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Aiming at the problem of subjectivity and uncertainty in the configuration and selection of spherical robot design schemes in multi-demand unstructured environments, a scheme evaluation method coupled with fuzzy rough number and complex proportional assessment (COPRAS） is proposed. Considering the importance relation between different demand characteristic indexes in scheme design, the quantitative analysis of demand characteristic indexes is realized based on the fuzzy rough number. The COPRAS method is used to comprehensively evaluate all alternative design schemes, the priority ranking of all alternative design schemes is obtained according to the utility index to determine the optimal configuration scheme， and the selection of deformed spherical robots is taken as an example for evaluation and verification. The research results show that the method has good reliability and practicability, which provides effective reference for the configuration and selection of the robot design scheme in the future.

In the rehabilitation training process of lower limb rehabilitation robots, the existence of uncertain factors such as model parameters and environmental interference will affect the accuracy of trajectory tracking of the robot. To solve this problem, an adaptive compensation control based on the radial basis function (RBF) neural network is proposed. This control method can improve the accuracy of mechanical system trajectory tracking. Firstly, a closed chain horizontal lower limb rehabilitation robot structure with four working modes and stable movement is designed. Secondly, the Lagrange method is used to solve the kinetic nominal model, the uncertainty factors such as model parameters and external interference of the rehabilitation device are separated, and the adaptive compensation algorithm based on the RBF neural network is designed for the approximate control. Finally, the Matlab/Simulink environment is used to verify the effectiveness of the control strategy. The results show that, compared with the traditional fuzzy proportional integral derivative (PID) control method, the adaptive compensation algorithm based on the RBF neural network has a faster response speed and better tracking effect in human gait curve trajectory tracking. Moreover, the peak angle errors of the hip joint and the knee joint trajectory tracking are 0.08° and 0.13° respectively, which are much less than the rotation angle of patients' lower limbs in rehabilitation exercise. A single-leg prototype experiment is designed to show that the RBF compensation adaptive controller used in the study can achieve high precision tracking results and meet the safety requirements of patients in rehabilitation training.

A linear deployable gear and rack mechanism is introduced. In order to improve the maneuverability and obstacle-crossing performance of the spherical robot in different scenarios such as narrow space, complex terrain and bumpy road, a passive expanded single-degree of freedom mechanism is used to expand the expanded radius to improve the obstacle-crossing performance. A linear expanded rack-and-pinion mechanism is introduced to design a spherical robot with compact structure and multiple motion modes. Mathematical models are established to perform kinematic and dynamic analysis of spherical robot mechanisms, the simulation results of rolling and obstacle crossing are compared with the theoretical values to verify the correctness of kinematics and dynamics. On this basis, the prototype is made to further verify the feasibility of the mechanism, and verify that the mechanism has strong mobility and obstacle crossing ability.

Background: The recent discussion of introducing artificial intelligence (AI) knowledge to K-12 students, like many engineering and technology education topics, has attracted a wide range of stakeholders and resources for school curriculum development. While teachers often have to directly interact with external stakeholders out of the public schooling system, few studies have scrutinized their negotiation process, especially teachers' responses to external influences, in such complex environments. Purpose: Guided by an integrated theoretical framework of social constructionism, this research examined the process of how a teacher-initiated AI curriculum was constructed with external influences. The research focused on teachers' perspectives and responses in mediating external influences into local schools and classrooms. Methods: A 3-year ethnographic study was conducted in relation to an AI curriculum project among 23 Computer Science (CS) teachers from primary schools. Data collected from ethnographic observation, teacher interviews, and artifacts, were analyzed using open coding and triangulation rooted in the ethnographic, interpretivist approach. Results: Three sets of external influences were found salient for teachers' curriculum decisions, including the orientation of state-level educational policies, AI faculty at a partner university, and students' media and technology environments. The teachers' situational logics and strategic actions were reconstructed with thick descriptions to uncover how they navigated and negotiated the external influences to fulfill local challenges and expectations in classrooms and schools. Conclusions: The ethnographic study uncovered the dynamic and multifaceted negotiation involved in the collaborative curriculum development, and offers insights to inform policymaking, teacher education, and student support in engineering education.

Aiming at the problem of subjectivity and uncertainty in the configuration and selection of spherical robot design schemes in multi-demand unstructured environments, a scheme evaluation method coupled with fuzzy rough number and complex proportional assessment (COPRAS) is proposed. Considering the importance relationship between different demand characteristic indexes in scheme design, the quantitative analysis of demand characteristic indexes is realized based on fuzzy rough number. The COPRAS method is used to comprehensively evaluate all alternative design schemes, and the priority ranking of all alternative design schemes is obtained according to the utility index to determine the optimal configuration scheme. The selection of deformable spherical robot is taken as an example for evaluation and verification. The results show that the method has good reliability and practicability, which provides an effective reference for the configuration and selection of the robot design scheme in the future.

In order to improve the maneuverability and obstacle-crossing performance of the spherical robot in different scenarios such as narrow space, complex terrain and bumpy road, a passive deployable single-degree of freedom mechanism is used to expand the deployable radius to improve the obstacle-crossing performance. A linear deployable rack-and-pinion mechanism is introduced to design a spherical robot with compact structure and multiple motion modes. Mathematical models are established to perform kinematic and dynamic analysis of spherical robot mechanisms; the simulation results of rolling and obstacle crossing are compared with the theoretical values to verify the correctness of kinematics and dynamics. On this basis, the prototype is made to further verify the feasibility of the mechanism, and verify that the mechanism has strong mobility and obstacle crossing ability.

Artificial Intelligence (AI) is increasingly popular, and educators are paying increasing attention to it. For students, learning AI helps them better cope with emerging societal, technological, and environmental challenges. This theory of planned behavior (TPB)-based study developed a survey questionnaire to measure behavioral intention to learn AI (n = 682) among primary school students. The questionnaire was administered online, and it measured responses to five TPB factors. The five factors were (1) self-efficacy in learning AI, (2) AI readiness, (3) perceptions of the use of AI for social good, (4) AI literacy, and (5) behavioral intention. Exploratory factor analysis and a subsequent confirmatory factor analysis were used to validate this five-factor survey. Both analyses indicated satisfactory construct validity. A structural equation model (SEM) was constructed to elucidate the factors' influence on intention to learn AI. According to the SEM, all factors could predict intention to learn AI, whether directly or indirectly. This study provides new insights for researchers and instructors who are promoting AI education in schools.