Similar articles

Abstract: It is challenging for underwater vehicle–;manipulator systems (UVMSs) to operate autonomously in unstructured underwater environments. Relying solely on teleoperation for both underwater vehicle (UV) and underwater manipulator (UM) imposes a considerable cognitive and physical load on the operator. In this paper, we propose a unified shared control (USC) architecture for the UVMS, integrating divisible shared control (DSC) and interactive shared control (ISC) to alleviate the operator’s workload. By applying task priority based on DSC, we divide the whole-body task into constraints, operation, and posture optimization subtasks. The robot autonomously avoids self-collisions and adjusts its posture according to the user’s visual preferences. ISC incorporates haptic feedback to enhance human–;robot collaboration, seamlessly integrating it into the operation task via a whole-body controller for the UVMS. Simulations and pool experiments are conducted to verify the feasibility of the method. Compared to manual control (MC), the proposed method reduces completion time by 17.50%, operator input length by 25.00%, and cognitive load by 35.53% in the simulations, with corresponding reductions of 22.73%, 40.00%, and 29.91% in the pool experiments. Subjective measurements demonstrate the reduction in operator workload with the proposed method.

基于任务优先级的水下机器人—机械手系统统一共享控制架构

[1]Birk A, Doernbach T, Mueller C, et al., 2018. Dexterous underwater manipulation from onshore locations: streamlining efficiencies for remotely operated underwater vehicles. IEEE Robot Autom Mag, 25(4):24-33.

[2]Brantner G, Khatib O, 2021. Controlling Ocean One: human–robot collaboration for deep-sea manipulation. J Field Robot, 38(1):28-51.

[3]Capocci R, Omerdic E, Dooly G, et al., 2018. Fault-tolerant control for ROVs using control reallocation and power isolation. J Mar Sci Eng, 6(2):40.

[4]Cieślak P, Ridao P, 2018. Adaptive admittance control in task-priority framework for contact force control in autonomous underwater floating manipulation. Proc IEEE/RSJ Int Conf on Intelligent Robots and Systems, p.6646-6651.

[5]Cieślak P, Ridao P, Giergiel M, 2015. Autonomous underwater panel operation by GIRONA500 UVMS: a practical approach to autonomous underwater manipulation. Proc IEEE Int Conf on Robotics and Automation, p.529-536.

[6]Di Lillo P, Simetti E, Wanderlingh F, et al., 2021. Underwater intervention with remote supervision via satellite communication: developed control architecture and experimental results within the DexROV Project. IEEE Trans Contr Syst Technol, 29(1):108-123.

[7]Dragan AD, Srinivasa SS, 2013. A policy-blending formalism for shared control. Int J Robot Res, 32(7):790-805.

[8]Hart SG, 2006. NASA-task load index (NASA-TLX); 20 years later. Proc Hum Factors Ergon Soc Annu Meet, 50(9):904-908.

[9]Javdani S, Srinivasa SS, Bagnell JA, 2015. Shared autonomy via hindsight optimization.

[10]Khatib O, Yeh X, Brantner G, et al., 2016. Ocean One: a robotic avatar for oceanic discovery. IEEE Robot Autom Mag, 23(4):20-29.

[11]Lin MW, Yang CJ, 2020. Ocean observation technologies: a review. Chin J Mech Eng, 33(1):32.

[12]Lynch KM, Park FC, 2017. Modern Robotics. Cambridge University Press, Cambridge, UK.

[13]Manhães MMM, Scherer SA, Voss M, et al., 2016. UUV simulator: a Gazebo-based package for underwater intervention and multi-robot simulation. Proc OCEANS MTS/IEEE Monterey, p.1-8.

[14]Manley JE, Halpin S, Radford N, et al., 2018. Aquanaut: a new tool for subsea inspection and intervention. Proc OCEANS MTS/IEEE Charleston, p.1-4.

[15]Marchand E, Spindler F, Chaumette F, 2005. ViSP for visual servoing: a generic software platform with a wide class of robot control skills. IEEE Robot Autom Mag, 12(4):40-52.

[16]Maurelli F, Carreras M, Salvi J, et al., 2016. The PANDORA project: a success story in AUV autonomy. Oceans, p.1-8.

[17]Nicolis D, Palumbo M, Zanchettin AM, et al., 2018. Occlusion-free visual servoing for the shared autonomy teleoperation of dual-arm robots. IEEE Robot Autom Lett, 3(2):796-803.

[18]Petillot YR, Antonelli G, Casalino G, et al., 2019. Underwater robots: from remotely operated vehicles to intervention-autonomous underwater vehicles. IEEE Robot Autom Mag, 26(2):94-101.

[19]Qin T, Li PL, Shen SJ, 2018. VINS-Mono: a robust and versatile monocular visual-inertial state estimator. IEEE Trans Robot, 34(4):1004-1020.

[20]Rakita D, Mutlu B, Gleicher M, 2018. An autonomous dynamic camera method for effective remote teleoperation. Proc ACM/IEEE Int Conf on Human–Robot Interaction, p.325-333.

[21]Ribas D, Ridao P, Turetta A, et al., 2015. I-AUV mechatronics integration for the TRIDENT FP7 project. IEEE/ASME Trans Mechatron, 20(5):2583-2592.

[22]Ridao P, Carreras M, Ribas D, et al., 2015. Intervention AUVs: the next challenge. Annu Rev Cont, 40:227-241.

[23]Sahoo A, Dwivedy SK, Robi PS, 2019. Advancements in the field of autonomous underwater vehicle. Ocean Eng, 181:145-160.

[24]Samuel CMT, Tee KP, 2019. Unified human–robot shared control with application to haptic telemanipulation. Proc IEEE/RSJ Int Conf on Intelligent Robots and Systems, p.2221-2226.

[25]Scheurer C, Fiore MD, Sharma S, et al., 2016. Industrial implementation of a multi-task redundancy resolution at velocity level for highly redundant mobile manipulators. 47^th Int Symp on Robotics, p.1-9.

[26]Shim H, Jun BH, Lee PM, et al., 2010. Workspace control system of underwater tele-operated manipulators on an ROV. Ocean Eng, 37(11-12):1036-1047.

[27]Siciliano B, Slotine JJE, 1991. A general framework for managing multiple tasks in highly redundant robotic systems. Proc 5^th Int Conf on Advanced Robotics, p.1211-1216.

[28]Simetti E, Casalino G, Torelli S, et al., 2014. Floating underwater manipulation: developed control methodology and experimental validation within the TRIDENT project. J Field Robot, 31(3):364-385.

[29]Simetti E, Casalino G, Wanderlingh F, et al., 2018. Task priority control of underwater intervention systems: theory and applications. Ocean Eng, 164:40-54.

[30]Simpkins CA, 2014. Introduction to autonomous manipulation: case study with an underwater robot, SAUVIM. IEEE Robot Autom Mag, 21(4):109-110.

[31]Sivčev S, Coleman J, Omerdić E, et al., 2018. Underwater manipulators: a review. Ocean Eng, 163:431-450.

[32]Wang FY, Guo JB, Bu GQ, et al., 2022. Mutually trustworthy human–machine knowledge automation and hybrid augmented intelligence: mechanisms and applications of cognition, management, and control for complex systems. Front Inform Technol Electron Eng, 23(8):1142-1157.

[33]Yang CJ, Wu X, Zhu YC, et al., 2022a. Recent progress of an underwater robotic avatar. Proc 15^th Int Conf on Intelligent Robotics and Applications, p.615-626.

[34]Yang CJ, Zhu YC, Chen YH, 2022b. A review of human–machine cooperation in the robotics domain. IEEE Trans Hum-Mach Syst, 52(1):12-25.

[35]Zhang T, Li Q, Zhang CS, et al., 2017. Current trends in the development of intelligent unmanned autonomous systems. Front Inform Technol Electron Eng, 18(1):68-85.

[36]Zhu YC, Yang CJ, Tu ZP, et al., 2023. A haptic shared control architecture for tracking of a moving object. IEEE Trans Ind Electron, 70(5):5034-5043.