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 Student’s 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