start-ver=1.4 cd-journal=joma no-vol= cd-vols= no-issue= article-no= start-page= end-page= dt-received= dt-revised= dt-accepted= dt-pub-year=2026 dt-pub=20260613 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=A novel robot for CT-guided bone needle insertion with a rotational drilling and force-feedback speed control mechanism: preliminary evaluation in swine en-subtitle= kn-subtitle= en-abstract= kn-abstract=Purpose To evaluate the feasibility and accuracy of computed tomography (CT)-guided needle insertion into swine bones by using a novel robotic system capable of rotational drilling.
Materials and methods This was an animal experiment using three swine. A remote-controlled robot equipped with a rotational drilling and force-feedback insertion speed control mechanism was developed for bone needle insertion. Using the robot, CT-guided insertion of a 10-gauge bone access needle was attempted in the lumbar vertebrae, ilia, and femora six times each. Needle insertion accuracy was evaluated using the angle error, which is defined as the difference between the predetermined and post-insertion needle angles on axial and sagittal CT images. The time required for needle insertion was measured. The angle error and time required for needle insertion were compared among the bones using the Kruskal?Wallis test. Adverse events were also evaluated.
Results Robotic bone needle insertion was successful in all attempts. The median axial and sagittal angle errors were 0.21 and 0.21 for the lumbar vertebrae, 0.32 and 0.13 for the ilia, and 0.65 and 0.25 for the femora, respectively. Axial angle errors were significantly different among the bone types (p?=?0.038). The time required for needle insertion was 23.6, 21.3, and 59.7 s for the lumbar vertebrae, ilia, and femora, respectively. Time was significantly different among bone types (p?=?0.017). No adverse events were observed.
Conclusion CT-guided bone needle insertion using a robot equipped with a rotational drilling and force-feedback insertion speed control mechanism was feasible and accurate in swine. en-copyright= kn-copyright= en-aut-name=MatsuiYusuke en-aut-sei=Matsui en-aut-mei=Yusuke kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=HirakiTakao en-aut-sei=Hiraki en-aut-mei=Takao kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=KimuraYuta en-aut-sei=Kimura en-aut-mei=Yuta kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=SasakiTakanori en-aut-sei=Sasaki en-aut-mei=Takanori kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= en-aut-name=MatsuuraRyutaro en-aut-sei=Matsuura en-aut-mei=Ryutaro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=5 ORCID= en-aut-name=TomitaKoji en-aut-sei=Tomita en-aut-mei=Koji kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=6 ORCID= en-aut-name=UmakoshiNoriyuki en-aut-sei=Umakoshi en-aut-mei=Noriyuki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=7 ORCID= en-aut-name=OkamotoSoichiro en-aut-sei=Okamoto en-aut-mei=Soichiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=8 ORCID= en-aut-name=MunetomoKazuaki en-aut-sei=Munetomo en-aut-mei=Kazuaki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=9 ORCID= en-aut-name=HigakiFumiyo en-aut-sei=Higaki en-aut-mei=Fumiyo kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=10 ORCID= en-aut-name=SakuraiJun en-aut-sei=Sakurai en-aut-mei=Jun kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=11 ORCID= en-aut-name=IguchiToshihiro en-aut-sei=Iguchi en-aut-mei=Toshihiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=12 ORCID= en-aut-name=NakazawaAtsushi en-aut-sei=Nakazawa en-aut-mei=Atsushi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=13 ORCID= en-aut-name=MatsumiyaKiyoshi en-aut-sei=Matsumiya en-aut-mei=Kiyoshi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=14 ORCID= en-aut-name=MatsunoTakayuki en-aut-sei=Matsuno en-aut-mei=Takayuki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=15 ORCID= en-aut-name=KamegawaTetsushi en-aut-sei=Kamegawa en-aut-mei=Tetsushi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=16 ORCID= affil-num=1 en-affil=Department of Radiology, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University kn-affil= affil-num=2 en-affil=Department of Radiology, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University kn-affil= affil-num=3 en-affil=Department of Radiology, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University kn-affil= affil-num=4 en-affil=Collaborative Research Center for OMIC, Graduate School of Medicine, Dentistry, and Pharmaceutical Sciences, Okayama University kn-affil= affil-num=5 en-affil=Department of Radiological Technology, Faculty of Health Sciences, Okayama University kn-affil= affil-num=6 en-affil=Department of Radiology, Medical Development Field, Okayama University kn-affil= affil-num=7 en-affil=Department of Radiology, Medical Development Field, Okayama University kn-affil= affil-num=8 en-affil=Department of Radiology, Medical Development Field, Okayama University kn-affil= affil-num=9 en-affil=Department of Radiology, Medical Development Field, Okayama University kn-affil= affil-num=10 en-affil=Department of Radiology, Medical Development Field, Okayama University kn-affil= affil-num=11 en-affil=Center for Innovative Clinical Medicine, Okayama University Hospital kn-affil= affil-num=12 en-affil=Department of Radiological Technology, Faculty of Health Sciences, Okayama University kn-affil= affil-num=13 en-affil=Faculty of Interdisciplinary Science and Engineering in Health Systems, Okayama University kn-affil= affil-num=14 en-affil=Department of Medical Technology, Faculty of Life Science, Okayama University of Science kn-affil= affil-num=15 en-affil=Faculty of Environmental, Life, Natural Science and Technology, Okayama University kn-affil= affil-num=16 en-affil=Faculty of Environmental, Life, Natural Science and Technology, Okayama University kn-affil= en-keyword=Robot kn-keyword=Robot en-keyword=Needle kn-keyword=Needle en-keyword=Bone kn-keyword=Bone en-keyword=CT-guided kn-keyword=CT-guided en-keyword=Intervention kn-keyword=Intervention END start-ver=1.4 cd-journal=joma no-vol=27 cd-vols= no-issue= article-no= start-page=106742 end-page= dt-received= dt-revised= dt-accepted= dt-pub-year=2025 dt-pub=202509 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Inscribed-type spherical speed reducer with uniform reduction ratio in all directions en-subtitle= kn-subtitle= en-abstract= kn-abstract=A spherical motor is an actuator that can generate rotational motion about all three orthogonal axes. However, it is difficult to obtain high output torque from most electromagnetic spherical motors, primarily due to limitations inherent in electromagnetic actuators, such as restricted magnetic force and thermal constraints. Since its torque cannot be increased using planar gears, spherical speed reducers that transmit rotational torque along three orthogonal axes through sphere-to-sphere contact are required. One major limitation of conventional spherical speed reducers is that their size increases significantly as the reduction ratio becomes higher. To address this issue, we propose a novel inscribed-type spherical speed reducer, in which the deceleration mechanism is integrated within the output sphere. This configuration enables a more compact design, reducing the overall size to approximately half that of conventional designs. To predict the angular velocity and transmitted torque, theoretical models for the rotation and torque transmission of the speed reducer were developed. According to the proposed model, the reduction ratio of the spherical speed reducer is 1/3. To verify the validity of these models, experiments were conducted to measure angular velocity and torque. The theoretical results agreed well with the experimental results. In addition, the theoretical torque exhibited an average relative error of 1.63 % compared to the experimental result. Therefore, it was confirmed that the rotation and torque transmission models were valid. These results demonstrate that a reduction ratio can be obtained in all directions of the 3-DOF of the spherical speed reducer, unlike conventional 1-DOF reducers. en-copyright= kn-copyright= en-aut-name=NaramuraSeiya en-aut-sei=Naramura en-aut-mei=Seiya kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=TonegawaKoichi en-aut-sei=Tonegawa en-aut-mei=Koichi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=ShimookaSo en-aut-sei=Shimooka en-aut-mei=So kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=YanoTomoaki en-aut-sei=Yano en-aut-mei=Tomoaki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= en-aut-name=GofukuAkio en-aut-sei=Gofuku en-aut-mei=Akio kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=5 ORCID= en-aut-name=KasashimaNagayoshi en-aut-sei=Kasashima en-aut-mei=Nagayoshi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=6 ORCID= en-aut-name=KamegawaTetsushi en-aut-sei=Kamegawa en-aut-mei=Tetsushi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=7 ORCID= affil-num=1 en-affil=Faculty of Environmental, Life, Natural Science and Technology, Okayama University kn-affil= affil-num=2 en-affil=Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University kn-affil= affil-num=3 en-affil=Faculty of Environmental, Life, Natural Science and Technology, Okayama University kn-affil= affil-num=4 en-affil=Faculty of Environmental, Life, Natural Science and Technology, Okayama University kn-affil= affil-num=5 en-affil=Okayama Prefectural University kn-affil= affil-num=6 en-affil=National Institute of Advanced Industrial Science and Technology kn-affil= affil-num=7 en-affil=Faculty of Environmental, Life, Natural Science and Technology, Okayama University kn-affil= en-keyword=Inscribed-type spherical speed reducer kn-keyword=Inscribed-type spherical speed reducer en-keyword=Rotation and torque transmission kn-keyword=Rotation and torque transmission en-keyword=Friction kn-keyword=Friction en-keyword=Spherical motor kn-keyword=Spherical motor en-keyword=Three-axis rotation kn-keyword=Three-axis rotation END start-ver=1.4 cd-journal=joma no-vol=390 cd-vols= no-issue= article-no= start-page=116594 end-page= dt-received= dt-revised= dt-accepted= dt-pub-year=2025 dt-pub=202508 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Extension-type flexible pneumatic actuator with a large extension force using a cross-link mechanism based on pantographs en-subtitle= kn-subtitle= en-abstract= kn-abstract=In this study, we propose an extension-type flexible pneumatic actuator (EFPA) with a high extension force and no buckling. In a previous study, soft actuators that extended in the axial direction by applying a supply pressure were unable to generate the extensionfs pushing force because the actuators buckled owing to their high flexibility. To generate a pushing force, the circumferential stiffness of an extension-type flexible soft actuator must be reinforced. Therefore, a cross-linked EFPA (CL-EFPA) was developed, inspired by a pantograph that restrains the EFPA three-dimensionally using the proposed link mechanism. The proposed CL-EFPA consists of three EFPAs and a cross-linking mechanism for integrating each EFPA circumference. The pushing force of the CL-EFPA is approximately 3.0 times compared with that generated by the previous EFPA with plates to restrain its plane. To perform various bending motions, attitude control was performed using an analytical model and a system that included valves, sensors, and controllers. en-copyright= kn-copyright= en-aut-name=ShimookaSo en-aut-sei=Shimooka en-aut-mei=So kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=TadachiKazuma en-aut-sei=Tadachi en-aut-mei=Kazuma kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=KamegawaTetsushi en-aut-sei=Kamegawa en-aut-mei=Tetsushi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= affil-num=1 en-affil=Faculty of Environmental, Life, Natural Science and Technology, Okayama University kn-affil= affil-num=2 en-affil=Mechanical and Systems Engineering Program, School of Engineering, Okayama University kn-affil= affil-num=3 en-affil=Faculty of Environmental, Life, Natural Science and Technology, Okayama University kn-affil= en-keyword=Soft robot kn-keyword=Soft robot en-keyword=Extension soft actuator kn-keyword=Extension soft actuator en-keyword=Link mechanism kn-keyword=Link mechanism en-keyword=Pantograph kn-keyword=Pantograph en-keyword=Attitude control kn-keyword=Attitude control END start-ver=1.4 cd-journal=joma no-vol=37 cd-vols= no-issue=1 article-no= start-page=43 end-page=53 dt-received= dt-revised= dt-accepted= dt-pub-year=2025 dt-pub=20250220 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Fan-Shaped Pneumatic Soft Actuator that Can Operate Bending Motion for Ankle-Joint Rehabilitation Device en-subtitle= kn-subtitle= en-abstract= kn-abstract=Nowadays, owing to declining birthrates and an aging population, patients and the elderly requiring rehabilitation are not getting enough physical activity. In addressing this issue, devices for rehabilitating them have been researched and developed. However, rehabilitation devices are almost exclusively used for patients who can get up, rather than those who are bedridden. In this study, we aim to develop a rehabilitation device that can provide passive exercise for bedridden patients. The ankle joint was selected as the target joint because the patients who have undergone surgery for cerebrovascular disease remain bedridden, and early recovery in the acute stage is highly desirable. We proposed and tested a fan-shaped pneumatic soft actuator (FPSA) that can expand and bend stably at angles when supply pressure is applied as an actuator for a rehabilitation device to encourage patient exercise. However, the previous FPSAfs movement deviates from the arch of the foot owing to increased supply pressure. In the ideal case, FPSA should push the arch of the foot in an arc motion. This study proposes and tests the FPSA that can operate a bending motion to provide passive exercise to the ankle joint using tensile springs and a winding mechanism powered by a servo motor. The proposed FPSA has a significant advantage of exhibiting no hysteresis in its pressure-displacement characteristics. The configuration and static analytical model of the improved FPSA are described. en-copyright= kn-copyright= en-aut-name=ShimookaSo en-aut-sei=Shimooka en-aut-mei=So kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=YokoyaHirosato en-aut-sei=Yokoya en-aut-mei=Hirosato kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=HamadaMasanori en-aut-sei=Hamada en-aut-mei=Masanori kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=ShiomiShun en-aut-sei=Shiomi en-aut-mei=Shun kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= en-aut-name=UeharaTakenori en-aut-sei=Uehara en-aut-mei=Takenori kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=5 ORCID= en-aut-name=HirayamaTakahiro en-aut-sei=Hirayama en-aut-mei=Takahiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=6 ORCID= en-aut-name=KamegawaTetsushi en-aut-sei=Kamegawa en-aut-mei=Tetsushi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=7 ORCID= affil-num=1 en-affil=Faculty of Environmental, Life, Natural Science and Technology, Okayama University kn-affil= affil-num=2 en-affil=Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University kn-affil= affil-num=3 en-affil=Department of Rehabilitation Medicine, Okayama University Hospital kn-affil= affil-num=4 en-affil=Department of Rehabilitation Medicine, Okayama University Hospital kn-affil= affil-num=5 en-affil=Department of Orthopaedic Surgery, NHO Okayama Medical Center kn-affil= affil-num=6 en-affil=Department of Emergency, Critical Care and Disaster Medicine, Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University kn-affil= affil-num=7 en-affil=Faculty of Environmental, Life, Natural Science and Technology, Okayama University kn-affil= en-keyword=fan-shaped pneumatic soft actuator kn-keyword=fan-shaped pneumatic soft actuator en-keyword=ankle-joint rehabilitation device kn-keyword=ankle-joint rehabilitation device en-keyword=hysteresis kn-keyword=hysteresis en-keyword=range of motion kn-keyword=range of motion END start-ver=1.4 cd-journal=joma no-vol=35 cd-vols= no-issue=1 article-no= start-page=65 end-page=73 dt-received= dt-revised= dt-accepted= dt-pub-year=2023 dt-pub=20230220 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Development of Automatic Inspection Systems for WRS2020 Plant Disaster Prevention Challenge Using Image Processing en-subtitle= kn-subtitle= en-abstract= kn-abstract=In this article, an approach used for the inspection tasks in the WRS2020 Plant Disaster Prevention Challenge is explained. The tasks were categorized into three categories: reading pressure gauges, inspecting rust on a tank, and inspecting cracks in a tank. For reading pressure gauges, the gyou only look onceh algorithm was used to focus on a specific pressure gauge and check the pressure gauge range strings on the gauge using optical character recognition algorithm. Finally, a previously learned classifier was used to read the values shown in the gauge. For rust inspection, image processes were used to focus on a target plate that may be rusted for rust detection. In particular, it was necessary to report the rust area and distribution type. Thus, the pixel ratio and grouping of rust were used to count the rust. The approach for crack inspection was similar to that for rust. The target plate was focused on first, and then the length of the crack was measured using image processing. Its width was not measured but was calculated using the crack area and length. For each system developed to approach each task, the results of the preliminary experiment and those of WRS2020 are shown. Finally, the approaches are summarized, and planned future work is discussed. en-copyright= kn-copyright= en-aut-name=ShimizuYuya en-aut-sei=Shimizu en-aut-mei=Yuya kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=KamegawaTetsushi en-aut-sei=Kamegawa en-aut-mei=Tetsushi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=WangYongdong en-aut-sei=Wang en-aut-mei=Yongdong kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=TamuraHajime en-aut-sei=Tamura en-aut-mei=Hajime kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= en-aut-name=TeshimaTaiga en-aut-sei=Teshima en-aut-mei=Taiga kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=5 ORCID= en-aut-name=NakanoSota en-aut-sei=Nakano en-aut-mei=Sota kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=6 ORCID= en-aut-name=TadaYuki en-aut-sei=Tada en-aut-mei=Yuki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=7 ORCID= en-aut-name=NakanoDaiki en-aut-sei=Nakano en-aut-mei=Daiki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=8 ORCID= en-aut-name=SasakiYuichi en-aut-sei=Sasaki en-aut-mei=Yuichi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=9 ORCID= en-aut-name=SekitoTaiga en-aut-sei=Sekito en-aut-mei=Taiga kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=10 ORCID= en-aut-name=UtsumiKeisuke en-aut-sei=Utsumi en-aut-mei=Keisuke kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=11 ORCID= en-aut-name=NagaoRai en-aut-sei=Nagao en-aut-mei=Rai kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=12 ORCID= en-aut-name=SembaMizuki en-aut-sei=Semba en-aut-mei=Mizuki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=13 ORCID= affil-num=1 en-affil=Okayama University kn-affil= affil-num=2 en-affil=Okayama University kn-affil= affil-num=3 en-affil=Okayama University kn-affil= affil-num=4 en-affil=Okayama University kn-affil= affil-num=5 en-affil=Okayama University kn-affil= affil-num=6 en-affil=Okayama University kn-affil= affil-num=7 en-affil=Okayama University kn-affil= affil-num=8 en-affil=Okayama University kn-affil= affil-num=9 en-affil=Okayama University kn-affil= affil-num=10 en-affil=Okayama University kn-affil= affil-num=11 en-affil=Okayama University kn-affil= affil-num=12 en-affil=Okayama University kn-affil= affil-num=13 en-affil=Okayama University kn-affil= en-keyword=WRS2020 kn-keyword=WRS2020 en-keyword=image processing kn-keyword=image processing en-keyword=auto inspection kn-keyword=auto inspection en-keyword=YOLO kn-keyword=YOLO en-keyword=OCR kn-keyword=OCR END start-ver=1.4 cd-journal=joma no-vol=10 cd-vols= no-issue= article-no= start-page=1138019 end-page= dt-received= dt-revised= dt-accepted= dt-pub-year=2023 dt-pub=20230329 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Parameter search of a CPG network using a genetic algorithm for a snake robot with tactile sensors moving on a soft floor en-subtitle= kn-subtitle= en-abstract= kn-abstract=When a snake robot explores a collapsed house as a rescue robot, it needs to move through various obstacles, some of which may be made of soft materials, such as mattresses. In this study, we call mattress-like environment as a soft floor, which deforms when some force is added to it. We focused on the central pattern generator (CPG) network as a control for the snake robot to propel itself on the soft floor and constructed a CPG network that feeds back contact information between the robot and the floor. A genetic algorithm was used to determine the parameters of the CPG network suitable for the soft floor. To verify the obtained parameters, comparative simulations were conducted using the parameters obtained for the soft and hard floor, and the parameters were confirmed to be appropriate for each environment. By observing the difference in snake robot's propulsion depending on the presence or absence of the tactile sensor feedback signal, we confirmed the effectiveness of the tactile sensor considered in the parameter search. en-copyright= kn-copyright= en-aut-name=TamuraHajime en-aut-sei=Tamura en-aut-mei=Hajime kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=KamegawaTetsushi en-aut-sei=Kamegawa en-aut-mei=Tetsushi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= affil-num=1 en-affil=Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University kn-affil= affil-num=2 en-affil=Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University kn-affil= en-keyword=snake robot kn-keyword=snake robot en-keyword=tactile sensor kn-keyword=tactile sensor en-keyword=CPG network kn-keyword=CPG network en-keyword=soft floor kn-keyword=soft floor en-keyword=genetic algorithm kn-keyword=genetic algorithm END start-ver=1.4 cd-journal=joma no-vol=22 cd-vols= no-issue=22 article-no= start-page=9016 end-page= dt-received= dt-revised= dt-accepted= dt-pub-year=2022 dt-pub=202211 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Realization of Crowded Pipes Climbing Locomotion of Snake Robot Using Hybrid Force-Position Control Method en-subtitle= kn-subtitle= en-abstract= kn-abstract=The movement capabilities of snake robots allow them to be applied in a variety of applications. We realized a snake robot climbing in crowded pipes. In this paper, we implement a sinusoidal curve control method that allows the snake robot to move faster. The control method is composed of a hybrid force-position controller that allows the snake robot to move more stably. We conducted experiments to confirm the effectiveness of the proposed method. The experimental results show that the proposed method is stable and effective compared to the previous control method that we had implemented in the snake robot. en-copyright= kn-copyright= en-aut-name=WangYongdong en-aut-sei=Wang en-aut-mei=Yongdong kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=KamegawaTetsushi en-aut-sei=Kamegawa en-aut-mei=Tetsushi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= affil-num=1 en-affil=Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University kn-affil= affil-num=2 en-affil=Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University kn-affil= en-keyword=snake robot kn-keyword=snake robot en-keyword=crowded pipes kn-keyword=crowded pipes en-keyword=hybrid force-position control kn-keyword=hybrid force-position control en-keyword=sinusoidal curve kn-keyword=sinusoidal curve END start-ver=1.4 cd-journal=joma no-vol=30 cd-vols= no-issue=3 article-no= start-page=1342 end-page=1349 dt-received= dt-revised= dt-accepted= dt-pub-year=2019 dt-pub=20191126 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Robotic CT-guided out-of-plane needle insertion: comparison of angle accuracy with manual insertion in phantom and measurement of distance accuracy in animals en-subtitle= kn-subtitle= en-abstract= kn-abstract=Objectives
To evaluate the accuracy of robotic CT-guided out-of-plane needle insertion in phantom and animal experiments.
Methods
A robotic system (Zerobot), developed at our institution, was used for needle insertion. In the phantom experiment, 12 robotic needle insertions into a phantom at various angles in the XY and YZ planes were performed, and the same insertions were manually performed freehand, as well as guided by a smartphone application (SmartPuncture). Angle errors were compared between the robotic and smartphone-guided manual insertions using Studentfs t test. In the animal experiment, 6 robotic out-of-plane needle insertions toward targets of 1.0 mm in diameter placed in the kidneys and hip muscles of swine were performed, each with and without adjustment of needle orientation based on reconstructed CT images during insertion. Distance accuracy was calculated as the distance between the needle tip and the target center.
Results
In the phantom experiment, the mean angle errors of the robotic, freehand manual, and smartphone-guided manual insertions were 0.4, 7.0, and 3.7 in the XY plane and 0.6, 6.3, and 0.6 in the YZ plane, respectively. Robotic insertions in the XY plane were significantly (p < 0.001) more accurate than smartphone-guided insertions. In the animal experiment, the overall mean distance accuracy of robotic insertions with and without adjustment of needle orientation was 2.5 mm and 5.0 mm, respectively.
Conclusion
Robotic CT-guided out-of-plane needle insertions were more accurate than smartphone-guided manual insertions in the phantom and were also accurate in the in vivo procedure, particularly with adjustment during insertion. en-copyright= kn-copyright= en-aut-name=KomakiToshiyuki en-aut-sei=Komaki en-aut-mei=Toshiyuki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=HirakiTakao en-aut-sei=Hiraki en-aut-mei=Takao kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=KamegawaTetsushi en-aut-sei=Kamegawa en-aut-mei=Tetsushi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=MatsunoTakayuki en-aut-sei=Matsuno en-aut-mei=Takayuki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= en-aut-name=SakuraiJun en-aut-sei=Sakurai en-aut-mei=Jun kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=5 ORCID= en-aut-name=MatsuuraRyutaro en-aut-sei=Matsuura en-aut-mei=Ryutaro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=6 ORCID= en-aut-name=YamaguchiTakuya en-aut-sei=Yamaguchi en-aut-mei=Takuya kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=7 ORCID= en-aut-name=SasakiTakanori en-aut-sei=Sasaki en-aut-mei=Takanori kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=8 ORCID= en-aut-name=MitsuhashiToshiharu en-aut-sei=Mitsuhashi en-aut-mei=Toshiharu kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=9 ORCID= en-aut-name=OkamotoSoichiro en-aut-sei=Okamoto en-aut-mei=Soichiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=10 ORCID= en-aut-name=UkaMayu en-aut-sei=Uka en-aut-mei=Mayu kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=11 ORCID= en-aut-name=MatsuiYusuke en-aut-sei=Matsui en-aut-mei=Yusuke kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=12 ORCID= en-aut-name=IguchiToshihiro en-aut-sei=Iguchi en-aut-mei=Toshihiro kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=13 ORCID= en-aut-name=GobaraHideo en-aut-sei=Gobara en-aut-mei=Hideo kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=14 ORCID= en-aut-name=KanazawaSusumu en-aut-sei=Kanazawa en-aut-mei=Susumu kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=15 ORCID= affil-num=1 en-affil=Department of Radiology, Okayama University Medical School kn-affil= affil-num=2 en-affil=Department of Radiology, Okayama University Medical School kn-affil= affil-num=3 en-affil=Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University kn-affil= affil-num=4 en-affil=Graduate School of Natural Science and Technology, Okayama University kn-affil= affil-num=5 en-affil=Center for Innovative Clinical Medicine, Okayama University Hospital kn-affil= affil-num=6 en-affil=Graduate School of Health Sciences, Okayama University Medical School kn-affil= affil-num=7 en-affil=Division of Radiology, Department of Medical Technology, Okayama University Hospital kn-affil= affil-num=8 en-affil=Collaborative Research Center for OMIC, Okayama University Graduate School of Medicine, Dentistry, and Pharmaceutical Sciences kn-affil= affil-num=9 en-affil=Center for Innovative Clinical Medicine, Okayama University Hospital kn-affil= affil-num=10 en-affil=Department of Radiology, Okayama University Medical School kn-affil= affil-num=11 en-affil=Department of Radiology, Okayama University Medical School kn-affil= affil-num=12 en-affil=Department of Radiology, Okayama University Medical School kn-affil= affil-num=13 en-affil=Department of Radiology, Okayama University Medical School kn-affil= affil-num=14 en-affil=Division of Medical Informatics, Okayama University Hospital kn-affil= affil-num=15 en-affil=Department of Radiology, Okayama University Medical School kn-affil= en-keyword=Robotics kn-keyword=Robotics en-keyword=Interventional radiology kn-keyword=Interventional radiology en-keyword=Animal experiments kn-keyword=Animal experiments END start-ver=1.4 cd-journal=joma no-vol= cd-vols= no-issue= article-no= start-page= end-page= dt-received= dt-revised= dt-accepted= dt-pub-year=2019 dt-pub=20191119 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Development of a separable search-and-rescue robot composed of a mobile robot and a snake robot en-subtitle= kn-subtitle= en-abstract= kn-abstract= In this study, we propose a new robot system consisting of a mobile robot and a snake robot. The system works not only as a mobile manipulator but also as a multi-agent system by using the snake robot's ability to separate from the mobile robot. Initially, the snake robot is mounted on the mobile robot in the carrying mode. When an operator uses the snake robot as a manipulator, the robot changes to the manipulator mode. The operator can detach the snake robot from the mobile robot and command the snake robot to conduct lateral rolling motions. In this paper, we present the details of our robot and its performance in the World Robot Summit. en-copyright= kn-copyright= en-aut-name=KamegawaTetsushi en-aut-sei=Kamegawa en-aut-mei=Tetsushi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=AkiyamaTaichi en-aut-sei=Akiyama en-aut-mei=Taichi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=SakaiSatoshi en-aut-sei=Sakai en-aut-mei=Satoshi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= en-aut-name=FujiiKento en-aut-sei=Fujii en-aut-mei=Kento kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=4 ORCID= en-aut-name=UneKazushi en-aut-sei=Une en-aut-mei=Kazushi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=5 ORCID= en-aut-name=OuEitou en-aut-sei=Ou en-aut-mei=Eitou kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=6 ORCID= en-aut-name=MatsumuraYuto en-aut-sei=Matsumura en-aut-mei=Yuto kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=7 ORCID= en-aut-name=KishutaniToru en-aut-sei=Kishutani en-aut-mei=Toru kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=8 ORCID= en-aut-name=NoseEiji en-aut-sei=Nose en-aut-mei=Eiji kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=9 ORCID= en-aut-name=YoshizakiYusuke en-aut-sei=Yoshizaki en-aut-mei=Yusuke kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=10 ORCID= en-aut-name=GofukuAkio en-aut-sei=Gofuku en-aut-mei=Akio kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=11 ORCID= affil-num=1 en-affil=Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University, kn-affil= affil-num=2 en-affil=Graduate School of Natural Science and Technology, Okayama University kn-affil= affil-num=3 en-affil=Graduate School of Natural Science and Technology, Okayama University kn-affil= affil-num=4 en-affil=Graduate School of Natural Science and Technology, Okayama University kn-affil= affil-num=5 en-affil=Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University, kn-affil= affil-num=6 en-affil=Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University, kn-affil= affil-num=7 en-affil=Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University, kn-affil= affil-num=8 en-affil=Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University, kn-affil= affil-num=9 en-affil=Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University, kn-affil= affil-num=10 en-affil=Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University, kn-affil= affil-num=11 en-affil=Graduate School of Interdisciplinary Science and Engineering in Health Systems, Okayama University, kn-affil= en-keyword=Separable robot kn-keyword=Separable robot en-keyword=snake robot kn-keyword=snake robot en-keyword=mobile robot kn-keyword=mobile robot en-keyword=urban search-and-rescue kn-keyword=urban search-and-rescue en-keyword=multi-agent system kn-keyword=multi-agent system END start-ver=1.4 cd-journal=joma no-vol=1 cd-vols= no-issue= article-no= start-page=791 end-page=796 dt-received= dt-revised= dt-accepted= dt-pub-year=2003 dt-pub=20030914 dt-online= en-article= kn-article= en-subject= kn-subject= en-title= kn-title=Extended QDSEGA for Controlling Real Robot : Acquisition of Locomotion Patterns for Snake : like Robot en-subtitle= kn-subtitle= en-abstract= kn-abstract=

Reinforcement learning is very effective for robot learning. It is because it does not need prior knowledge and has higher capability of reactive and adaptive behaviors. In our previous works, we proposed new reinforce learning algorithm: "Q-learning with dynamic structuring of exploration space based on genetic algorithm (QDSEGA)". It is designed for complicated systems with large action-state space like a robot with many redundant degrees of freedom. However the application of QDSEGA is restricted to static systems. A snake-like robot has many redundant degrees of freedom and the dynamics of the system are very important to complete the locomotion task. So application of usual reinforcement learning is very difficult. In this paper, we extend layered structure of QDSEGA so that it becomes possible to apply it to real robots that have complexities and dynamics. We apply it to acquisition of locomotion pattern of the snake-like robot and demonstrate the effectiveness and the validity of QDSEGA with the extended layered structure by simulation and experiment.

en-copyright= kn-copyright= en-aut-name=ItoKazuyuki en-aut-sei=Ito en-aut-mei=Kazuyuki kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=1 ORCID= en-aut-name=KamegawaTetsushi en-aut-sei=Kamegawa en-aut-mei=Tetsushi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=2 ORCID= en-aut-name=MatsunoFumitoshi en-aut-sei=Matsuno en-aut-mei=Fumitoshi kn-aut-name= kn-aut-sei= kn-aut-mei= aut-affil-num=3 ORCID= affil-num=1 en-affil= kn-affil=Okayama University affil-num=2 en-affil= kn-affil=Tokyo Institute of Technology affil-num=3 en-affil= kn-affil=Tokyo Institute of Technology en-keyword=genetic algorithms kn-keyword=genetic algorithms en-keyword= learning (artificial intelligence) kn-keyword= learning (artificial intelligence) en-keyword=mobile robots kn-keyword=mobile robots en-keyword=motion control kn-keyword=motion control en-keyword=robot dynamics kn-keyword=robot dynamics en-keyword=robot kinematics kn-keyword=robot kinematics END