Abstract
Classical haptic teleoperation systems heavily rely on operators’ intelligence and efforts in aerial robot navigation tasks, thereby posing significantly users’ workloads. In this paper, a novel shared control scheme is presented facilitating a multirotor aerial robot haptic teleoperation system that exhibits autonomous navigation capability. A hidden Markov model filter is proposed to identify the intention state of operator based on human inputs from haptic master device, which is subsequently adopted to derive goal position for a heuristic sampling based local path planner. The human inputs are considered as commanded velocity for a trajectory servo controller to drive the robot along the planned path. In addition, vehicle velocity is perceived by the user via haptic feedback on master device to enhance situation awareness and navigation safety of the user. An experimental study was conducted in a simulated and a physical environment, and the results verify the effectiveness of the novel scheme in safe navigation of aerial robots. A user study was carried out between a classical haptic teleoperation system and the proposed approach in the identical simulated complex environment. The flight data and task load index (TLX) are acquired and analyzed. Compared with the conventional haptic teleoperation scheme, the proposed scheme exhibits superior performance in safe and fast navigation of the multirotor vehicle, and is also of low task and cognitive loads.
Similar content being viewed by others
References
Goertz RC (1954) Mechanical master-slave manipulator. Nucleonics(US) Ceased publication 12(11):45–46
Clement G, Fournier R, Gravez P, Morillon J (1988) Computer aided teleoperation: from arm to vehicle control. In: 1988 IEEE International Conference on Robotics and Automation(ICRA), IEEE, pp 590–592
Hong SG, Lee JJ, Kim S (1999) Generating artificial force for feedback control of teleoperated mobile robots. In: 1999 IEEE/RSJ international conference intelligent robots and systems, IEEE, pp 1721–1726
Li X, Song A, Li H, Lu W, Mao C (2012) Real-time obstacle avoidance for telerobotic systems based on equipotential surface. Int. J. Adv. Robot. Syst. 9(3):71–79
Luo J, Lin Z, Li Y, Yang C (2020) A teleoperation framework for mobile robots based on shared control. IEEE Robot Autom Lett 5(2):377–384
Hong SG, Kim BS, Kim S, Lee JJ (1998) Artificial force reflection control for teleoperated mobile robots. Mechatronics 8(6):707–717
Elhajj I, Xi N, Fung WK, Liu YH, Li WJ, Kaga T, Fukuda T (2001) Haptic information in internet-based teleoperation. IEEE Trans Mechatron 6(3):295–304
Lee S, Sukhatme GS, Kim GJ, Park CM (2002) Haptic control of a mobile robot: a user study. In: 2002 IEEE/RSJ international conference intelligent robots and systems, IEEE, vol 3, pp 2867–2874
Diolaiti N, Melchiorri C (2002) Teleoperation of a mobile robot through haptic feedback. In: 2002 IEEE international workshop haptic virtual environments and their applications, IEEE, pp 67–72
Lam TM, Boschloo HW, Mulder M, van Paassen MM (2009) Artificial force field for haptic feedback in uav teleoperation. IEEE Trans Syst Man Cybern Part A Syst Hum 39(6):1316–1330
Franchi A, Secchi C, Son HI, Bulthoff HH, Giordano PR (2012) Bilateral teleoperation of groups of mobile robots with time-varying topology. IEEE Trans Robot Autom 28(5):1019–1033
Lee D, Franchi A, Son HI, Ha C, Bulthoff HH, Giordano PR (2013) Semiautonomous haptic teleoperation control architecture of multiple unmanned aerial vehicles. IEEE Trans Mechatron 18(4):1334–1345
Mersha AY, Stramigioli S, Carloni R (2014) On bilateral teleoperation of aerial robots. IEEE Trans Robot 30(1):258–274
Hou X, Mahony R (2016) Comparative study of haptic interfaces for bilateral teleoperation of vtol aerial robots. IEEE Trans Syst Man Cybern Syst 46(10):1352–1363
Li W, Ding L, Gao H, Tavakoli M (2020) Haptic tele-driving of wheeled mobile robots under nonideal wheel rolling, kinematic control and communication time delay. IEEE Trans Syst Man Cybern Syst 50(1):336–347
Mahony R, Schill F, Corke P, Oh YS (2019) A new framework for force feedback teleoperation of robotic vehicles based on optical flow. In: 2009 IEEE international conference on robotics and automation(ICRA), IEEE, pp 1079–1085
Omari S, Hua MD, Ducard G, Hamel T (2013) Bilateral haptic teleoperation of vtol uavs. In: 2013 IEEE international conference on robotics and automation(ICRA), IEEE, pp 2385–2391
Hou X, Mahony R (2016) Dynamic kinesthetic boundary for haptic teleoperation of vtol aerial robots in complex environments. IEEE Trans Syst Man Cybern Syst 46(5):694–705
Luo J, Yang CG, Wang N, Wang M (2019) Enhanced teleoperation performance using hybrid control and virtual fixture. Int J Syst Sci 50(3):451–462
Jiang S, Lin C, Huang K, Song K (2017) Shared control design of a walking-assistant robot. IEEE Trans Control Syst Technol 25(6):2143–2150
Kong H, Yang C, Li G, Dai S (2020) A semg-based shared control system with no-target obstacle avoidance for omnidirectional mobile robots. IEEE Access 8:26030–26040
Nieto J, Slawinski E, Mut V, Wagner B (2010) Mobile robot teleoperation augmented with prediction and path-planning. Anal Design Eval Hum Mach Syst 11:53–58
Nieto J, Slawinski E, Mut V, Wagner B (2012) Toward safe and stable time-delayed mobile robot teleoperation through sampling-based path planning. Robotica 30(3):351–361
Sheridan TB (1989) Telerobotics. Automatica 25(4):487–507
Petkovic T, Puljiz D, Markovic I, Hein B (2019) Human intention estimation based on hidden markov model motion validation for safe flexible robotized warehouses. Robot Comput Integr Manuf 57:182–196
Khokar KH, Alqasemi R, Sarkar S, Dubey RV (2013) Human motion intention based scaled teleoperation for orientation assistance in preshaping for grasping. In: 2013 IEEE International Conference on Rehabilitation Robotics (ICORR), IEEE, pp 1–6
Li Y, Ge SS (2014) Human-robot collaboration based on motion intention estimation. IEEE Trans Mechatron 19(3):1007–1014
Yu X, He W, Li Y, Xue C, Li J, Zou J, Yang C (2019) Bayesian estimation of human impedance and motion intention for human-robot collaboration. IEEE Transactions on Cybernetics, pp 1–13
Neville H (1985) Impedance control: An approach to manipulation: Part I–Theory. J Dyn Syst Meas Control, 107:1–24. Parts I, II, III
Mut V, Postigo J, Slawinski E, Kuchen B (2002) Bilateral teleoperation of mobile robots. Robotica 20(02):213–221
Alaimo S, Pollini L, Buelthoff HH (2011) Admittance-based bilateral teleoperation with time delay for an unmanned aerial vehicle involved in an obstacle avoidance task. In: AIAA modeling and simulation technologies conference
Schill F, Hou X, Mahony R (2010) Admittance mode framework for haptic teleoperation of hovering vehicles with unlimited workspace. In: 2010 Australian conference on robotics and automation(ACRA)
Hou X, Mahony R, Schill F (2010) Representation of vehicle dynamics in haptic teleoperation of aerial robots. In: 2013 IEEE international conference on robotics and automation(ICRA), IEEE, pp 1447–1483
Cappe O, Moulines E, Ryden T (2006) Inference in hidden Markov models. Springer Science & Business Media, Berlin
Elliott RJ, Aggoun L, Moore JB (2008) Hidden Markov models: estimation and control, vol 29. Springer Science & Business Media, Berlin
Viterbi AJ (1967) Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Trans Inf Theory 13(2):260–269
Rabiner L (1989) A tutorial on hidden markov models and selected applications in speech recognition. Proc IEEE 77(2):257–286
LaValle SM, Kuffner JJ (1999) Randomized kinodynamic planning. In: 1999 IEEE international conference on robotics and automation (ICRA), IEEE, vol 1, pp 473–479
Karaman S, Frazzoli E (2011) Sampling-based algorithms for optimal motion planning. Int J Robot Res 30(7):846–894
Mahony R, Hamel T, Pflimlin JM (2008) Nonlinear complementary filters on the special orthogonal group. IEEE Trans Autom Control 53(5):1203–1218
Mahony R, Kumar V, Corke P (2012) Multirotor aerial vehicles: Modeling, estimation, and control of quadrotor. IEEE Robot Autom Mag 19(3):20–32
Hamel T, Mahony R, Lozano R, Ostrowski J (2002) Dynamic modelling and configuration stabilization for an x4-flyer. IFAC World Congress 15(1):217–222
Pounds P, Mahony R, Corke P (2010) Modelling and control of a large quadrotor robot. Control Eng Pract 18(7):691–699
Acknowledgements
This work was supported by National Natural Science Foundation of China (Grant Nos. 61703343), Natural Science Foundation of Shaanxi Province (Grant Nos. 2018JQ6070) and Northwestern Polytechnical University “the Fundamental Research Funds for the Central Universities” (Grant Nos. 3102018JCC003).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Hou, X. Haptic teleoperation of a multirotor aerial robot using path planning with human intention estimation. Intel Serv Robotics 14, 33–46 (2021). https://doi.org/10.1007/s11370-020-00339-2
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11370-020-00339-2