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The purpose of this paper is to investigate the research obstacles facing Zarqa University (ZU) faculty members, and the ways of overcoming them according to their points of view.

The study attempted to investigate the research obstacles facing ZU faculty members and how to overcome them according to the points of view among the staff. This was carried out through answering four key research questions: What are the research obstacles facing ZU faculty members according to their points of view?; Are there any statistical differences between the trends among ZU faculty members regarding research obstacles attributed to the type of faculty (scientific or humanity), gender and academic rank?; What are the ways of overcoming research obstacles facing ZU faculty members, from their points of view?; and Are there any statistical differences between the points of view among ZU faculty members regarding the ways of overcoming research obstacles attributed to the type of faculty (scientific or humanity), gender and academic ranks? The principal research tool used was a questionnaire, designed and developed by the researcher and based on his personal academic experience, intuitive hints gathered from discussions with ZU faculty members, related literature, and consultation with academic referees specialising in library and information science. The questionnaire was modified where necessary. The final version included a set of questions on the research obstacles facing ZU faculty members and the ways of overcoming research obstacles according to their points of view. In order to verify the reliability of the study, the initial questionnaire was reviewed by four referees to solicit their opinions on clarity and the extent to which each phrase met the study objectives: two university professors and two university associate professors. The researcher also applied Cronbach alpha (α) to the responses using a statistical package (SPSS), and the stability coefficient of the tool was 0.631, acceptable for the educational purposes of the study.

The study revealed that the means of the respondents' trends towards the ways of overcoming research obstacles were higher than the means of the trends towards research obstacles. The means of the responses towards the ways of overcoming research obstacles ranged from 2.91 to 4.49, while the means of the responses towards research obstacles ranged from 1.79 to 4.01. It also revealed that in spite of ZU faculty members having good motivation as well as research and statistical skills, as demonstrated in their responses, the financial and moral support required were the main problems. Furthermore, there were no significant differences between the trends among faculty members towards research obstacles and the ways of overcoming them attributed to dependent variables, such as type of faculty (scientific or humanity), academic ranks and gender.

The study limitation was the trends among ZU faculty members, who were in the first semester of the academic year 2008/2009.

The importance of the study comes from its results by finding out the trends among ZU faculty members towards the research obstacles at the university and ways of overcoming them. It is an important tool to inform ZU's administrators of the study results in order to try to avoid research obstacles and support the ways of overcoming them whilst encouraging faculty members to perform more research in different fields.

This paper aims to investigate an autonomous obstacle-surmounting method based on a hybrid gait for the problem of crossing low-height obstacles autonomously by a six wheel-legged robot. The autonomy of obstacle-surmounting is reflected in obstacle recognition based on multi-frame point cloud fusion.

In this paper, first, for the problem that the lidar on the robot cannot scan the point cloud of low-height obstacles, the lidar is driven to rotate by a 2D turntable to obtain the point cloud of low-height obstacles under the robot. Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping algorithm, fast ground segmentation algorithm and Euclidean clustering algorithm are used to recognize the point cloud of low-height obstacles and obtain low-height obstacle in-formation. Then, combined with the structural characteristics of the robot, the obstacle-surmounting action planning is carried out for two types of obstacle scenes. A segmented approach is used for action planning. Gait units are designed to describe each segment of the action. A gait matrix is used to describe the overall action. The paper also analyzes the stability and surmounting capability of the robot’s key pose and determines the robot’s surmounting capability and the value scheme of the surmounting control variables.

The experimental verification is carried out on the robot laboratory platform (BIT-6NAZA). The obstacle recognition method can accurately detect low-height obstacles. The robot can maintain a smooth posture to cross low-height obstacles, which verifies the feasibility of the adaptive obstacle-surmounting method.

The study can provide the theory and engineering foundation for the environmental perception of the unmanned platform. It provides environmental information to support follow-up work, for example, on the planning of obstacles and obstacles.

To solve the obstacle detection problem in robot autonomous obstacle negotiation, this paper aims to propose an obstacle detection system based on elevation maps for three types of obstacles: positive obstacles, negative obstacles and trench obstacles.

The system framework includes mapping, ground segmentation, obstacle clustering and obstacle recognition. The positive obstacle detection is realized by calculating its minimum rectangle bounding boxes, which includes convex hull calculation, minimum area rectangle calculation and bounding box generation. The detection of negative obstacles and trench obstacles is implemented on the basis of information absence in the map, including obstacles discovery method and type confirmation method.

The obstacle detection system has been thoroughly tested in various environments. In the outdoor experiment, with an average speed of 22.2 ms, the system successfully detected obstacles with a 95% success rate, indicating the effectiveness of the detection algorithm. Moreover, the system’s error range for obstacle detection falls between 4% and 6.6%, meeting the necessary requirements for obstacle negotiation in the next stage.

This paper studies how to solve the obstacle detection problem when the robot obstacle negotiation.

Imitation learning is a powerful tool for planning the trajectory of robotic end-effectors in Cartesian space. Present methods can adapt the trajectory to the obstacle; however, the solutions may not always satisfy users, whereas it is hard for a nonexpert user to teach the robot to avoid obstacles in time as he/she wishes through demonstrations. This paper aims to address the above problem by proposing an approach that combines human supervision with the kernelized movement primitives (KMP) model.

This approach first extracts the reference database used to train KMP from demonstrations by using Gaussian mixture model and Gaussian mixture regression. Subsequently, KMP is used to modulate the trajectory of robotic end-effectors in real time based on feedback from its interaction with humans to avoid obstacles, which benefits from a novel reference database update strategy. The user can test different obstacle avoidance trajectories in the current task until a satisfactory solution is found.

Experiments performed with the KUKA cobot for obstacle avoidance show that this approach can adapt the trajectories of the robotic end-effector to the user’s wishes in real time, including trajectories that the robot has already passed and has not yet passed. Simulation comparisons also show that it exhibits better performance than KMP with the original reference database update strategy.

An interactive learning approach based on KMP is proposed and verified, which not only enables users to plan the trajectory of robotic end-effectors for obstacle avoidance more conveniently and efficiently but also provides an effective idea for accomplishing interactive learning tasks under constraints.

This study investigates how perceived political corruption, a generally overlooked corruption type, relates to firms' new product development (NPD) through perceived regulatory obstacles. It also examines firms' perceptions of business association support in this relationship, considering these associations' potential support for NPD.

This study conducted an empirical analysis of 1,663 firms in Turkey, a country noted for a history of legislative corruption, and in which there are strong business associations. Drawing the data from the World Bank's 2019 Enterprise Surveys Dataset, this study tested the hypotheses via the two-stage factor score regression method.

This study finds that perceived political corruption significantly relates to NPD negatively through perceived regulatory obstacles. It also finds that the perceived support of business associations to NPD is significantly greater when firms perceive regulatory obstacles but only slight political corruption.

As far as political corruption is concerned, this study reveals that corruption can also be the cause of regulatory obstacles, expanding the common view of corruption as a means of overcoming regulatory obstacles to NPD. In addition, it introduces the role of business associations in this relationship by revealing their support to NPD for different levels of perceived political corruption and regulatory obstacles.

The purpose of this study is to investigate the obstacles to mechatronics adoption in the construction industry from a Nigerian perspective. It aims to fill the knowledge gap by focusing on the specific challenges faced in developing countries, considering the unique contexts and constraints of the Nigerian construction industry.

The study used a comprehensive literature review to identify 26 obstacles to mechatronics adoption. These obstacles were used to develop a well-structured questionnaire, which was then distributed to construction professionals using Google Forms through purposive and snowball sampling techniques. The rankings obtained from the questionnaire responses were analyzed to determine the most significant obstacles.

The study revealed the top five most significant obstacles to mechatronics adoption in the Nigerian construction industry. These obstacles include high costs of operation and maintenance, resistance to adopting new technologies, a lack of standardized protocols, insufficient maintenance capabilities and a lack of government support. Factor analysis revealed five clusters of obstacles: technological-related factors, economic-related factors, capability-related factors, government-related factors and awareness-related factors.

Findings from this study have the potential to inform decision-making, drive policy changes and guide future research efforts aimed at promoting the widespread adoption of mechatronics technologies, ultimately leading to the transformation and improvement of the construction industry as a whole.

This study contributes to the field of mechatronics adoption in the construction industry by addressing the gap in research specific to developing countries such as Nigeria. By identifying and analyzing the obstacles from a Nigerian perspective, the study offers unique insights and original findings.

This paper aims to use redundant manipulators to solve the challenge of collision avoidance in construction operations such as welding and painting.

In this paper, a null-space-based task-priority adjustment approach is developed to avoid collisions. The method establishes the relative position of the obstacle and the robot arm by defining the “link space,” and then the priority of the collision avoidance task and the end-effector task is adjusted according to the relative position by introducing the null space task conversion factors.

Numerical simulations demonstrate that the proposed method can realize collision-free maneuvers for redundant manipulators and guarantee the tracking precision of the end-effector task. The experimental results show that the method can avoid dynamic obstacles in redundant manipulator welding tasks.

A new formula for task priority adjustment for collision avoidance of redundant manipulators is proposed, and the original task tracking accuracy is guaranteed under the premise of safety.

The purpose of this paper is to solve the problem that mobile robots are still based on reactive collision avoidance in unknown dynamic environments leading to a lack of interaction with obstacles and limiting the comprehensive performance of mobile robots. A dynamic window approach with multiple interaction strategies (DWA-MIS) is proposed to solve this problem.

The algorithm firstly classifies the moving obstacle movement intention, based on which a rule function is designed to incorporate positive incentives to motivate the robot to make correct avoidance actions. Then, the evaluation mechanism is improved by considering the time cost and future information of the environment to increase the motion states. Finally, the optimal objective function is designed based on genetic algorithm to adapt to different environments with time-varying multiparameter optimization.

Faced with obstacles in different states, the mobile robot can choose a suitable interaction strategy, which solves the limitations of the original DWA evaluation function and avoids the defects of reactive collision avoidance. Simulation results show that the algorithm can efficiently adapt to unknown dynamic environments, has less path length and iterations and has a high comprehensive performance.

A DWA-MIS is proposed, which increases the interaction capability between mobile robots and obstacles by improving the evaluation function mechanism and broadens the navigation strategy of DWA at a lower computational cost. After real machine verification, the algorithm has a high comprehensive performance based on real environment and provides a new idea for local path planning methods.

This paper aims to investigate the effect of partially blocking the cathode channel with the stair arrangement of obstacles on the performance of a proton exchange membrane fuel cell.

A numerical study is conducted by developing a three-dimensional computational fluid dynamics model.

As the angle of the stair arrangement increases, the performance of the fuel cell is reduced and the pressure drop is decreased. The use of four stair obstacles with an angle of 0.17° leads to higher power density and a lower pressure drop compared to the case with three rectangular obstacles of the same size and maximum height. The use of four stair obstacles with an angle of 0.34° results in higher power density and lower pressure drop compared to the case with two rectangular obstacles of the same size and maximum height.

Using the stair arrangement of obstacles as an innovation of the present work, in addition to improving the fuel cell’s performance, creates a lower pressure drop than the simple arrangement of obstacles.

The goal of this research is to develop a dynamic step path planning algorithm based on the rapidly exploring random tree (RRT) algorithm that combines Q-learning with the Gaussian distribution of obstacles. A route for autonomous vehicles may be swiftly created using this algorithm.

The path planning issue is divided into three key steps by the authors. First, the tree expansion is sped up by the dynamic step size using a combination of Q-learning and the Gaussian distribution of obstacles. The invalid nodes are then removed from the initially created pathways using bidirectional pruning. B-splines are then employed to smooth the predicted pathways.

The algorithm is validated using simulations on straight and curved highways, respectively. The results show that the approach can provide a smooth, safe route that complies with vehicle motion laws.

An improved RRT algorithm based on Q-learning and obstacle Gaussian distribution (QGD-RRT) is proposed for the path planning of self-driving vehicles. Unlike previous methods, the authors use Q-learning to steer the tree's development direction. After that, the step size is dynamically altered following the density of the obstacle distribution to produce the initial path rapidly and cut down on planning time even further. In the aim to provide a smooth and secure path that complies with the vehicle kinematic and dynamical restrictions, the path is lastly optimized using an enhanced bidirectional pruning technique.