be seen that the algorithm proposed in the study has performance advantages, demonstrating the effectiveness and advantages of robot path planning, and can provide reference for robot obstacle avoidance optimization. Research can complete tasks for robots in practical environments, which has certain reference value for the research of robots in path planning and the development of path obstacle avoidance planning.</div

Structure diagram of fuzzy controller.

The current robot path planning methods only use global or local methods, which is difficult to meet the real-time and integrity requirements, and can not avoid dynamic obstacles. Based on this, this study will use the improved A-star global planning algorithm to design a hybrid robot obstacle avoidance path planning algorithm that integrates sliding window local planning methods to solve related problems. Specifically, A-star is optimized by evaluation function, sub node selection mode and path smoothness, and fuzzy control is introduced to optimize the sliding window algorithm. The study conducted algorithm validation on the TurtleBot3 mobile robot, with data sourced from experimental data from a certain college. The results showed that hybrid algorithm enabled the planned path to effectively navigate around dynamic obstacles and reach the target point accurately. When compared with traditional methods, path length reduced by 9.6%, path planning time decreased by 29% with an approximate 26.7% increase in the average speed of the robot. Compared with the traditional methods, the research algorithm has greatly improved in avoiding dynamic obstacles, path planning efficiency, model adaptability and so on, which has important value for relevant research. It can be seen that the algorithm proposed in the study has performance advantages, demonstrating the effectiveness and advantages of robot path planning, and can provide reference for robot obstacle avoidance optimization. Research can complete tasks for robots in practical environments, which has certain reference value for the research of robots in path planning and the development of path obstacle avoidance planning.</div

Improving the weight coefficients of DWA algorithm.

Improving the weight coefficients of DWA algorithm.</p

Schematic diagram of optimizing sub nodes.

The current robot path planning methods only use global or local methods, which is difficult to meet the real-time and integrity requirements, and can not avoid dynamic obstacles. Based on this, this study will use the improved A-star global planning algorithm to design a hybrid robot obstacle avoidance path planning algorithm that integrates sliding window local planning methods to solve related problems. Specifically, A-star is optimized by evaluation function, sub node selection mode and path smoothness, and fuzzy control is introduced to optimize the sliding window algorithm. The study conducted algorithm validation on the TurtleBot3 mobile robot, with data sourced from experimental data from a certain college. The results showed that hybrid algorithm enabled the planned path to effectively navigate around dynamic obstacles and reach the target point accurately. When compared with traditional methods, path length reduced by 9.6%, path planning time decreased by 29% with an approximate 26.7% increase in the average speed of the robot. Compared with the traditional methods, the research algorithm has greatly improved in avoiding dynamic obstacles, path planning efficiency, model adaptability and so on, which has important value for relevant research. It can be seen that the algorithm proposed in the study has performance advantages, demonstrating the effectiveness and advantages of robot path planning, and can provide reference for robot obstacle avoidance optimization. Research can complete tasks for robots in practical environments, which has certain reference value for the research of robots in path planning and the development of path obstacle avoidance planning.</div

Comparison of experimental data of different algorithms in different maps.

Comparison of experimental data of different algorithms in different maps.</p

Table_1_Team-based learning vs. lecture-based learning in nursing: A systematic review of randomized controlled trials.DOCX

IntroductionOur study aims to identify, appraise, and summarize randomized controlled trials (RCT) on the effectiveness of team-based learning (TBL) versus lecture-based learning (LBL) in nursing students.MethodsWe searched PubMed, Ovid, Embase, Cochrane, CBM, VIP, CNKI, and Wan Fang databases from inception to 22nd July 2022 to enroll RCTs that compared TBL versus LBL. The studies reporting the performance of nursing students receiving TBL pedagogy compared to those receiving traditional lecture-based learning (LBL) were to be analyzed. Scores of academic or nursing abilities were considered the primary outcome, and the results of nursing competencies, students' engagement with, behaviors, attitudes toward, experience, satisfaction, or perceptions of TBL were considered the secondary outcome. This systematic review was conducted following the guidelines of the Cochrane Reviewer's Handbook and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement.ResultsA total of 1,009 participants in 10 RCTs were enrolled in this study. Of the 10 RCTs, eight studies investigated undergraduate students, one involved vocational college students, and one enrolled secondary school students. The most reported outcomes were class engagement survey toward TBL (n = 8); students' ability (n = 5), academic knowledge or performance (n = 4); students' experience (n = 4), satisfaction or perceptions of TBL (n = 4).ConclusionThis review suggested that the TBL was an effective pedagogy in improving academic performance and general ability in nursing students. High-quality trials are needed, and standardized outcomes should be used.</p

Box plot of experimental results for different algorithms.

Box plot of experimental results for different algorithms.</p

Schematic diagram of path smoothing distance relationship.

Schematic diagram of path smoothing distance relationship.</p

Box plot of experimental results for different algorithms.

Box plot of experimental results for different algorithms.</p