Kento Takemoto, Yang Teni, Motoki Sakai, Yew Qi Ming, The University of Tokyo

and the AUV, MakiComet.
Introduction
On August 24 and 25, students and enthusiasts gathered at JAMSTEC headquarters in Yokosuka, Japan, for the Underwater Robot Convention in JAMSTEC 2024. The event, organized by NPO Japan Underwater Robot Network, provided a platform for participants to exchange technical ideas and network through the competition and presentations of self-build underwater robots. For more details on the convention, visit the official website [1] (in Japanese), and additional reports from past conventions can be found in [2], [3], [4], [5], [6], and [7].
We, the authors, are master’s students from Prof. Maki’s laboratory at the University of Tokyo, and we participated in the AI-challenge division as team “MakiCommanders” [Figure 1]. Our primary goal was to gain essential knowledge and skills in underwater robotics to support our future research. Additionally, we gained valuable experience in teamwork, which is crucial for the development and operation of underwater robots.

Competition Rules
The AI-challenge division was created to promote the use of Artificial Intelligence (AI) in underwater robotics. In this challenge, robots were required to autonomously break balloons placed in a water tank. There were three types of balloons: red, yellow, and blue, each with different point values depending on their height in the tank [Table 1]. The arrangement of the balloons was random. Figure 2 shows a diagram of the water tank setup. The only tool allowed to break the balloons was a thumbtack. Teams had 4 minutes to complete the task, and the higher the score, the better the team’s rank. In addition to the performance score, judges also evaluated the robots’ autonomy and the poster presentations, with the final ranking based on the total score.

Color | Height [m] | Score |
Red | 0.5 | 30 |
Yellow | 1.5 | 10 |
Blue | 0.7 | -10 |
Table 1. Height and score of balloons.
Strategy
MakiComet is designed based on last year’s competition vehicle Sebastian, and can control surge, heave, and yaw. This year’s competition rules are the same as last year’s, requiring participants to pop as many red and yellow balloons as possible while avoiding blue balloons within the time limit to earn higher scores. Therefore, last year’s algorithms [2] are used for the balloon search and approach methods. In summary, at the start of the competition, the AUV moves to the center of the pool and uses the heave thruster to land on the bottom of the pool. The point where it hits the bottom is used as the vertical reference, and it rises to an altitude of 50 cm where the red balloon is placed, and then begins its search. When it finds the target balloon, it moves toward its center coordinates.

Our main goal this year was to solve last year’s problems by utilizing this search and approach method. We thought that there were two major problems last year. The first problem was that when popping a balloon with a pin, the AUV’s speed was insufficient, or when the speed was increased, it became difficult to control, and the balloon could not be hit properly and escaped. To address this problem, we added a new mechanism that allows the pin to be pressed firmly against the balloon. Specifically, a new shaftless thruster was installed and a pin was placed on its intake port. This shaftless thruster finds the target balloon and starts rotating when the approach begins. This allows the balloon to be reliably approached, and the suction force of the thruster presses the balloon against the pin, making it possible to pop it reliably. The second challenge is that the color identification of balloons, which worked in the AUV’s development environment, does not work well in the competition environment. This is thought to be a phenomenon caused by differences in the lighting and natural light intensity between the development environment and the competition environment. To address this issue, we ensured that the parameters could be adjusted based on the local environment. Specifically, when identifying a color as red, instead of using the RGB value of red as the standard, the RGB value actually captured by the camera is checked and set. This approach allows the standard appropriate to the environment to be set for each target color, improving the accuracy of balloon color identification.

Top: before, Bottom: after.
AUV
This mission used the original cruising autonomous underwater vehicle “MakiComet” [Figure 3], [Table 2], based on the Sebastian AUV that participated in last year’s competition.
MakiComet can move in surge, heave, pitch, and yaw by four thrusters. Its acrylic hull, which serves as a buoyant body, is located on both sides. At the same time, the center of gravity is set lower in the middle to maintain stability in the roll direction. As sensors, we used a depth sensor for estimating depth and a camera module for recognizing balloons in the water. The AUV was also equipped with leg parts for landing on the pool floor. Additionally, it featured another thruster without a shaft, as mentioned in the previous section, at the top front of the AUV, and a pushpin was attached at the center of the thruster. Thanks to the suction power of the new thruster, MakiComet was able to suction a balloon [Figure 4], and burst the balloon reliably. It’s also worth noting that the power didn’t disturb MakiComet’s movement or posture maintenance.
As a control tool, MakiComet used a common open-source tool
Spec | |
Length[mm] | 560 |
Width[mm] | 410 |
Height[mm] | 350 |
Weight[kg] | 9 |
Thrusters | |
Surge | 2 |
Heave | 2 |
Suction | 1 |

Top: before, Bottom: after.
for robotics control called Robot Operating System (ROS) on the Raspberry Pi. The Teensy driver is used to send PWM commands to the motor drivers. In this mission, circle detection was planned to be performed using the ellipse fitting algorithm initially. This was because the Hough transform was not robust against objects like ladders inside the tank. However, we were unable to implement the alternative method effectively, so we ultimately reverted to using the Hough transform in OpenCV on images captured by a USB camera [Figure 5]. For details on the algorithm, please refer to last year’s article [2].
The competition rules were released in June 2024, and our development project was started. The development was divided into three areas: new thruster implementation, balloon recognition algorithm, and state transition algorithm. By the end of July, we abandoned the implementation of the ellipse fitting algorithm due to difficulties in making it work effectively and focused instead on thruster development and the state transition algorithm. By mid-August, the entire system was completed, and we confirmed that balloon detection and destruction could be performed reliably [Figure 6].

Result At the first day of the competition, each team is required to make a presentation with their own A0-sized poster. Judges then ask questions based on the presentation and category of participation. After the presentation, there is a time slot for every team to test and fine tune their AUV. We tested our AUV with a tether cable so that we could see the status and the environment parameters experienced by the AUV. Our AUV could not detect the balloons as the color of balloons detected is different in that environment compared to what we have practiced.
On the second day of the competition, each team is given some time to test and fine tune again. By using relevant RGB values, we managed to tune our AUV for detecting the balloons together with better maneuvering performance to carry out the mission. However at our last practice, the AUV could not float and could not switch into the search mode after diving. During the competition, it is also unfortunate for our AUV to perform the same as our last practice. After the competition, we tested the AUV and it works fine again as usual. Thus, we suspect the depth sensor or Teensy controller board might be old which cause the unexpected performance during the competition.
Throughout this competition, we realize that unexpected situation will arise easily in underwater robot development, not only limited to us. Therefore it is important to stay alert even if everything seems to be going smoothly at first. We believe that the lesson we learned from this competition will inspire us and allow us to contribute better in our research and future careers.
Acknowledgement
The Underwater Robot Convention in JAMSTEC in 2024 was supported by The Japan Society of Naval Architects and Ocean Engineers, Techno-Ocean Network, IEEE/OES Japan Chapter, MTS Japan Section, Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Oki Electric Industry Co., Ltd., Oki Com-Echoes Co., Ltd., IDEA Consultants, Inc., Nortek Japan LLC, Sea challenge Co., Ltd., Misago Co., Ltd., MinebeaMitsumi Inc., Japan Underwater Drone Association, Matsuyama Industry Co., Ltd., IWAKITEC Co., Ltd., Robotis Co., Ltd., FullDepth Co., Ltd., KOWA Corporation, Aqua Modelers Meeting, National Ocean Policy Secretariat, Cabinet Office, Government of JAPAN, Kanagawa Prefecture, Yokosuka City, Tokyo University of Marine Science and Technology, and Center for Integrated Underwater Observation Technology at Institute of Industrial Science, the University of Tokyo. We would like to express our sincere appreciation to the sponsors for their strong support and cooperation in realizing this convention.
Comments
Takemoto: It is really disappointing that I could not perform well in the actual competition, since we were able to confirm my balloon-breaking action in the practice right before the competition. However, through this result, I found out what I was lacking, and I will take this as a big lesson for the next challenge.
Yang: I realized the significance of preparation and the need to anticipate potential issues and plan solutions in advance. This experience will be invaluable throughout my master’s studies.
Sakai: I am disappointed that we were unable to achieve good results. The failure of surfacing is a critical issue that could lead to the loss of the AUV in an actual mission. I am determined to apply this lesson to my future project.
Yew: This competition makes me realize my flaws in underwater development. We will be more alert in the future.
References
[1] Underwater Robot Convention in JAMSTEC 2024 (Japanese). https://www.jam24.underwaterrobonet.org/ [2] H. Kasuga, L. Hakataya, H. Yokohata, Sebastian, the AUV Won First Place in Underwater Robot Convention in JAMSTEC 2023! IEEE OES Beacon Newsletter, 12(4), 76-79 (2023.12) https://ieeeoes.org/oes-beacon/december-2023/sebastian-the-auv-won-first-place-in-underwater-robot-convention-in-jamstec-2023/ [3] A. Toriyama, M. Ohashi, H. Yokohata, wARIEL, the AUV Won First Place in Underwater Robot Convention in JAMSTEC 2022! IEEE OES Beacon Newsletter, 11(4), 83-86 (2022.12) [4] K. Yamamoto, S. Chun, Y. Sekimori, C. Kawamura, ARIEL, the AUV Won First Place in Underwater Robot Convention in JAMSTEC 2021! IEEE OES Beacon Newsletter, 10(4), 70-73 (2021.12) [5] Y. Sekimori, T. Maki, Underwater Robot Convention in JAMSTEC 2020 – All Hands on Deck! Online!!, IEEE OES Beacon Newsletter, 10(1), 39-42 (2021.3) [6] K. Fujita, Y. Hamamatsu, H. Yatagai, Reflection for Singapore Autonomous Underwater Vehicle Challenge – the Comparison Between SAUVC and a Competition Held in Japan, IEEE OES Beacon Newsletter, 8(2), 64-67 (2019.6) [7] H. Yamagata, T. Maki, Underwater Robot Convention in JAMSTEC 2018 – from an Educational Perspective, IEEE OES Beacon Newsletter, 7(4), 68-72 (2018.12)