Types Of Obstacles Research Articles

Study on laser ablation characteristics of tree obstacles in transmission lines

IntroductionAs a new and efficient method for obstacle removal, laser clearance has promising applications in eliminating tree obstacles in transmission lines. This paper aims to explore various aspects related to the laser ablation of tree obstacles in transmission lines through simulation and experimentation.MethodsThis paper employs simulation and experimentation to test the ignition time, burn-through time, peak temperature, and basic carbonization rate of various types of tree obstacles in transmission lines. Additionally, it analyzes the effects of tree density, laser power, clearance distance, and tree moisture content on laser ablation characteristics.ResultsThe results reveal that different types of tree obstacles exhibit distinct ablation characteristics. With increasing tree density, both the ignition and burn-through times increase, while the peak temperature and basic carbonization rate decrease. Additionally, laser power and clearance distance significantly influence laser ablation behavior. Higher laser energy density results in greater heat flux density, leading to shorter ignition and burn-through times and higher peak temperatures and carbonization rates. Moreover, the tree obstacle with the highest moisture content (58.4%) had the shortest burn-through time of 46.56 s, whereas the one with the lowest moisture content (14.8%) took the longest at 58.41 s, which demonstrates that increased moisture content enhances the laser ablation rate.DiscussionThese findings provide a basis for the application of laser-based tree obstacle removal in power transmission lines. The understanding of how different factors such as tree density, laser power, clearance distance, and moisture content affect the laser ablation process can help in optimizing the laser clearance operations for more efficient removal of tree obstacles in transmission lines.

Barrier-free tomato fruit selection and location based on optimized semantic segmentation and obstacle perception algorithm

With the advancement of computer vision technology, vision-based target perception has emerged as a predominant approach for harvesting robots to identify and locate fruits. However, little attention has been paid to the fact that fruits may be obscured by stems or other objects. In order to improve the vision detection ability of fruit harvesting robot, a fruit target selection and location approach considering obstacle perception was proposed. To enrich the dataset for tomato harvesting, synthetic data were generated by rendering a 3D simulated model of the tomato greenhouse environment, and automatically producing corresponding pixel-level semantic segmentation labels. An attention-based spatial-relationship feature extraction module (SFM) with lower computational complexity was designed to enhance the ability of semantic segmentation network DeepLab v3+ in accurately segmenting linear-structured obstructions such as stems and wires. An adaptive K-means clustering method was developed to distinguish individual instances of fruits. Furthermore, a barrier-free fruit selection algorithm that integrates information of obstacles and fruit instances was proposed to identify the closest and largest non-occluded fruit as the optimal picking target. The improved semantic segmentation network exhibited enhanced performance, achieving an accuracy of 96.75%. Notably, the Intersection-over-Union (IoU) of wire and stem classes was improved by 5.0% and 2.3%, respectively. Our target selection method demonstrated accurate identification of obstacle types (96.15%) and effectively excluding fruits obstructed by strongly resistant objects (86.67%). Compared to the fruit detection method without visual obstacle avoidance (Yolo v5), our approach exhibited an 18.9% increase in selection precision and a 1.3% reduction in location error. The improved semantic segmentation algorithm significantly increased the segmentation accuracy of linear-structured obstacles, and the obstacle perception algorithm effectively avoided occluded fruits. The proposed method demonstrated an appreciable ability in precisely selecting and locating barrier-free fruits within non-structural environments, especially avoiding fruits obscured by stems or wires. This approach provides a more reliable and practical solution for fruit selection and localization for harvesting robots, while also being applicable to other fruits and vegetables such as sweet peppers and kiwis.

Barriers influencing the sustainable development of community-based tourism (CBT): evidence from Ciaotou heritage village in Taiwan

PurposeIn recent years, community-based tourism (CBT) in the global market has been combined with specific themes in order to establish various tourism activities. Relatively few studies have comprehensively investigated how this type of CBT should be developed. Therefore, this study takes CBT with festival themes combined with community characteristics as a case study, which explores what obstacles exist in CBT with a combination of community characteristics – the industrial history of sugar factories and external themes, such as, formal flower displays.Design/methodology/approachThe thematic analysis is employed for data analysis of interviews. All interview participants were stakeholders.FindingsThis study identifies five types of obstacles: policy-related, environmental-related, innovation-related, industry-related and communication-related. Based on these findings, it is vital to consider both community and other characteristics of CBT. At the same time, after evaluating the rational perceptions of different stakeholders within the same type of obstacle, this study proposes a more specific and comprehensive development strategies for CBT.Originality/valueTo the best of the authors' knowledge, this is the first study of community agricultural tourism which collected the viewpoints of different stakeholders – tourists, residents, community associations, local businesses and the government. This study employs in-depth analysis of external policies and environmental and agricultural factors to discover and develop relevant coping strategies and implications.

Path Loss Analysis of ZigBee for Smart Meter Network Deployment in NAN

A fundamental and vital aspect of Smart Metering infrastructure is the communication technologies and techniques associated with it, especially between the Smart Meters and the Data Concentrator Unit. Among many available communication technologies, ZigBee provides a low-cost, low-power, and easy-to-deploy network solution for a Smart Meter network. There exists limited literature that discusses ZigBee as a potential communication technology for long-range networks. Hence thorough analysis is demanded on the suitability of ZigBee for smart meter deployment under different types of environmental conditions, coverage ranges, and obstacles. This work evaluates the performance of an extended ZigBee module in outdoor as well as indoor conditions in the presence of different types of obstacles. Parameters are obtained for path loss exponent and the standard deviation of the Gaussian Random variable to validate the Log Normal Shadowing model for modeling long-range ZigBee communication. The impact of obstacles on path loss is also considered. The results show that the Log Normal Shadowing model is a good approximation for the behavior of ZigBee path loss. Accordingly, the suitability of ZigBee for a Smart Meter network spanned as a Neighborhood Area Network is also assessed based on the approximated model.

Dynamic Scene Representation for Docking in Urban Waters Using a Stereo Camera

Abstract To navigate and dock safely in urban waters, an autonomous ferry must assess its safe navigation paths by identifying navigable free space and potential obstacles. A deep understanding of its surroundings is crucial for situational awareness. Knowing which objects are static and dynamic is an indispensable ingredient for path planning algorithms and collision avoidance systems, and is especially useful for docking operations, as moving obstacles can interfere with the optimal path to the dock. To handle all types of obstacles, we present a novel dynamic scene representation for docking in urban waters using a short baseline stereo camera. Obstacles protruding from the water surface are represented with vertically oriented rectangles, known as stixels. The stixels are tracked over consecutive frames with dense optical flow. Together with stereo correspondences, their 3D motion is estimated. Stixels representing the same object are grouped together into line segments which are classified as either dynamic or static based on the motion of its associated stixels. Our dynamic representation presents objects in the 3D camera coordinate system as a bird’s eye view map. Additionally, stereo depth uncertainty can significantly impact depth estimates and is considered using a Kalman filter, providing smooth position and velocity estimates. We test our dynamic scene representation on a real docking scenario recorded with an autonomous ferry using a stereo camera. We evaluate the accuracy of the reconstructed 3D scene points and the velocity estimates of the dynamic objects using LiDAR and GNSS.

Real-time deep learning-based position control of a mobile robot

This study uses PID (Proportional-Integral-Derivative), fuzzy logic, and deep learning algorithm to experimentally achieve real-time position control of a four-wheel-drive symmetric autonomous mobile robot whose design and prototype are realized. At the same time, the convolutional neural network-based YOLO (You only look once) algorithm is used to detect and get around obstacles by classifying the items in front of it during the robot's movement, to get the robot to reach the position specified as a reference a fuzzy logic controller is created. As the robot can be used outside, It is necessary to recognize more than one type of obstacle. For this reason, YOLO training is conducted to classify eighty objects that the robot may encounter in the external environment, such as cats, dogs, people, and chairs. In this research, a single obstacle is studied and it is determined as a human class obstacle, which is one of the obstacles that the robot is most likely to encounter in the laboratory environment. While the target location points are sent to the robot with the designed Android application, all the controls needed by the robot are carried out by the microcontrollers on it, independent of any computer. As a result of the experimental studies, it is seen that people are detected in real time with YOLO within the range of 90 cm–130 cm specified in the algorithm, without any problems. In addition, the mobile robot reached the target points sent to it from the Android application without any errors by overcoming obstacles within the specified approach distance (0.05 m or 0.1 m). While the maximum mean absolute error in the speed controls made on the motors throughout all experimental studies is 0.351 rpm, the Robot moved with a maximum absolute linear speed error of 2.8 mm/s. This shows that the robot is successfully controlled with fuzzy and PID controllers in line with the information obtained with YOLO.

Design and Development of a Side Spray Device for UAVs to Improve Spray Coverage in Obstacle Neighborhoods

Electric multirotor plant protection unmanned aerial vehicles (UAVs) are widely used in China for efficient and precise plant protection at low altitude for low volumes. Unstructured farmland in China has various types of obstacles, and UAVs usually use a detour path to avoid obstacles due to flight altitude limitations. However, existing UAV spray systems do not spray when in obstacle neighborhoods during obstacle avoidance, resulting in insufficient droplet coverage and reduced plant protection quality in the area. To improve the droplet coverage in obstacle neighborhoods, this article carries out a study of side spray technology with an electric quadrotor UAV, and proposes the design and development of a side spray device. The relationship between the obstacle avoidance path of the UAV and the spray pattern of the side spray device and their effect on droplet coverage in obstacle neighborhoods was explored. An accurate measurement method of the relative position between the UAV and obstacles was proposed. Spray angle calculations and nozzle selection for the side spray device were carried out in conjunction with the relative position. A rotor wind field simulation model was designed based on the lattice Boltzmann method (LBM), and the spatial layout of the side spray device on the UAV was designed based on the simulation results. To explore suitable spray patterns for the side spray device, comparative experiments of droplet coverage in obstacle neighborhoods were carried out under different environments, spray patterns, and flight parameter combinations. The relationship between the flight parameter combinations and the distribution uniformity of droplets and the effective swath width of the side spray device was explored. The experimental results were analyzed by an analysis of variance (ANOVA) and a relationship model was obtained. The results showed that the side spray device can effectively improve droplet coverage in obstacle neighborhoods compared to a device without side spray using the same flight parameter combinations. The effective swath width in obstacle neighborhoods can be increased by a minimum of 6.35%, maximum of 35.32%, and average of 15.25% using the side spray device. The error between the predicted values of the relational model and the field experiment results was less than 15%. The results verify the effectiveness and rationality of the method proposed in this article. This study can provide technical and theoretical references for improving the plant protection quality of UAVs in obstacle environments.

Field Obstacle Detection and Location Method Based on Binocular Vision

When uncrewed agricultural machinery performs autonomous operations in the field, it inevitably encounters obstacles such as persons, livestock, poles, and stones. Therefore, accurate recognition of obstacles in the field environment is an essential function. To ensure the safety and enhance the operational efficiency of autonomous farming equipment, this study proposes an improved YOLOv8-based field obstacle detection model, leveraging depth information obtained from binocular cameras for precise obstacle localization. The improved model incorporates the Large Separable Kernel Attention (LSKA) module to enhance the extraction of field obstacle features. Additionally, the use of a Poly Kernel Inception (PKI) Block reduces model size while improving obstacle detection across various scales. An auxiliary detection head is also added to improve accuracy. Combining the improved model with binocular cameras allows for the detection of obstacles and their three-dimensional coordinates. Experimental results demonstrate that the improved model achieves a mean average precision (mAP) of 91.8%, representing a 3.4% improvement over the original model, while reducing floating-point operations to 7.9 G (Giga). The improved model exhibits significant advantages compared to other algorithms. In localization accuracy tests, the maximum average error and relative error in the 2–10 m range for the distance between the camera and five types of obstacles were 0.16 m and 2.26%. These findings confirm that the designed model meets the requirements for obstacle detection and localization in field environments.

Boundary optimal control problems for parabolic variational inequalities of bilateral obstacle type

This paper is concerned with the optimality system for an optimal control problem governed by parabolic bilateral obstacle problems with Robin boundary conditions. The control-state dynamics is described by variational inequalities and the control on the boundary is considered. Our analysis relies on a regularization method of approximating the variational inequality by a series of semilinear PDEs. The Lipschitz continuity and directionally differentiability in the weak sense for the control-to-state mapping are proved. Then, the necessary optimality conditions for approximate optimal control problems are derived. Finally, the first-order optimality system including complementarity conditions for the original problem is established by a limiting procedure. The result is carefully compared with those obtained in existing literature.

Injury severity analysis of e-bike riders in China based on the in-vehicle recording video crash data: a random parameter ordered logit model

This study investigates the impacts of various factors on e-bike riders’ injury severity in crashes with motor vehicles, based on the in-vehicle recording video crash data in China. Variables from human factors, vehicle characteristics, road conditions, and environmental attributes are extracted from the video, especially for drivers and riders’ illegal and avoidance behaviour before the crash, and sun shade canopy use. Results of mixed logit models reveal that drivers’ speeding, running red lights, slow-down and swerve behaviour, light trucks, heavy trucks, and buses have significantly varied impacts on riders’ injury. Moreover, both drivers and riders’ illegal behaviour leads to an increased injury, while their avoidance behaviour before crashes can protect riders. In addition, types of visual obstacles, accidents occurring at night, large vehicles’ involvement, and the application of sunshade canopies by riders increased the probability of severe injury, while helmet use can protect riders in accidents with motor vehicles.

Lane Line and Object Detection Using Yolo v3

In the contemporary age, creating autonomous vehi- cles is a crucial starting point for the advancement of intelligent transportation systems that rely on sophisticated telecommu- nications network infrastructure, including the upcoming 6g networks. The paper discusses two significant issues, namely, lane detection and obstacle detection (such as road signs, traffic lights, vehicles ahead, etc.) using image processing algorithms. To address issues like low accuracy in traditional image processing methods and slow real-time performance of deep learning-based methods, barriers for smart traffic lane and object detection algorithms are proposed. We initially rectify the distorted image resulting from the camera and then employ a threshold algorithm for the lane detection algorithm. The image is obtained by extracting a specific region of interest and applying an inverse perspective transform to obtain a top-down view. Finally, we apply the sliding window technique to identify pixels that belong to each lane and modify it to fit a quadratic equation. The Yolo algorithm is well-suited for identifying various types of obstacles, making it a valuable tool for solving identification problems. Finally, we utilize real-time videos and a straightforward dataset to conduct simulations for the proposed algorithm. The simula- tion outcomes indicate that the accuracy of the proposed method for lane detection is 97.91% and the processing time is 0.0021 seconds. The proposal for detecting obstacles has an accuracy rate of 81.90% and takes only 0.022 seconds to process. Compared to the conventional image processing technique, the proposed method achieves an average accuracy of 89.90% and execution time of 0.024 seconds, demonstrating a robust capability against noise. The findings demonstrate that the suggested algorithm can be implemented in self-driving car systems, allowing for efficient and fast processing of the advanced network.

Studies on in-flight aircraft rerouting

With the publication of this article, the authors continue the research on the development and testing of a methodology for in-flight aircraft rerouting which have begun in previously published articles in the Civil Aviation High Technologies of the Moscow State Technical University of Civil Aviation. This article presents the results of the study in terms of developing the potentials of the methodology from reconfiguring a route of a hypothetical aircraft and obstacles in the horizontal plane, which were previously demonstrated, prior to rerouting in both the horizontal and vertical planes for two different types of obstacles: 1) ground natural or artificial (mountain, power line support, etc.); 2) air (squall line, prohibited flight area, etc.) and their combinations using an example of a Mi-8 helicopter flight on route using a real digital map of the terrain. As mentioned above, a large amount of aviation accidents is associated with the loss of control in-flight, as well as the collision with terrain in a controlled flight (categories LOC-I, CFIT, LALT). As a result of the investigation of the aviation accidents, it was found that these accidents are often caused by the requirement to reroute quickly due to obstacles, for example, a squall line. When determining alternatives to avoid an obstacle, as well as while implementing the selected route for avoiding action, the crew makes errors due to the increased psychophysiological load and lack of time. The methodology and the algorithms, proposed by the authors, make it possible to assess the safety of an original route, estimate options for alternatives to avoid around obstacles detected in-flight, check them for feasibility, taking into account aircraft performance, flight envelope, and also select the optimal route from the view of some criterion, for example, based on minimizing the route length increase, reducing additional fuel consumption, the time required to implement a new route of flight, etc.

Balance recovery after trips is affected by the type of tripping obstacles

Occupational falls are often initiated by trips. Mechanical perturbations applied onto the tripped foot are different for different types of tripping obstacles. The present study aimed to determine how different types of tripping obstacles affect balance recovery after trips. Sixty-four healthy adults participated in an experimental study. They were instructed to perform several walking trials, during which two trips were randomly induced, one by a pole-like obstacle and the other by a board-like obstacle. Balance recovery after trips was measured and compared between the two obstacles. Results showed that the board-like obstacle led to longer step-off time, shorter recovery step duration, and smaller minimum hip height, suggesting that the risk of trip-initiated falls could be higher with the board-like obstacle vs. the pole-like obstacle. This finding presents the need for future research to consider the influence of obstacle type when exploring mechanisms for trips and falls.

ANALISA KINERJA LALU LINTAS JALAN JENDERAL AHMAD YANI KOTA PALEMBANG

Traffic performance refers to the evaluation of the service quality of a road segment relative to the traffic flow through it. Road traffic performance assessment is measured by the degree of saturation and travel speed. Road capacity (C) is the ability of a road section to accommodate traffic flow in a certain time unit. Road capacity is influenced by the type of side obstacles and the frequency of occurrence. This research was conducted on Jalan Jenderal Ahmad Yani Campus A, Muhammadiyah University of Palembang to calculate road capacity (C) and traffic performance on Jalan Jenderal Ahmad Yani. Data analysis was carried out using the 2023 Indonesian Road Capacity Guide where road capacity and degree of saturation are road traffic performance parameters so that the value of the level of service is known. Based on the results of research and analysis that has been carried out located on Jalan Jenderal Ahmad Yani, Palembang, the road capacity (C) is 2,924 PCU/hour and the free flow speed (VB) value is 49.59 Km/hour, while the road traffic performance and level of service on the Jalan Simpang 4 Jakabaring-Jalan Jenderal Ahmad Yani section is 0.81 and is classified as category D (Flow is close to stable, speed can still be controlled, traffic volume is approaching capacity), while from the direction of Jalan Jenderal Ahmad Yani -Intersection 4 Jakabaring obtained a service level of 0.94 and was classified as category E (Unstable flow and sometimes stopping, traffic volume approaching capacity, low speed and changing).

Effects of AR-HMD Interactive Interface Layout on Usability and Obstacle Avoidance in Subway Overhauls

The maintenance and overhaul of rail transit vehicle frames are vital for ensuring the safe and reliable operation of equipment. AR-based maintenance has been proven to be cost-reducing and effective. Few studies explored the application of AR-based maintenance on subway overhaul. In this study, 74 participants were selected to conduct the underground maintenance AR recognition and obstacle avoidance experiment. And two-factor ANOVA, independent samples t-test, and non-parametric test were used for data statistics. The results show that a high-opacity AR interface has better recognition but affects obstacle avoidance. Facing different types of obstacles, AR interfaces with different layouts have significant differences in terms of recognizability and obstacle avoidance effects. Compared with the list layout, the matrix layout AR interface will make more effort for workers. The findings provide useful references for designing the visual information layout of AR interfaces and conducting usability testing of AR interfaces in complex scenarios such as underground maintenance. Highlights This study assessed the effects of the AR-HMD interface on workers’ recognition of and ability to safely avoid obstacles in a subway maintenance environment. A high-opacity AR interface leads to better recognition but affects obstacle avoidance. List AR interfaces lead to better recognition but increase the probability of obstacle avoidance failure by 50% under block obstacles. For linear obstacles, layout has no significant effect on recognition or obstacle avoidance. A matrix layout AR interface is more effortful than the list layout AR for the workers.

The coupled-motion enhanced wireless signal transmission with long distance based on Maxwell’s displacement current

Wireless signal transmission plays an increasingly imperative role in numerous aspects of modern society, but it still remains challenging to achieve it with low-cost and efficient way. Herein, we demonstrate a long-distance wireless signal transmission system, which mainly includes an electrodeless triboelectric nanogenerator coupled with linear motion and rotational motion (LR-TENG) as a transmitter and a receiver located at a distance. Based on the varying electric field originated from Maxwell's displacement current, the maximum transmission distance of LR-TENG can reach 86 cm under the external excitation of 1 Hz, creating the highest record among the relevant researches. In addition, the influence of obstacle type, thickness, size and position on signal transmission has been systematically investigated for the first time, and the distribution of time-varying electric field in space is also analyzed qualitatively. Furthermore, a Labview interface is developed for accurate positioning in complex scenes relying on the received signals from multiple receivers at different positions. This work illustrates a simple and feasible design method for extending the distance of wireless signal transmission based on TENG, and promotes its application in the field of wireless communication.

Vision-based planting position selection system for an unmanned reforestation machine

Abstract Research on automated seedling planting systems in forestry is a crucial aspect of forestry automation. This paper introduces the development of a vision-based automated seedling planting position selection system, integrated with hardware and software components on an unmanned forest machine platform. Developed around object detection as the core, this research presents a comprehensive system consisting of two main functionalities: (i) A vision system that performs obstacle detection and localization, providing estimated obstacle types, sizes, and positions to the plant planner function. (ii) A plant planner function utilizes this information to plan the plantable areas and selects suitable planting locations. The integrated system has been tested in the field and we found it to effectively determine suitable planting locations on the ground of a clear-cut. The implementation of this system lays the foundation for subsequent automated planting operations. Furthermore, the automation of forest seedling planting reduces the need for manual labor and enhances planting precision, contributing to improved forest health and ecological balance. Looking ahead, this research offers insights into the future development of unmanned forestry operations, making strides in automating forest management, achieving cost-effectiveness, and facilitating ecological restoration.

Identification of Children with Learning Barriers (Dysgraphia) and Treatment in Elementary School

Background: Education in elementary schools certainly has the aim of properly realizing the function of national education. However, without realizing it, in carrying out national functions certain obstacles come from both students and teachers. One of the obstacles that is often encountered is dysgraphia. Where dysgraphia is a difficulty experienced by children in reading due to poor writing and spelling in children. Therefore, researchers in this study have the aim of identifying learning barriers and handling them in one of the elementary schools in Sukoharjo Regency. This research uses a qualitative approach. The research participants were guidance and counseling teachers, class teachers, and students. Data collection techniques include observation, interviews, and, documentation. The analysis technique used is interactive data analysis through data reduction, data presentation, and data conclusions. The results of the research show that children with learning disabilities have types of dyslexia dysgraphia, motor dysgraphia, and spatial dysgraphia. Therefore, children with this type of digraphia obstacle can be overcome by using strategies used by class teachers and guidance and counseling teachers in various ways, namely by providing motivation to students, providing interesting media, writing pre-school activities, tracing letters, writing block letters and practicing in writing cursive letters.

Research on mobile robot path planning in complex environment based on DRQN algorithm

A deep reinforcement Q learning algorithm (DRQN) based on radial neural network is proposed to achieve path planning and obstacle avoidance for mobile robots in complex ground environments with different types of obstacles, including static and dynamic obstacles. Firstly, the path planning problem is represented as a partially-observed Markov decision process. Steering angle, running characteristics, and other elements are introduced into the state-action decision space and the greedy factor is dynamically adjusted using a simulated annealing algorithm, which improves the mobile robot’s environment exploration and action selection accuracy. Secondly, the Q-learning algorithm is improved by replacing the Q-table structure with an RBF neural network to enhance the approximation ability of the algorithm’s function values, and the parameters of the implicit layer and the weights between the implicit and the output layer are trained using the dynamic clustering and least-mean methods respectively, which improves the convergence speed and enhances the ability of mobile robots to handle large-scale computation. Lastly, the double reward mechanism is set up to prevent the mobile robot from blind searching in unknown environments, which enhances the learning ability and improves path planning safety and flexibility at the same time. Different types of scenarios are set up for simulation experiments, and the results verified the superiority of the DQRN algorithm. Taking the 30 * 30 complex scene as an example, using the DQRN algorithm for path planning reduces the values of distance, turning angle, and planning time by 27.04%, 7.76%, and 28.05%, respectively, compared to the average values of Q-learning, optimized Q-learning, deep Q-learning, and DDPG algorithms, which can effectively improve the path planning efficiency for mobile robots in complex environments.

How High School Students Solve Proportional Reasoning Problem?

This research is motivated by the phenomenon of students' difficulties in solving proportional reasoning problems and the lack of proportional reasoning research at the high school level. This research aims to describe in depth the ability of high school students to solve proportional reasoning problems and learning obstacles that may occur. The method used is qualitative with a case study approach. The research subjects involved were 5 students from grades 10 to 12. The instruments used were tests and interview guides. The research results showed that there were 4 mistakes made by students in solving proportional reasoning problems, namely (1) Using the additional strategy in solving proportional reasoning problems; (2) Not being able to solve problems of indirect proportion; (3) Using multiplication strategies in solving non-proportional problems; and (4) Not being able to compare ratios. Concerning learning obstacles, there are 2 types of learning obstacles that are indicated to be experienced by students, namely epistemological obstacles and ontogenic obstacles. The results of this research can be used as a basis for carrying out further research in the form of research that designs learning flows and/or learning designs that can develop students' proportional reasoning abilities.