광섬유 힘 센서가 내장된 로봇 원격 및 무인 조작을 위한 모듈화 로봇 스킨

학위논문(석사) -- 서울대학교대학원 : 공과대학 기계공학부, 2021.8. 박용래.Robots have been used to replace human workers for dangerous and difficult tasks that require human-like dexterity. To perform sophisticated tasks, force and tactile sensing is one of the key requirements to achieve dexterous manipulation. Robots equipped with sensitive skin that can play a role of mechanoreception in animals will be able to perform tasks with high levels of dexterity. In this research, we propose modularized robotic skin that is capable of not only localizing external contacts but also estimating the magnitudes of the contact forces. In order to acquire three pieces of key information on a contact, such as contact locations in horizontal and vertical directions and the magnitude of the force, each skin module requires three degrees of freedom in sensing. In the proposed skin, force sensing is achieved by a custom-designed triangular beam structure. A force applied to the outer surface of the skin module is transmitted to the beam structure underneath, and bending of the beam is detected by fiber optic strain sensors, called fiber Bragg gratings. The proposed skin shows resolutions of 1.45 N for force estimation and 1.85 mm and 1.91 mm for contact localization in horizontal and vertical directions, respectively. We also demonstrate applications of the proposed skin for remote and autonomous operations of commercial robotic arms equipped with an array of the skin modules.로봇은 인간과 같은 높은 조작성이 필요한 어려운 작업 환경이나 위험한 환경에서 인간을 대체할 수 있도록 연구되고 있다. 이를 위해 동물의 기계적 감응(mechanoreception) 역할과 같은 기능을 수행하면서 로봇에 부착될 수 있는 스킨을 연구하고 있고, 민감한 로봇 스킨이 부착된 로봇은 높은 수준의 조작성을 가지고 주어진 작업을 성공할 수 있다. 다시 말해 로봇의 힘 센싱과 촉각 센싱 기능은 정교한 로봇 조작의 핵심 요소들 중 하나로 로봇의 세밀한 작업들을 수행하기 필요로 하다. 따라서 우리는 이 연구에서 외부 접촉의 위치뿐만 아니라 외력의 크기도 추정할 수 있는 모듈화된 로봇 스킨을 제안한다. 접촉 힘의 크기, 접촉의 수직 및 수평 위치 등 접촉에 대한 3가지 정보를 얻기 위해서 각 스킨 모듈은 3 자유도를 가지도록 설계하였다. 제안한 스킨에서 힘 센싱은 새롭게 설계한 삼각형 형태의 빔 구조의 변형을 통해서 측정한다. 구체적으로 스킨 모듈의 외피에 가해진 힘은 빔 구조로 전달되고, 이로 인해 발생하는 빔 구조의 변형은 “fiber Bragg gratings” 이라고 불리는 광섬유 스트레인 센서들에 의해서 측정된다. 제안한 스킨은 1.45 N의 힘 추정 해상도를 가지고, 수평 및 수직 위치 추정은 각각 1.85 mm와 1.91 mm의 해상도를 가진다. 우리는 상용화된 로봇팔에 여러 개의 스킨 모듈을 배열 및 부착하여 로봇의 원격 조작 및 무인 조작을 실행하였고, 스킨의 활용성을 검증하였다.1. Introduction 1 2. Design 7 2.1. Skin Module . 2.2. Skin Array . 3. Modeling 12 3.1. FBG Sensing Principle and Temperature Compensation 25 3.2. Estimation of Beam Force and Deflection . 3.3. Estimation of Spring Force . 3.4. Estimation of Contact Locations and Force . 4. Experiments 25 4.1. Experimental Setup . 4.2. Initialization . 4.3. Parameter Optimization . 4.4. Result . 5. Application 32 5.1. Remote Robot Manipulation . 5.2. Autonomous Robot Control . 6. Discussion 46 7. Conclusion 48 8. Appendix 49 8.1. Beam Deflection . Bibliography 52 Abstract in Korean 60석

Bimanual robot control for surface treatment tasks

This work develops a method to perform surface treatment tasks using a bimanual robotic system, i.e. two robot arms cooperatively performing the task. In particular, one robot arm holds the workpiece while the other robot arm has the treatment tool attached to its end-effector. Moreover, the human user teleoperates all the six coordinates of the former robot arm and two coordinates of the latter robot arm, i.e. the teleoperator can move the treatment tool on the plane given by the workpiece surface. Furthermore, a force sensor attached to the treatment tool is used to automatically attain the desired pressure between the tool and the workpiece and to automatically keep the tool orientation orthogonal to the workpiece surface. In addition, to assist the human user during the teleoperation, several constraints are defined for both robot arms in order to avoid exceeding the allowed workspace, e.g. to avoid collisions with other objects in the environment. The theory used in this work to develop the bimanual robot control relies on sliding mode control and task prioritisation. Finally, the feasibility and effectiveness of the method are shown through experimental results using two robot arms

Bimanual robot control for surface treatment tasks

This is an Author's Accepted Manuscript of an article published in Alberto García, J. Ernesto Solanes, Luis Gracia, Pau Muñoz-Benavent, Vicent Girbés-Juan & Josep Tornero (2022) Bimanual robot control for surface treatment tasks, International Journal of Systems Science, 53:1, 74-107, DOI: 10.1080/00207721.2021.1938279 [copyright Taylor & Francis], available online at: http://www.tandfonline.com/10.1080/00207721.2021.1938279[EN] This work develops a method to perform surface treatment tasks using a bimanual robotic system, i.e. two robot arms cooperatively performing the task. In particular, one robot arm holds the work-piece while the other robot arm has the treatment tool attached to its end-effector. Moreover, the human user teleoperates all the six coordinates of the former robot arm and two coordinates of the latter robot arm, i.e. the teleoperator can move the treatment tool on the plane given by the work- piece surface. Furthermore, a force sensor attached to the treatment tool is used to automatically attain the desired pressure between the tool and the workpiece and to automatically keep the tool orientation orthogonal to the workpiece surface. In addition, to assist the human user during the teleoperation, several constraints are defined for both robot arms in order to avoid exceeding the allowed workspace, e.g. to avoid collisions with other objects in the environment. The theory used in this work to develop the bimanual robot control relies on sliding mode control and task prioritisation. Finally, the feasibility and effectiveness of the method are shown through experimental results using two robot arms.This work was supported by Generalitat Valenciana [grant numbers ACIF/2019/007 and GV/2021/181] and Spanish Ministry of Science and Innovation [grant number PID2020117421RB-C21].García-Fernández, A.; Solanes, JE.; Gracia Calandin, LI.; Muñoz-Benavent, P.; Girbés-Juan, V.; Tornero, J. (2022). Bimanual robot control for surface treatment tasks. International Journal of Systems Science. 53(1):74-107. https://doi.org/10.1080/00207721.2021.19382797410753

On the role of wearable haptics for force feedback in teleimpedance control for dual-arm robotic teleoperation

Robotic teleoperation enables humans to safely complete exploratory procedures in remote locations for applications such as deep sea exploration or building assessments following natural disasters. Successful task completion requires meaningful dual arm robotic coordination and proper understanding of the environment. While these capabilities are inherent to humans via impedance regulation and haptic interactions, they can be challenging to achieve in telerobotic systems. Teleimpedance control has allowed impedance regulation in such applications, and bilateral teleoperation systems aim to restore haptic sensation to the operator, though often at the expense of stability or workspace size. Wearable haptic devices have the potential to apprise the operator of key forces during task completion while maintaining stability and transparency. In this paper, we evaluate the impact of wearable haptics for force feedback in teleimpedance control for dual-arm robotic teleoperation. Participants completed a peg-in-hole, box placement task, aiming to seat as many boxes as possible within the trial period. Experiments were conducted both transparent and opaque boxes. With the opaque box, participants achieved a higher number of successful placements with haptic feedback, and we saw higher mean interaction forces. Results suggest that the provision of wearable haptic feedback may increase confidence when visual cues are obscured

Artificial Intelligence based Robotic Platforms for Autonomous Precision Agriculture

Robotic applications are continuously expanding into every aspect of human livelihood, it becomes paramount to leverage this trend for precision agriculture. The agricultural sector despite being an important sector for human is slowly evolving in terms of technology. Crude and manual processes which are conventionally used for agriculture have severe economic and social impacts. The inefficiencies and less productiveness of these methods results to food wastage amidst food shortage, inconsistencies, time consumption, higher labour expenses, and low yield. The world will benefit from automating the processes in agriculture. In bid of addressing such, it becomes necessary to build on existing platforms and develop intelligent autonomous vehicles for precision agriculture. This should include development of intelligent drones for precision agriculture, development of intelligent ground robots for precision agriculture, and other systems working cooperatively. To achieve this, we leverage on Artificial Intelligence (AI) and mathematical methods to impact sufficient intelligence on robotic platforms to make them suitable for precision agriculture. This thesis explores the capabilities of AI for weed classification and detection, weed relative position estimation, fruit 6D pose estimation and virtual reality for teleoperated systems in fruit picking. Infestation of weeds diminishes the yield of crops in agriculture. Deep learning is becoming a more popular approach for identifying weeds on farmlands. However, precision agriculture requires that the object of interest (weed) is precisely classified and detected to facilitate removal or spraying. An approach for this is presented and involves cascading a classification network (ResNet-50) with a detection network (YOLO) for weed classification and detection which we termed Fused-YOLO. Thus, weeds can precisely be located and classified (type) within an image frame. Inspired by the precision of this detection model, the work extends to presenting a novel monocular vision-based approach for drones to detect multiple types of weeds and estimate their positions autonomously for precision agriculture applications. A drone is subjected to an elliptical trajectory while acquiring images from an onboard monecular camera. The images are fed to the fused-YOLO model in real-time. The centre of the detection bounding boxes is leveraged to be the centre of the detected object of interest (weeds). The centre pixels are extracted and converted into world coordinates forming azimuth and elevation angles from the target to the UAV and are effectively used in an estimation scheme that adopts the Unscented Kalman Filteration to estimate the exact relative positions of the weeds. The robustness of this algorithm allows for both indoor and outdoor implementation while achieving a competitive result with affordable off-the-shelf sensors. Artificial intelligence for autonomous 6D pose estimation has valuable contributions to agricultural practices rallying around fruit picking, harvesting, remote operations and other contact-related applications. Conventionally, Convolutional Neural Networks (CNNs) based approaches are adopted for pose estimation. However, precision agriculture applications are demanding on higher accuracy at lower computational costs for real-time applications. Motivated by this, a novel architecture called Transpose is proposed based on transformers. TransPose is an improved Transformer-based 6D pose estimation with a depth refinement. More modalities often result in higher accuracy at the expense of computational cost. TransPose takes in a single RGB image as input without extra modality. However, an innovative light-weight depth estimation network architecture is incorporated into the model to estimate depth from an RGB image using a feature pyramid with an up-sampling method. A transformer model having proven to be efficient, regress the 6D pose directly and also outputs object patches. The depth and the patches are utilised to further refine the regressed 6D pose. The performance of the model is extensively assessed and compared with state-of-the-art methods. As part of this research, a first-ever fruit-oriented 6D pose dataset was acquired. Lastly, a seamless teleoperation pipeline that interfaces virtual reality with robots for precision agriculture tasks is proposed to pave the way for virtual agriculture. This utilises the Transpose model to estimate the 6D pose of a fruit and render it in a virtual reality environment. A robotic manipulator is which is then controlled from within the virtual reality environment to pick/harvest the fruit while being guided by the Transpose AI model. The robustness of the pipeline is tested over simulation and real-time implementation with a physical robotic manipulator is also investigated

Combining haptics and inertial motion capture to enhance remote control of a dual-arm robot

[EN] High dexterity is required in tasks in which there is contact between objects, such as surface conditioning (wiping, polishing, scuffing, sanding, etc.), specially when the location of the objects involved is unknown or highly inaccurate because they are moving, like a car body in automotive industry lines. These applications require the human adaptability and the robot accuracy. However, sharing the same workspace is not possible in most cases due to safety issues. Hence, a multi-modal teleoperation system combining haptics and an inertial motion capture system is introduced in this work. The human operator gets the sense of touch thanks to haptic feedback, whereas using the motion capture device allows more naturalistic movements. Visual feedback assistance is also introduced to enhance immersion. A Baxter dual-arm robot is used to offer more flexibility and manoeuvrability, allowing to perform two independent operations simultaneously. Several tests have been carried out to assess the proposed system. As it is shown by the experimental results, the task duration is reduced and the overall performance improves thanks to the proposed teleoperation method.This research was funded by Generalitat Valenciana (Grants GV/2021/074 and GV/2021/181) and by the SpanishGovernment (Grants PID2020-118071GB-I00 and PID2020-117421RBC21 funded by MCIN/AEI/10.13039/501100011033). This work was also supported byCoordenacao de Aperfeiaoamento de Pessoal de Nivel Superior (CAPES Brasil) under Finance Code 001, by CEFET-MG, and by a Royal Academy of Engineering Chair in Emerging Technologies to YD.Girbés-Juan, V.; Schettino, V.; Gracia Calandin, LI.; Solanes, JE.; Demiris, Y.; Tornero, J. (2022). Combining haptics and inertial motion capture to enhance remote control of a dual-arm robot. Journal on Multimodal User Interfaces. 16(2):219-238. https://doi.org/10.1007/s12193-021-00386-8219238162Hägele M, Nilsson K, Pires JN, Bischoff R (2016) Industrial robotics. Springer, Cham, pp 1385–1422. https://doi.org/10.1007/978-3-319-32552-1_54Hokayem PF, Spong MW (2006) Bilateral teleoperation: an historical survey. Automatica 42(12):2035–2057. https://doi.org/10.1016/j.automatica.2006.06.027Son HI (2019) The contribution of force feedback to human performance in the teleoperation of multiple unmanned aerial vehicles. J Multimodal User Interfaces 13(4):335–342Jones B, Maiero J, Mogharrab A, Aguliar IA, Adhikari A, Riecke BE, Kruijff E, Neustaedter C, Lindeman RW (2020) Feetback: augmenting robotic telepresence with haptic feedback on the feet. In: Proceedings of the 2020 international conference on multimodal interaction, pp 194–203Merrad W, Héloir A, Kolski C, Krüger A (2021) Rfid-based tangible and touch tabletop for dual reality in crisis management context. J Multimodal User Interfaces. https://doi.org/10.1007/s12193-021-00370-2Schettino V, Demiris Y (2019) Inference of user-intention in remote robot wheelchair assistance using multimodal interfaces. In: 2019 IEEE/RSJ international conference on intelligent robots and systems (IROS). IEEE, pp 4600–4606Casper J, Murphy RR (2003) Human–robot interactions during the robot-assisted urban search and rescue response at the world trade center. IEEE Trans Syst Man Cybern Part B (Cybern) 33(3):367–385. https://doi.org/10.1109/TSMCB.2003.811794Chen JY (2010) UAV-guided navigation for ground robot tele-operation in a military reconnaissance environment. Ergonomics 53(8):940–950. https://doi.org/10.1080/00140139.2010.500404 (pMID: 20658388.)Aleotti J, Micconi G, Caselli S, Benassi G, Zambelli N, Bettelli M, Calestani D, Zappettini A (2019) Haptic teleoperation of UAV equipped with gamma-ray spectrometer for detection and identification of radio-active materials in industrial plants. In: Tolio T, Copani G, Terkaj W (eds) Factories of the future: the Italian flagship initiative. Springer, Cham, pp 197–214. https://doi.org/10.1007/978-3-319-94358-9_9Santos Carreras L (2012) Increasing haptic fidelity and ergonomics in teleoperated surgery. PhD Thesis, EPFL, Lausanne, pp 1–188. https://doi.org/10.5075/epfl-thesis-5412Hatzfeld C, Neupert C, Matich S, Braun M, Bilz J, Johannink J, Miller J, Pott PP, Schlaak HF, Kupnik M, Werthschützky R, Kirschniak A (2017) A teleoperated platform for transanal single-port surgery: ergonomics and workspace aspects. In: IEEE world haptics conference (WHC), pp 1–6. https://doi.org/10.1109/WHC.2017.7989847Burns JO, Mellinkoff B, Spydell M, Fong T, Kring DA, Pratt WD, Cichan T, Edwards CM (2019) Science on the lunar surface facilitated by low latency telerobotics from a lunar orbital platform-gateway. Acta Astronaut 154:195–203. https://doi.org/10.1016/j.actaastro.2018.04.031Sivčev S, Coleman J, Omerdić E, Dooly G, Toal D (2018) Underwater manipulators: a review. Ocean Eng 163:431–450. https://doi.org/10.1016/j.oceaneng.2018.06.018Abich J, Barber DJ (2017) The impact of human–robot multimodal communication on mental workload, usability preference, and expectations of robot behavior. J Multimodal User Interfaces 11(2):211–225. https://doi.org/10.1007/s12193-016-0237-4Hong A, Lee DG, Bülthoff HH, Son HI (2017) Multimodal feedback for teleoperation of multiple mobile robots in an outdoor environment. J Multimodal User Interfaces 11(1):67–80. https://doi.org/10.1007/s12193-016-0230-yKatyal KD, Brown CY, Hechtman SA, Para MP, McGee TG, Wolfe KC, Murphy RJ, Kutzer MDM, Tunstel EW, McLoughlin MP, Johannes MS (2014) Approaches to robotic teleoperation in a disaster scenario: from supervised autonomy to direct control. In: IEEE/RSJ international conference on intelligent robots and systems, pp 1874–1881. https://doi.org/10.1109/IROS.2014.6942809Niemeyer G, Preusche C, Stramigioli S, Lee D (2016) Telerobotics. Springer, Cham, pp 1085–1108. https://doi.org/10.1007/978-3-319-32552-1_43Li J, Li Z, Hauser K (2017) A study of bidirectionally telepresent tele-action during robot-mediated handover. In: Proceedings—IEEE international conference on robotics and automation, pp 2890–2896. https://doi.org/10.1109/ICRA.2017.7989335Peng XB, Kanazawa A, Malik J, Abbeel P, Levine S (2018) Sfv: reinforcement learning of physical skills from videos. ACM Trans. Graph. 37(6):178:1-178:14. https://doi.org/10.1145/3272127.3275014Coleca F, State A, Klement S, Barth E, Martinetz T (2015) Self-organizing maps for hand and full body tracking. Neurocomputing 147: 174–184. Advances in self-organizing maps subtitle of the special issue: selected papers from the workshop on self-organizing maps 2012 (WSOM 2012). https://doi.org/10.1016/j.neucom.2013.10.041Von Marcard T, Rosenhahn B, Black MJ, Pons-Moll G (2017) Sparse inertial poser: automatic 3d human pose estimation from sparse Imus. In: Computer graphics forum, vol 36. Wiley, pp 349–360Zhao J (2018) A review of wearable IMU (inertial-measurement-unit)-based pose estimation and drift reduction technologies. J Phys Conf Ser 1087:042003. https://doi.org/10.1088/1742-6596/1087/4/042003Malleson C, Gilbert A, Trumble M, Collomosse J, Hilton A, Volino M (2018) Real-time full-body motion capture from video and IMUs. In: Proceedings—2017 international conference on 3D vision, 3DV 2017 (September), pp 449–457. https://doi.org/10.1109/3DV.2017.00058Du G, Zhang P, Mai J, Li Z (2012) Markerless kinect-based hand tracking for robot teleoperation. Int J Adv Robot Syst 9(2):36. https://doi.org/10.5772/50093Çoban M, Gelen G (2018) Wireless teleoperation of an industrial robot by using myo arm band. In: International conference on artificial intelligence and data processing (IDAP), pp 1–6. https://doi.org/10.1109/IDAP.2018.8620789Lipton JI, Fay AJ, Rus D (2018) Baxter’s homunculus: virtual reality spaces for teleoperation in manufacturing. IEEE Robot Autom Lett 3(1):179–186. https://doi.org/10.1109/LRA.2017.2737046Zhang T, McCarthy Z, Jow O, Lee D, Chen X, Goldberg K, Abbeel P (2018) Deep imitation learning for complex manipulation tasks from virtual reality teleoperation. In: IEEE international conference on robotics and automation (ICRA), pp 5628–5635. https://doi.org/10.1109/ICRA.2018.8461249Hannaford B, Okamura AM (2016) Haptics. Springer, Cham, pp 1063–1084. https://doi.org/10.1007/978-3-319-32552-1_42Rodríguez J-L, Velàzquez R (2012) Haptic rendering of virtual shapes with the Novint Falcon. Proc Technol 3:132–138. https://doi.org/10.1016/J.PROTCY.2012.03.014Teklemariam HG, Das AK (2017) A case study of phantom omni force feedback device for virtual product design. Int J Interact Des Manuf (IJIDeM) 11(4):881–892. https://doi.org/10.1007/s12008-015-0274-3Karbasizadeh N, Zarei M, Aflakian A, Masouleh MT, Kalhor A (2018) Experimental dynamic identification and model feed-forward control of Novint Falcon haptic device. Mechatronics 51:19–30. https://doi.org/10.1016/j.mechatronics.2018.02.013Georgiou T, Demiris Y (2017) Adaptive user modelling in car racing games using behavioural and physiological data. User Model User-Adapted Interact 27(2):267–311. https://doi.org/10.1007/s11257-017-9192-3Son HI (2019) The contribution of force feedback to human performance in the teleoperation of multiple unmanned aerial vehicles. J Multimodal User Interfaces 13(4):335–342. https://doi.org/10.1007/s12193-019-00292-0Ramírez-Fernández C, Morán AL, García-Canseco E (2015) Haptic feedback in motor hand virtual therapy increases precision and generates less mental workload. In: 2015 9th international conference on pervasive computing technologies for healthcare (PervasiveHealth), pp 280–286. https://doi.org/10.4108/icst.pervasivehealth.2015.260242Saito Y, Raksincharoensak P (2019) Effect of risk-predictive haptic guidance in one-pedal driving mode. Cognit Technol Work 21(4):671–684. https://doi.org/10.1007/s10111-019-00558-3Girbés V, Armesto L, Dols J, Tornero J (2016) Haptic feedback to assist bus drivers for pedestrian safety at low speed. IEEE Trans Haptics 9(3):345–357. https://doi.org/10.1109/TOH.2016.2531686Girbés V, Armesto L, Dols J, Tornero J (2017) An active safety system for low-speed bus braking assistance. IEEE Trans Intell Transp Syst 18(2):377–387. https://doi.org/10.1109/TITS.2016.2573921Escobar-Castillejos D, Noguez J, Neri L, Magana A, Benes B (2016) A review of simulators with haptic devices for medical training. J Med Syst 40(4):104. https://doi.org/10.1007/s10916-016-0459-8Coles TR, Meglan D, John NW (2011) The role of haptics in medical training simulators: a survey of the state of the art. IEEE Trans Haptics 4(1):51–66. https://doi.org/10.1109/TOH.2010.19Okamura AM, Verner LN, Reiley CE, Mahvash M (2010) Haptics for robot-assisted minimally invasive surgery. In: Kaneko M, Nakamura Y (eds) Robotics research. Springer tracts in advanced robotics, vol 66. Springer, Berlin, pp 361–372. https://doi.org/10.1007/978-3-642-14743-2_30Ehrampoosh S, Dave M, Kia MA, Rablau C, Zadeh MH (2013) Providing haptic feedback in robot-assisted minimally invasive surgery: a direct optical force-sensing solution for haptic rendering of deformable bodies. Comput Aided Surg 18(5–6):129–141. https://doi.org/10.3109/10929088.2013.839744Ju Z, Yang C, Li Z, Cheng L, Ma H (2014) Teleoperation of humanoid Baxter robot using haptic feedback. In: 2014 international conference on multisensor fusion and information integration for intelligent systems (MFI). IEEE, pp 1–6. https://doi.org/10.1109/MFI.2014.6997721Clark JP, Lentini G, Barontini F, Catalano MG, Bianchi M, O’Malley MK (2019) On the role of wearable haptics for force feedback in teleimpedance control for dual-arm robotic teleoperation. In: International conference on robotics and automation (ICRA), pp 5187–5193. https://doi.org/10.1109/ICRA.2019.8793652Gracia L, Solanes JE, Muñoz-Benavent P, Miro JV, Perez-Vidal C, Tornero J (2018) Adaptive sliding mode control for robotic surface treatment using force feedback. Mechatronics 52:102–118. https://doi.org/10.1016/j.mechatronics.2018.04.008Zhu D, Xu X, Yang Z, Zhuang K, Yan S, Ding H (2018) Analysis and assessment of robotic belt grinding mechanisms by force modeling and force control experiments. Tribol Int 120:93–98. https://doi.org/10.1016/j.triboint.2017.12.043Smith C, Karayiannidis Y, Nalpantidis L, Gratal X, Qi P, Dimarogonas DV, Kragic D (2012) Dual arm manipulation—a survey. Robot Auton Syst 60(10):1340–1353. https://doi.org/10.1016/j.robot.2012.07.005Girbés-Juan V, Schettino V, Demiris Y, Tornero J (2021) Haptic and visual feedback assistance for dual-arm robot teleoperation in surface conditioning tasks. IEEE Trans Haptics 14(1):44–56. https://doi.org/10.1109/TOH.2020.3004388Tunstel EW Jr, Wolfe KC, Kutzer MD, Johannes MS, Brown CY, Katyal KD, Para MP, Zeher MJ (2013) Recent enhancements to mobile bimanual robotic teleoperation with insight toward improving operator control. Johns Hopkins APL Tech Digest 32(3):584García A, Solanes JE, Gracia L, Muñoz-Benavent P, Girbés-Juan V, Tornero J (2021) Bimanual robot control for surface treatment tasks. Int J Syst Sci. https://doi.org/10.1080/00207721.2021.1938279Jasim IF, Plapper PW, Voos H (2014) Position identification in force-guided robotic peg-in-hole assembly tasks. Proc CIRP 23((C)):217–222. https://doi.org/10.1016/j.procir.2014.10.077Song HC, Kim YL, Song JB (2016) Guidance algorithm for complex-shape peg-in-hole strategy based on geometrical information and force control. Adv Robot 30(8):552–563. https://doi.org/10.1080/01691864.2015.1130172Kramberger A, Gams A, Nemec B, Chrysostomou D, Madsen O, Ude A (2017) Generalization of orientation trajectories and force-torque profiles for robotic assembly. Robot Auton Syst 98:333–346. https://doi.org/10.1016/j.robot.2017.09.019Pliego-Jiménez J, Arteaga-Pérez MA (2015) Adaptive position/force control for robot manipulators in contact with a rigid surface with unknown parameters. In: European control conference (ECC), pp 3603–3608. https://doi.org/10.1109/ECC.2015.7331090Gierlak P, Szuster M (2017) Adaptive position/force control for robot manipulator in contact with a flexible environment. Robot Auton Syst 95:80–101. https://doi.org/10.1016/j.robot.2017.05.015Solanes JE, Gracia L, Muñoz-Benavent P, Miro JV, Girbés V, Tornero J (2018) Human–robot cooperation for robust surface treatment using non-conventional sliding mode control. ISA Trans 80:528–541. https://doi.org/10.1016/j.isatra.2018.05.013Ravandi AK, Khanmirza E, Daneshjou K (2018) Hybrid force/position control of robotic arms manipulating in uncertain environments based on adaptive fuzzy sliding mode control. Appl Soft Comput 70:864–874. https://doi.org/10.1016/j.asoc.2018.05.048Solanes JE, Gracia L, Muñoz-Benavent P, Esparza A, Miro JV, Tornero J (2018) Adaptive robust control and admittance control for contact-driven robotic surface conditioning. Robot Comput Integr Manuf 54:115–132. https://doi.org/10.1016/j.rcim.2018.05.003Perez-Vidal C, Gracia L, Sanchez-Caballero S, Solanes JE, Saccon A, Tornero J (2019) Design of a polishing tool for collaborative robotics using minimum viable product approach. Int J Comput Integr Manuf 32(9):848–857. https://doi.org/10.1080/0951192X.2019.1637026Chen F, Zhao H, Li D, Chen L, Tan C, Ding H (2019) Contact force control and vibration suppression in robotic polishing with a smart end effector. Robot Comput Integr Manuf 57:391–403. https://doi.org/10.1016/j.rcim.2018.12.019Mohammad AEK, Hong J, Wang D, Guan Y (2019) Synergistic integrated design of an electrochemical mechanical polishing end-effector for robotic polishing applications. Robot Comput Integr Manuf 55:65–75. https://doi.org/10.1016/j.rcim.2018.07.005Waldron KJ, Schmiedeler J (2016) Kinematics. Springer, Cham, pp 11–36. https://doi.org/10.1007/978-3-319-32552-1_2Featherstone R, Orin DE (2016) Dynamics. Springer, Cham, pp 37–66. https://doi.org/10.1007/978-3-319-32552-1_3Wen K, Necsulescu D, Sasiadek J (2008) Haptic force control based on impedance/admittance control aided by visual feedback. Multimed Tools Appl 37(1):39–52. https://doi.org/10.1007/s11042-007-0172-1Tzafestas C, Velanas S, Fakiridis G (2008) Adaptive impedance control in haptic teleoperation to improve transparency under time-delay. In: IEEE international conference on robotics and automation, pp 212–219. https://doi.org/10.1109/ROBOT.2008.4543211Chiaverini S, Oriolo G, Maciejewski AA (2016) Redundant robots. Springer, Cham, pp 221–242. https://doi.org/10.1007/978-3-319-32552-1_10Ogata K (1987) Discrete-time control systems. McGraw-Hill, New YorkGarcía A, Girbés-Juan V, Solanes JE, Gracia L, Perez-Vidal C, Tornero J (2020) Human–robot cooperation for surface repair combining automatic and manual modes. IEEE Access 8:154024–154035. https://doi.org/10.1109/ACCESS.2020.301450