IROS2019国际学术会议论文集1625

Manipulation Purpose Underwater Agent Vehicle for Ghost Net Recovery Mission Juhwan Kim1, Taesik Kim1, Jason Kim1and Son-Cheol Yu1 AbstractWe designed a manipulation purpose small agent vehicle and performed a ghost net recovery mission with the vehicle to test its control accuracy and capacity to be used in other tasks. We constructed a control model that allows the vehicle to perform the complex motion of 4-degrees-of- freedom (DOF) movements and underwater manipulation. Also, we implemented a ghost net recovery algorithm that can automatically detect, grip, and lift ghost nets. The test results show stable yaw, depth, and position control capabilities, and the manipulation performance was verifi ed by automatically performing a ghost net recovery mission. I. INTRODUCTION Underwater vehicles with manipulation ability are useful in several missions as it allows direct observation of target objects by underwater sampling and can operate in dangerous or shallow areas on behalf of human divers 12. The researchers generally applied underwater manipulators to remotely operated vehicles (ROVs) as robot arms, and they conducted few studies related to maneuvering. Manipulation using ROVs is robust and can respond immediately to various situations because the operator manually manipulates the ROVs motion directly 4. However, one of the problems that the remotely operated vehicles (ROVs) carrying out manipulation tasks has is that the vehicles cannot perform operations on extensive territories manually controlled by human pilots. Additionally, it is challenging to work on narrow and complex terrains when their sizes are not small enough. On the other hand, it is desirable to develop an autonomous underwater vehicle (AUV) system that can navigate a wide area. However, entering complex terrains and controlling vehicles to hover while the operation is other essential issues in manipulation tasks 56. When robot arm moves, the center of vehicle changes, and consequently, the pose of vehicle changes. We should build a sophisticated control model as a small error could fail delicate works to keep the vehicles pose. Besides, because of the limitations of the valuable operating vehicles, it is almost impossible to carry out multiple manipulation tasks such as moving far long pipes or that must perform at the same time from a distance. *This research was supported by the MSIT(Ministry of Science and ICT), Korea, under the ICT Consilience Creative program(IITP-2019-2011-1- 00783) supervised by the IITP(Institute for Information however, currently, due to the limited experimental resources, the test was conducted in a water tank where a similar environment was confi gured. 2) Mission: We used the abandoned net in the actual sea as the target object. This net is deeply entangled in the fl oor. While carrying out the net recovery mission, the vehicle would encounter various situations, such as a net is freely lying on the fl oor or some stones are weighing down a net. In some cases, even cutting down some parts of a net should be considered. Fig. 4. The fl ow diagram of recovering underwater ghost net mission. IV. METHOD A. Recovering Ghost Net Mission To achieve the net recovery mission, we implemented an algorithm for agent vehicles that maximize the success rate. Fig. 3 describes the overall process of task determination, image processing, and motion controlling steps performed in the net recovery mission. We enter into specifi c methods for each step below. The agent vehicle starts operation at an initial pose in the water tank where the target net is not visible at fi rst sight. 1) Ghost net detection: We used tiny-YOLOv3, a deep learning-based object detection algorithm 1112, to train the vehicle to recognize nets. We trained the network using 500 ghost net images obtained from google and 300 target net images taken inside and outside of the water tank. After training, the vehicle was able to detect ghost nets in real- time. 2) Grasping and Lifting the Target Objects: The vehicle confi rms whether the object is successfully grasped or not by looking at the camera image of the gripper. If the image shows no object is near the gripper and the jaw opening length of the gripper is around zero, the vehicle assumes that the grasping has failed and tries again. Fig. 6 shows an orange color fi lter that eliminates all pixels other than the jaw of the gripper and the fi ltered image when the vehicle successfully grasped the target object. If some parts of the target object are knocked down by massive objects, such as stones, the vehicle will be stuck at a certain point. The vehicle determines whether it is stuck by tracking its depth change. When the depth of the vehicle suddenly stops for a while, the vehicle proceeds to step 3). 3) Pull Motion Technique: If the lifting fails after grasp- ing the net, the vehicle loosens the net and tries to untie it with three types of pulling motions: motion on the swing to 3908 Fig. 5.Ghost net detection result by deep learning based real-time object detection. Fig. 6.The determination of the net grasping. disentangle net, reinforced heave function to stretch net, and motion on the conical surface to disentangle net (Fig. 7). V. RESULT A. Control Experiment We conducted a control experiment on yaw, depth, and position control in a water tank, and recorded the step responses as graphs. 1) Yaw and Depth Control: As a result, we performed yaw and depth control experiments. The gain of the PID controller is experimentally adjusted to converge to the targeted level (Fig. 8) (Fig. 9). B. Manipulation Experiment The ghost net recovery experiment was carried out in a water tank of size 3 m x 1.5 m x 1.5 m (width x length x height). The experiment was carried out in a rather small tank, however enough room to test the effectiveness of the system (Fig. 10). The rest of the results are in the video of the experiment. Fig. 7.The examples of pull motion technique. Fig. 8.Experiment result, step response of yaw. 3909 Fig. 9.Experiment result, step response of depth. Fig. 10.The result of recovering ghost net mission. VI. DISCUSSION AND CONCLUSIONS This study verifi ed the development of the manipulation purpose agent vehicle, and the ghost net recovery mission was carried out. An agent vehicle can be an effective solution in the underwater manipulation domain. We designed an agent vehicle specialized for manipulation, performed funda- mental modeling, and conducted basic control experiments. In addition, we have selected a simple manipulation mission to implement this and to advance the possibility of the ultimate agent vehicle system further. We found that the pro- posed system was able to perform manipulation successfully. Future work on studying multi-agent systems and cooperative operation with main AUV, we can make great progress in underwater manipulation. REFERENCES 1 Lane, David M., et al. ”AMADEUS: advanced manipulation for deep underwater sampling.” IEEE Robotics & Automation Magazine 4.4 (1997): 34-45. 2 Baranova, Olga. ”World ocean atlas 2005.” (2015). 3 Ryu, Jee-Hwan, Dong-Soo Kwon, and Pan-Mook Lee. ”Control of underwater manipulators mounted on an ROV using base force infor- mation.” Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No. 01CH37164). Vol. 4. IEEE, 2001. 4 Marani, Giacomo, Song K. Choi, and Junku Yuh. ”Underwater autonomous manipulation for intervention missions AUVs.” Ocean Engineering 36.1 (2009): 15-23. 5 Prats, Mario, et al. ”Reconfi gurable AUV for intervention missions: a case study on underwater object recovery.” Intelligent Service Robotics 5.1 (2012): 19-31. 6 Cieslak, Patryk, Pere Ridao, and Mariusz Giergiel. ”Autonomous underwater panel operation by GIRONA500 UVMS: A practical approach to autonomous underwater manipulation.” 2015 IEEE In- ternational Conference on Robotics and Automation (ICRA). IEEE, 2015. 7 Son-Cheol Yu, Junku Yuh, Jinwhan Kim, A Preliminary Test On Agent-based Do