Robot Competition Research Articles

Basketball robot object detection and distance measurement based on ROS and IBN-YOLOv5s algorithms.

With the combination of artificial intelligence and robotics technology, more and more professional robots are entering the public eye. Basketball robot competition, as a very good target system for autonomous robot research, is very suitable for conducting research on robot autonomous perception system object detection. However, traditional basketball robots have problems such as recognition difficulties, which seriously affect the recognition of robot targets and distance measurement based on recognition. To improve the performance of basketball robots in competitions, research was conducted to improve the object detection system. Firstly, a basketball robot object detection system based on robot operating system was designed. In the software layer of the object detection system, an algorithm that combines YOLOv5s and laser detection was used, and an appropriate instance batch normalization network module was introduced in the YOLOv5s algorithm to improve the model's generalization ability. The experiment outcomes indicated that the improved algorithm had intersection over union (IoU), structural information loss, ambiguity and signal-to-noise ratio of 0.96, 0.03, 0.13, and 0.98, respectively, and performed the best in the other comparison models. The recall curve area and F1 value of the improved algorithm were 0.95 and 0.9789, respectively. In the detection of basketball, volleyball, and calibration columns, the average classification accuracy of the improved model was 95.87%, and the average calibration box accuracy was 97.05%. From this, the algorithm proposed in the study has robust performance and can efficiently achieve object detection and recognition of basketball robots. The improved algorithm proposed in the study provides more reliable and rich information for the perception ability of basketball robots, as well as for their subsequent decision-making and action planning, thereby improving the overall technical level of the robots.

A Visualized Exploration of Educational Robotics Over Five Decades

With the swift development of information technologies, educational robotics has been widely used in education, generally leading to improved educational outcomes. While educational robotics has gained popularity and achieved success in education, few researchers have conducted bibliographic analyses in this field. This study complements this missing link by visualizing the top ten authors, organizations, countries, keywords, authors, and sources via VOSviewer. The clustering techniques in CitNetExplorer were used to cluster the publications, where citation networks and qualitative analyses were developed to discuss the topics including the learning outcomes, social and interactive skills, working memory, and higher-order skills in the educational robotics-based context. This study concludes that effective educational robotics can exert a positive influence on the learning outcomes and robotics can improve the development of proximal zones in human brains, conducive to the learning outcomes. Future researchers may focus their attention on robotic competitions, affective features, and evaluative and analytical frameworks to improve educational robotics-based educational effectiveness.

A Target Collision Algorithm of Autonomous Mobile Robot for Two-player Competition

Abstract A variety of robot competitions are held to train and attract students everywhere. This paper studies the algorithm of autonomous robot with two-player competition. The autonomous robots in a two-player competition takes the scoring form of collision and wins first. The impact object is the enemy target. The key algorithms in a two-player competition are target detection, target path decision, and collision target motion control. First, the vehicle target data set was established and enhanced, the lightweight detection algorithm model was screened and trained, the model pruning optimization experiment and the model deployment optimization experiment were carried out, and then the TensorRT framework was used for inter-layer fusion and data quantification processing. After hitting the target path decision algorithm after detecting the target target, it finally integrates the decision in simple and complex situations. The collision target motion control is the scheme of more intelligent and self-adjustable parameters of fuzzy Radial Basis Function (RBF) network Proportional Integral Derivative (PID) controller. Finally, the collision target implementation algorithm is tested in a real environment, and the results show that the task requirements can be met.

The role of underwater robotics competitions in shaping postgraduate research and industry

The role of underwater robotics competitions in shaping postgraduate research and industry

Towards accessible robot-assisted physical play for children with physical disabilities

Abstract MyJay is an open-source robot designed to facilitate play between children with and without physical disabilities. The robot acts as a proxy for children with upper limb challenges, allowing them to participate in physical games with their peers. Our design was inspired by the FIRST Robotics Competition, which involves teleoperating robots to manipulate objects. Taking a user-centred perspective, we consulted therapists and conducted remote interviews with children with disabilities and their guardians at various stages of the design process. We then conducted an in-person feasibility study with 18 typically developing children in a school setting. The study involved children teleoperating the robot to pick up and throw balls into a designated goal, and the interaction was evaluated using the user experience questionnaire and the Robotic Social Attributes Scale. The results of the study show great potential for MyJay to act as a play mediator in various scenarios, and the response from the children was positive. The ultimate aim of our research agenda is to pave the way towards creating more inclusive play environments through robot-mediated interactions, breaking barriers posed by physical limitations.

Applications of a vacuum-actuated multi-material hybrid soft gripper: lessons learnt from RoboSoft manipulation challenge.

Soft grippers are garnering increasing attention for their adeptness in conforming to diverse objects, particularly delicate items, without warranting precise force control. This attribute proves especially beneficial in unstructured environments and dynamic tasks such as food handling. Human hands, owing to their elevated dexterity and precise motor control, exhibit the ability to delicately manipulate complex food items, such as small or fragile objects, by dynamically adjusting their grasping configurations. Furthermore, with their rich sensory receptors and hand-eye coordination that provide valuable information involving the texture and form factor, real-time adjustments to avoid damage or spill during food handling appear seamless. Despite numerous endeavors to replicate these capabilities through robotic solutions involving soft grippers, matching human performance remains a formidable engineering challenge. Robotic competitions serve as an invaluable platform for pushing the boundaries of manipulation capabilities, simultaneously offering insights into the adoption of these solutions across diverse domains, including food handling. Serving as a proxy for the future transition of robotic solutions from the laboratory to the market, these competitions simulate real-world challenges. Since 2021, our research group has actively participated in RoboSoft competitions, securing victories in the Manipulation track in 2022 and 2023. Our success was propelled by the utilization of a modified iteration of our Retractable Nails Soft Gripper (RNSG), tailored to meet the specific requirements of each task. The integration of sensors and collaborative manipulators further enhanced the gripper's performance, facilitating the seamless execution of complex grasping tasks associated with food handling. This article encapsulates the experiential insights gained during the application of our highly versatile soft gripper in these competition environments.

Design and Implementation of a Low-Cost Intelligent Unmanned Surface Vehicle

This article describes the design and construction journey of a self-developed unmanned surface vehicle (USV). In order to increase the accessibility and lower the barrier of entry we propose a low-cost (under EUR 1000) approach to the vessel construction with great adaptability and customizability. This design prioritizes minimal power consumption as a key objective. It focuses on elucidating the intricacies of both the design and assembly processes involved in creating an economical USV. Utilizing easily accessible components, the boat outlined in this study has been already participated in the 1st Aegean Ro-boat Race 2023 competition and is tailored for entry into similar robotic competitions. Its primary functionalities encompass autonomous sea navigation coupled with sophisticated collision avoidance capabilities. Finally, we studied reinforcement learning strategies for constructing a robust intelligent controller for the task of USV navigation under disturbances and we show some preliminary simulation results we have obtained.

Application of Multilayer Neural Networks for Controlling a Line-Following Robot in Robotic Competitions

The paper presents an approach for controlling a line-following robot using artificial intelligence algorithms. This study aims to evaluate and validate the design and implementation of a competitive line-following robot based on multilayer neural networks for controlling the torque on the wheels and regulating the movements. The configuration of the line-following Robot consists of a chassis with a set of infrared sensors that can detect the line on the track and provide input data to the neural network. The performance of the line-following Robot on a running track with different configurations is then evaluated. The results show that the line-following Robot responded more efficiently with an artificial neural network control algorithm than a PID control or fuzzy control algorithm. At the same time, the reaction and correction time of the Robot to errors on the track is earlier by about 0.1 seconds. In conclusion, the capabilities of a neural network allow the line-following Robot to adapt to environmental conditions and overcome obstacles on the track more effectively.

The USA - China robotics competition: Leading the race in innovation and global power

The International Federation of Robots claims that the number of robots being produced today is at an all-time high, particularly in sectors like electronics and the automotive industry. Considering this, as artificial intelligence (AI) becomes more and more popular, it is imperative to study the robotics market, particularly in the nations that are major players in it. Robots are now integrated into all industries, especially automotive and electronics. The need for modernization and increasing competition are pushing countries to automate production and improve business processes; the United States and China have achieved the greatest success in this. According to the authors, it is China that will succeed in the industrial robotics market by introducing “smart manufacturing” and “smart factories.” In addition, there is now a growing trend towards creating robots that interact with people: the development of technology, the emergence of artificial intelligence, as well as human acceptance of the robot - all this contributes to the introduction of robots into our lives. Thus, according to the authors, it is the United States that has achieved the greatest success in the field of introducing service robots responsible for interaction with humans. The evolution of the robotics industry in the two major market participants - China and the United States - is analyzed. The study analyzes the industries in both countries to pinpoint development areas and reviews government initiatives that support business growth in both China and the United States.

A multimodal dialogue system for customer service based on user personality adaptation and dialogue strategies

ABSTRACT This paper presents a multimodal dialogue system with user personality adaptation and multiple nonverbal behavior generation for a conversational android robot that plays a customer service role in recommending travel plans. It is useful to estimate the user's personality to realize a dialogue system that adapts the dialogue strategy to individual users. To improve the user's impression of the conversation robot as a customer service agent, we must control the nonverbal behavior of the robot, such as its facial expressions and motions. Moreover, we need to design the dialogue strategy to provide information and make users enjoy the conversation by adding an ice-breaking step. Against this background, we develop a dialogue system prototype that adjusts the dialogue strategy based on the user's personality, as estimated by a pre-trained multimodal sensing model. Furthermore, we implement appropriate nonverbal behavior patterns for each situation, including the voice, motion, and facial expressions of the android robot, to provide it appropriate and polite service agent behaviors. A personality assessment game is introduced into the dialogue to prevent the user from becoming bored. The game plays an ice-breaking role in the dialogue. To let the user engage with the robot, we implement a complement to the user as a dialogue act. We evaluate the proposed multimodal dialogue system for customer service by means of participation in a dialogue robot competition. The fair evaluation results from 26 dialogue users who conversed with multiple systems show that the proposed dialogue system achieved the best score among 13 teams participating in the preliminary round of the competition, indicating that the user personality adaptation and various elements implemented in our dialogue system improve the dialogue experience of general users. The results of a third-party evaluation and multiple regression analysis show that natural response is the most important factor contributing to user impression.

Asymmetric communication: cognitive models of humans toward an android robot.

In the development of dialogue systems for android robots, the goal is to achieve human-like communication. However, subtle differences between android robots and humans are noticeable, leading even human-like android robots to be perceived differently. Understanding how humans accept android robots and optimizing their behavior is crucial. Generally, human customers have various expectations and anxieties when interacting with a robotic salesclerk instead of a human. Asymmetric communication arises when android robots treat customers like humans while customers treat robots as machines. Focusing on human-robot interaction in a tourist guide scenario, In this paper, we propose an asymmetric communication strategy that does not use estimation technology for preference information, but instead performs changing the agent's character in order to pretend to tailor to the customer. In line with this, we prepared an experimental method to evaluate asymmetric communication strategies, using video clips to simulate dialogues. Participants completed questionnaires without prior knowledge of whether the salesclerk was human-like or robotic. The method allowed us to assess how participants treated the salesclerk and the effectiveness of the asymmetric communication strategy. Additionally, during our demonstration in a dialogue robot competition, 29 visitors had a positive impression of the android robot's asymmetric communication strategy and reported a high level of satisfaction with the dialogue.

Drone navigation system based on QR code recognition using fast component-based two-staged algorithm

Kontes Robot Terbang Indonesia (KRTI) is a robotics competition organized by the National Achievement Center (Puspresnas). One of the branches of this competition is Vertical Takeoff and Landing (VTOL), which challenges participants to develop autonomous drones for package delivery based on QR code recognition. This research focuses on improving the accuracy of QR code recognition and drone navigation using a fast component-based two-stage method. Various image recognition methods for QR codes are discussed, including multi-threshold method, canny edge detection method, and fast componentbased two-stage method. The selected methods are modified to overcome the problems of rotation and perspective distortion. This research uses a quantitative and practical approach and is conducted indoors due to the limitations of drones on wind, light, and patterns on the ground. The fast component-based two-step method achieved 90.74% accuracy in QR code recognition, with certain challenges such as dark colored images. The navigation system relies on the drone’s position relative to the QR code in the image. The drone is instructed to approach the QR code for decoding. Test results showed 83.07% accuracy in matching commands, with problems due to QR code recognition failure. Strategies to overcome the recognition failure included rotation during takeoff and matching commands based on previous instructions. In addition, lag issues during drone navigation were addressed by optimizing the number of frames processed. The drone’s environmental requirements, such as well-lit areas and patterned floors, are described to ensure stable flight. This research provides insight into improving autonomous drone systems for package delivery through improved QR code recognition and navigation methods.

Developing A Robot to Improve The Accuracy of Ring Retrieval and Throwing at The ABU Robocon Indonesia Robot Competition

This article outlines the creation and application of a technologically improved robot designed to amplify the precision and effectiveness of ring retrieval and projection tasks in the ABU Robocon Indonesia Robot Challenge. The ABU Robocon competition is an annual event that tasks teams with crafting robots capable of accomplishing specific assignments under a predetermined time limit. The ring retrieval and projection task, historically known for its precision requirements, has proven to be quite demanding. Our strategy entailed the incorporation of cutting-edge technologies into the robot's design, encompassing computer vision and machine learning algorithms, to augment its accuracy and performance. We equipped the robot with cameras and sensors for the detection and analysis of ring positions and orientations. Real-time decisions regarding the optimal approach for retrieving and accurately projecting the rings were made using machine learning models that had undergone training. The outcomes of our experiments reveal a marked enhancement in the robot's performance when compared to conventional methods. The tech-enhanced robot consistently exhibited a heightened success rate when performing ring retrieval and projection tasks. This development not only boosts the competitiveness of our robot in the ABU Robocon competition but also underscores the potential of advanced technologies in enhancing the performance of robotics systems when confronted with intricate tasks.

Improving user's sense of participation in robot-driven dialogue

ABSTRACT In this study, we aimed to enhance the sense of dialogue participation (SDP), which reflects an individual's conscious engagement in dialogues, and investigated the multimodal dialogue elements that contribute to its improvement. To achieve this, we developed a dialogue system utilizing an android robot specifically for tour spot recommendations at a travel agency. The system was designed with various components aimed at enhancing SDP and was evaluated by 29 participants during the Dialogue Robot Competition 2022. After conceptually defining SDP, we employed the author's subjective evaluation to visualize the time-series changes in SDP at approximately 20-second intervals, using video recordings of the dialogues. Initially, we expected an overall decrease in SDP, as the robot had a speaking duration of around two minutes during the middle of the dialogue session when presenting the tour plan. However, the analysis of five participants' data revealed an increase in SDP during segments that involved ‘Dealing with content that increases the user's interest’ and ‘Multimodal response requests.’ Consequently, we can conclude that these elements have a discernible impact on enhancing SDP.

Application of Object Grasping Using Dual-Arm Autonomous Mobile Robot—Path Planning by Spline Curve and Object Recognition by YOLO—

In the trash-collection challenge of the Nakanoshima Robot Challenge, an autonomous robot must collect trash (bottles, cans, and bentos) scattered in a defined area within a time limit. A method for collecting the trash is to use machine learning to recognize the objects, move to the target location, and grasp the objects. An autonomous robot can achieve the target position and posture by rotating on the spot at the starting point, moving in a straight line, and rotating on the spot at the destination, but the rotation requires stopping and starting. To achieve faster movement, we implemented a smooth movement approach without sequential stops using a spline curve. When using the training data previously generated by the authors in their laboratory for object recognition, the robot could not correctly recognize objects in the environment of the robot competition, where strong sunlight shines through glass, because of the varying brightness and darkness. To solve this problem, we added our newly generated training data to YOLO, an image-recognition algorithm based on deep learning, and performed machine learning to achieve object recognition under various conditions.

Proposal for Navigation System Using Three-Dimensional Maps—Self-Localization Using a 3D Map and Slope Detection Using a 2D Laser Range Finder and 3D Map

Many teams participating in robotic competitions achieve localization using a 2D map plotted using adaptive Monte Carlo localization, a robot operating system (ROS) open-source software program. However, outdoor environments often include nonlevel terrain such as slopes. In the indoor environment of multilevel structures, the data representing different levels overlap on the map. These factors can lead to localization failures. To resolve this problem, we develop a software by combining HDL localization, which is an ROS open-source software, with our own program, and use it to achieve localization based on a 3D map. Furthermore, the authors observe the erroneous recognition of a slope as a forward obstacle during a competition event. To resolve this, we propose a method to correct erroneous recognition of obstacles using a 2D laser range finder and 3D map and confirm its validity in an experiment carried out on a slope on a university campus.

Pelatihan Robot Soccer Bagi Siswa-Siswi SMKN 4 Kota Serang

In the era of the Merdeka Curriculum, Industry 4.0 encompasses online learning, data analysis, artificial intelligence, the Internet of Things, digital skills development, project-based learning, and innovation. This enables a more personalized, efficient, and relevant education, while prioritizing equal access to technology and student data security and privacy. Community service in this context aims to provide introduction and training packaged in the form of a one-day workshop to attract the interest and talents of students at SMKN 4 Kota Serang to the basics of robotics, hardware components, software, as well as the design of hardware and software for soccer robots. The outcome of this activity is that students are able to understand the basic components, programming concepts of soccer robots, and can assemble a soccer robot module effectively and operate it. Many students are enthusiastic about this activity and express hope that it can continue so that a new robotics team can be formed at SMKN 4 Kota Serang, preparing them for upcoming robot competition championships.

University of Wollongong announced as champions of National Instruments’ autonomous robotic competition

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Barriers to the Full Participation of Girls in Robotics: A Case Study of a South African Community of Practice

The lack of participation of girls in programming robotics is well documented in the international literature on the subject. The Inspired Towards Science, Engineering and Technology (I-SET) is a community engagement project that promotes science engagement in predominantly disadvantaged communities. In this paper, we present the findings of an action research project. The primary objective of this research project is to monitor and improve this community of practice. This research paper aims to explore whether girls encounter barriers to full participation in robotics competition teams. While educational parity has been reached in STEMI-related subjects at the school level, young women are far less likely to select STEMI fields at the tertiary level compared to young men. The I-SET community of practice is well-situated to promote STEMI fields to girls from an early age. Barriers to their full participation in the I-SET community of practice included male dominance in the everyday activities and speech acts of the teams; a lack of technohabitus; and alienation. Broader constructions of gender identity in schools and the communities that they serve presented a major challenge to coaches keen to actively include girls. These challenges were further compounded by gendered divisions of labour that left little time for girls (particularly those from poorer backgrounds) to practice.

Using ‘Real-World’ Robotics Competitions to Support Digital Technology Implementation: Impacts of Professional Development

Using ‘Real-World’ Robotics Competitions to Support Digital Technology Implementation: Impacts of Professional Development