Robot Contest Research Articles

Demonstration of the Robot that Equips 2 DOF Ball Throwing Mechanism in Real Robot Contest

Demonstration of the Robot that Equips 2 DOF Ball Throwing Mechanism in Real Robot Contest

Implementation of Omnidirectional Movement on Rugby Robot

Robots in the KRAI competition (Indonesian ABU Robot Contest) are required to be able to maneuver and move well, efficiently and quickly in order to be able to carry out the task of picking up and kicking rugby balls. In order for the robot to move quickly and precisely, a wheel that can move dynamically is needed. The wheel used is the Omniwheel. This wheel has a small wheel located on the outer side of the main wheel, so that the movement of the rugby robot can run smoother when changing positions. With dynamic and efficient maneuvers and displacement of the rugby robot, the robot can move more agile and can stop in the desired position.

PERANCANGAN APLIKASI BASE STATION DALAM SISTEM KOORDINASI ROBOT SEPAK BOLA BERODA DENGAN MULTI THREAD

The Indonesian Wheeled Football Robot Contest (KRSBI Beroda) is one of the divisions of the Indonesian Robot Contest (KRI). At KRSBI Beroda robots are expected to be able to move automatically by using the Base Station as a communication center between robots in the same team. Base Station has a function to forward orders from the referee or RefBox (Referee Box) to the robot. This study aims to add features to the Base Station by displaying the position of two robots in the field in animation using the multi-thread method to facilitate strategy formulation. The result is that the Base Station that is made can display the position of two robots in the field without any errors.

Perancangan Software Robot Pencari dan Penyusun Menara Lagori pada ABU Robocon 2022

Lagori is a traditional game and one of the games originating from southern India. This game is played by two teams, where the first team is the seeker and the second team is the pitcher. The game starts by throwing the ball by the seeker to break the stone tower called Lagori. While the seeker tries to pile up more stones, the hitter throws the ball to annoy them. The purpose of this final project is to design a search robot and a Lagori tower to take part in the Indonesian Robot Contest (KRI) 2022. This search robot and Lagori tower are designed based on the Contest procedure using the Atmega2560 microcontroller as a microcontroller, pneumatics are used to pull and remove the gripper, robot The search engine and the Lagori tower use a semi-automatic system, so a wireless joystick is used as a controller, making it easier for users to control it. The results of the search robot and tower testing are in accordance with the algorithm that has been embedded in the 2560, for the pwm setting it varies between 100 and 110 and is used as a speed controller and the robot moves directions such as forward, backward, forward, backward, left, and to the right. The I/O ports on the microcontroller are given logic (logic 1), while for pneumatics repelling and pulling the gripper arm, gripper motors, gripper motors, bohh ball drive motors, and off logic firing motors (logic 0).

Implementasi Algoritma Haar Cascade Classifier Dalam Mendeteksi Robot Sepak Bola Beroda

In soccer robot contests, generally soccer robots can recognize their own team robots through color detection using the HSV model. Robots that use color detection to identify their own team robot can detect objects that have the same color value, so objects that have the same color will be considered as their own team robot. However, if you only rely on this method, it is still lacking when viewed in terms of object tracking. Haar like feature or known as Haar Cascade Classifier is a rectangular (square) feature method, which gives a specific indication of an image. This method is able to detect quickly and in real time. Therefore, the researchers tried to unite these two detections to be able to produce a better detection system than before. It is hoped that hoped that in this study, researchers can provide input for the Robotics team of Dinamika Bangsa University in improving the accuracy of robot detection against robots in the same team. From the results of the study it was found that the detection of objects with a distance from the camera as far as 100 cm to 150 cm was successfully carried out, but with a distance of more than 150 cm the object was not detected.

Optimization of Humanoid Robot Leg Movement Using Open CM 9.04

The Indonesian Robot Dance Contest (KRSTI) is a branch of the Indonesian Robot Contest (KRI) with the theme of dance. The robot used is a humanoid robot that can dance. Every year at the event, the provisions for robots constantly change, both the type of dance being demonstrated and the requirements for the robot's height. The taller the robot, the more difficult it is to control its walking movements because of the load it carries. This study uses a suitable algorithm to make the walking movement more natural and minimize the robot's falling. Human ROM data is used as a parameter for the range of motion of the servos that act as joints in the robot's legs. The algorithm created serves to determine the initial position of the angle on the servo to avoid the wrong initial movement position between one servo and another. The robot used is the Bioloid Robot’s leg Type A and uses OpenCM 9.04-A as the controller. The results showed that ROM on human feet could not be fully implemented on robot legs due to the robot's structure and the need for a robot that only relies on an algorithm to find the correct fulcrum to maintain balance. The comparison results show that the movement when walking on the ankle (ID servo 15) ranges from 749-567, while the ROM range is only between 580-512. When walking (servo ID 16), movement ranges from 460-291, while the ROM range ranges from 580-512.

Creative Robot Contests for Decommissioning as Conceived by College of Technology or KOSEN Educators

This paper’s theme “Creative Robot Contest for Decommissioning (hereinafter, this Creative Robot Contest)” may be called a contest that would never have been planned in the absence of the Great East Japan Earthquake on March 11, 2011 and the core meltdown accident at Fukushima Daiichi Nuclear Power Plant of Tokyo Electric Power Company. When it was decided to decommission the nuclear reactors, their decommissioning was immediately put in hand, which, however, met with a high barrier of high radiation doses unconquerable even by the modern science and technologies. In order to advance the stagnating decommissioning work and in hopes of some creative breakthrough ideas from KOSEN students, this Robot Contest, first held in 2016, has been held by KOSEN teams who have shown their willingness to participate in it as a venue useful to address any difficulties they will face in the real world or as teaching material for them to consider social problems. This Robot Contest, sponsored by the Japan Atomic Energy Agency and the Council of Technical Colleges for Human Resource Development for Decommissioning, is planned and managed by the Executive Committee of Creative Robot Contest for Decommissioning (hereinafter, the Executive Committee) with its secretariat at National Institute of Technology, Fukushima College (hereinafter, Fukushima KOSEN) in Fukushima Prefecture where Fukushima Daiichi Nuclear Power Plant is located. In this paper, teachers at Osaka Metropolitan University College of Technology (hereinafter, Osaka Metropolitan University KOSEN) and at Nara National College of Technology, Nara College (hereinafter, Nara KOSEN), whose respective teams have participated in this Robot Contest every year from the first one, and teachers at Fukushima KOSEN who organize and manage the executive committee describe their own views on this Robot Contest based on their viewpoints as KOSEN educators. In the first half, this paper reviews this Robot Contest and outlines the robots of Osaka Metropolitan University KOSEN which participated in it in FY2016 and FY2017 and of Nara KOSEN which did in FY2016. In the latter half, this paper describes the views of the KOSEN teachers who educate engineers in the framework of robot contests from the viewpoints of the teachers who have instructed the teams of Osaka Metropolitan University KOSEN and Nara KOSEN who have participated in this Robot Contest up to the sixth one and who have held this Robot Contest.

Development of Multi-Articulated Tracked Vehicle with a Sensorless Salvaging Bucket for Decommissioning

In this paper, we describe a prototypical crawler vehicle developed for “Hairo Sozo Robocon,” i.e., the 4th creative robot contest for decommissioning conducted in 2019. The rules of this robot contest required a high turning ability on a flat floor and high mobility through the inner space of a pipe. In addition, the robot needed to salvage objects from a floor placed 3.2 meters underneath itself. We developed a multi-articulated tracked vehicle to provide high mobility in both contexts. The vehicle was equipped with a sensorless salvaging bucket inspired by a traditional sampler sounding weight.

Special Issue on Creative Robot Contest for Decommissioning

The decommissioning of the Fukushima Daiichi Nuclear Power Plant, the plant affected by the Great East Japan Earthquake, will continue for a long time to come, but few young people are willing to take on the challenge because they tend to have the impression that decommissioning work is a post-accident cleanup. However, decommissioning is the most important issue for Fukushima and is of global importance. Therefore, the National Institute of Technology, Fukushima College (Fukushima KOSEN), which is located closest to the Fukushima Daiichi Nuclear Power Plant, established a council of cooperation and started the Creative Robot Contest for Decommissioning in cooperation with other National Institute of Technologies (KOSEN) to increase the awareness and interest in decommissioning among the young. The robot contest, themed on issues related to the decommissioning of the Fukushima Daiichi Nuclear Power Plant, has been held annually since 2016 for students of KOSEN across Japan, and the 7th Creative Robot Contest for Decommissioning is scheduled to be held in December 2022. Although the ideas and robots developed by KOSEN students from all over Japan to solve the problems involved in the decommissioning will not directly contribute to future decommissioning work, the ideas and technologies learned can be used in other fields, and the participating students are expected to be active in a wide range of fields as well. This special issue contains review papers of the Creative Robot Contest for Decommissioning, development reports, and research papers on the robots developed. A total of eight papers are included: two review papers, two research papers, and four development reports on decommissioning robots designed by students. At the same time, we hope that young people in Japan and abroad will also become interested in the robots introduced in this special issue, and that the decommissioning work of Fukushima Daiichi Nuclear Power Plant will proceed safely and securely by combining the wisdom of the whole world.

Development of Robot Simulating Fuel Debris Retrieval

A creative robot contest for decommissioning was held as part of human resources development for decommissioning of the Fukushima Daiichi Nuclear Power Station of Tokyo Electric Power Co., Inc. In the 5th creative robot contest for decommissioning, robots were developed for fuel-debris retrieval from the lower part of the pedestal. Fukushima KOSEN developed a robot consisting of a primary robot that moves to an access port on the platform while a secondary robot is stored inside the main body. The robot collects fuel debris by lowering the secondary robot 3.2 m downward. Fukushima KOSEN also developed a graphical user interface (GUI) to visually express the motion of the robot.

Activities of the Creative Robot Contest for Decommissioning at National Institute of Technology (NIT), Tsuruoka College

The purpose of the Creative Robot Contest for Decommissioning is to raise students’ interest in decommissioning through robotics, contribute to the cultivation of students’ creativity, and develop not only their problem-solving ability but also their problem-finding ability. The contest assumes fuel debris removal from the lower part of the pedestal at the Fukushima Daiichi Nuclear Power Plant. This paper describes the activities of our college in the Creative Robot Contest for Decommissioning and the robot design to overcome the challenges of the 4th and 5th contests. Whereas the 4th contest was held on site, the 5th contest was held online. To evaluate the robot that we have developed, we prepared a mock-up of the contest field at our school and conducted a demonstration. In the 4th contest, our team was the only one among the participating teams to complete the task and won the award from the Ministry of Education, Culture, Sports, Science and Technology (MEXT). In the 5th contest, we won the award from the Governor of Fukushima prefecture. The educational effects of the Creative Robot Contest for Decommissioning were great. The students who participated in the contest became more interested in decommissioning and were able to develop problem-solving skills, the ability to study independently, schedule management, and so on.

Research on Debris Removal Robot

This study developed a debris removal robot for the Fukushima Daiichi Nuclear Power Plant. The creative robot contest for decommissioning the nuclear reactors of the Fukushima Daiichi Nuclear Power Station is an annual competition based on problem-based learning. It aims to stimulate students’ interest in decommissioning nuclear reactors while simultaneously imparting problem-solving skills. The development of our debris removal robot started with this contest. The proposed robot can travel in a pipe, has a 3 m telescopic arm, and is equipped with a debris removal device known as a spiral catcher. This report provides an overview of this robot. We expect that complex robot development will be achieved by putting forth several ideas from various fields and fostering advanced problem-solving skills by encouraging students to address difficult tasks. Therefore, we successfully fostered the social implementation skills of students and developed a debris removal robot.

Robots Climbing Up and Down a Steep Stairs and Robots Retrieving Objects from High Places

We designed and fabricated robots with various mechanisms to solve the problems set in the Creative Robot Contest for Decommissioning. Robots with various mechanisms were designed and manufactured to solve the problems set by the Creative Robot Contest for Decommissioning. More specifically, a mechanism that can climb up the stairs robustly was realized by attaching claws to the crawlers to solve the problem of climbing up a steep staircase while carrying a heavy load. For the task of retrieving simulated fuel debris from a high location, we developed a mechanism to raise and lower the robot and a mechanism to retrieve simulated fuel debris without fail. Consequently, we realized the idea of robot technology required for decommissioning work and training human resources involved in decommissioning work.

Algorithm Contest of Calibration-free Motor Imagery BCI in the BCI Controlled Robot Contest in World Robot Contest 2021: A survey

Objective: From September 10 to 13, 2021, the finals of the BCI Controlled Robot Contest in World Robot Contest 2021 were held in Beijing, China. Eleven teams participated in the Algorithm Contest of Calibration-free Motor Imagery BCI. The participants employed both traditional electroencephalograph (EEG) analysis methods and deep learning-based methods in the contest. In this paper, we reviewed the algorithms utilized by the participants, extracted the trends and highlighted interesting approaches from these methods to inform future contests and research recommendations. Method: First, we analyzed the algorithms in separate steps, including EEG channel and signal segment setup, prepossessing technology, and classification model. Then, we emphasized the highlights of each algorithm. Finally, we compared the competition algorithm with the SOTA algorithm. Results: The algorithm employed in the finals performed better than that of the SOTA algorithm. During the final stage of the contest, four of the top five teams used convolutional neural network models, suggesting that with the rapid development of deep learning, convolutional neural network-based models have been the most popular methods in the field of motor imagery BCI.

A solution to supervised motor imagery task in the BCI Controlled Robot Contest in World Robot Contest

Background: One of the most prestigious competitions in the world is the World Robot Conference. This paper presents the winning solution to the supervised motor imagery (MI) task in the BCI Controlled Robot Contest in World Robot Contest 2021. Methods: Data augmentation, preprocessing, feature extraction, and model training are the main components of the solution. The model is based on EEGNet, a popular convolutional neural networks model for classifying electroencephalography data. Results: Despite the model’s lack of stability, this solution was the most successful in the task. The channels closest to the vertex were the most helpful in feature extraction. Conclusion: This solution is suitable for supervised MI tasks not only in this competition but also in future scenarios.

Overview of the winning approaches in BCI Controlled Robot Contest in World Robot Contest 2021: Calibration-free SSVEP

Recently, steady-state visual evoked potential (SSVEP) has become one of the most popular electroencephalography paradigms due to its high information transfer rate. Several approaches have been proposed to improve the performance of SSVEP. The calibration-free scenario is significant in SSVEP-based brain–computer interface systems, where the subject is the first time to use the system. The participating teams proposed several effective calibration-free algorithm frameworks in the SSVEP competition (calibration-free) of the BCI Controlled Robot Contest in World Robot Contest 2021. This paper introduces the approaches used in the algorithms of the top five teams in the final. The results of the five subjects in the final proved the effectiveness of the approaches. This paper discusses the effectiveness of each approach in improving the system performance in the calibration-free scenario and gives suggestions on how to use these approaches in a real-world system.

Comparison of cross-subject EEG emotion recognition algorithms in the BCI Controlled Robot Contest in World Robot Contest 2021

Electroencephalogram (EEG) data depict various emotional states and reflect brain activity. There has been increasing interest in EEG emotion recognition in brain–computer interface systems (BCIs). In the World Robot Contest (WRC), the BCI Controlled Robot Contest successfully staged an emotion recognition technology competition. Three types of emotions (happy, sad, and neutral) are modeled using EEG signals. In this study, 5 methods employed by different teams are compared. The results reveal that classical machine learning approaches and deep learning methods perform similarly in offline recognition, whereas deep learning methods perform better in online cross-subject decoding.

Promoting brain–computer interface in China by BCI Controlled Robot Contest in World Robot Contest

Promoting brain–computer interface in China by BCI Controlled Robot Contest in World Robot Contest

Review of training-free event-related potential classification approaches in the World Robot Contest 2021

Recently, rapid serial visual presentation (RSVP), as a new event- related potential (ERP) paradigm, has become one of the most popular forms in electroencephalogram signal processing technologies. Several improvement approaches have been proposed to improve the performance of RSVP analysis. In brain–computer interface systems based on RSVP, the family of approaches that do not depend on training specific parameters is essential. The participating teams proposed several effective training-free frameworks of algorithms in the ERP competition of the BCI Controlled Robot Contest in World Robot Contest 2021. This paper discusses the effectiveness of various approaches in improving the performance of the system without requiring training and suggests how to apply these approaches in a practical system. First, appropriate preprocessing techniques will greatly improve the results. Then, the non-deep learning algorithm may be more stable than the deep learning approach. Furthermore, ensemble learning can make the model more stable and robust.

An improved EEGNet for single-trial EEG classification in rapid serial visual presentation task

As a new type of brain–computer interface (BCI), the rapid serial visual presentation (RSVP) paradigm has attracted significant attention. The mechanism of RSVP is detecting the P300 component corresponding to the target image to realize fast and correct recognition. This paper proposed an improved EEGNet model to achieve good performance in offline and online data. Specifically, the data were filtered by xDAWN to enhance the signal-to-noise ratio of the electroencephalogram (EEG) signals. The focal loss function was used instead of the cross-entropy loss function to solve the classification problems of unbalanced samples. Additionally, the subject-specific data were fed to the improved EEGNet model to obtain a subject-specific model. We applied the proposed model at the BCI Controlled Robot Contest in World Robot Contest 2021 and won the second place. The average recall rate of the four participants reached 51.56% in triple classification. In the offline data benchmark dataset (64 subjects-RSVP tasks), the average recall rates of groups A and B reached 76.07% and 78.11%, respectively. We provided an alternative method to identify targets based on the RSVP paradigm.