Haptic Assisted Teleoperation Based on Virtual Fixture and Dynamic Modelling

It is a difficult task to teleoperate a robot in a partially known or unstructured environment without any assistance.In this paper, a haptic assisted teleoperation system is discussed that provides virtual fixtures for motion guidance and haptic feedbacks for dynamic interaction.First, a novel virtual fixture generation method based on a point-set implicit surface was proposed in a point cloud augmented virtual environment for human operational guidance.A robot-centered potential force field model was applied to generate guidance virtual fixtures.The resultant forces generated from both forbidden region and guidance virtual fixtures were fed back in real time to the human operator through a haptic device.Second, a real-time dynamic modelling method was proposed to reconstruct contacts in the teleoperation system.For the reconstruction of dynamic properties, an adaptive forgetting factor recursive least-squares method was applied for real-time parameter estimation.With haptic assistance from the motion guidance force and the dynamic force feedback, human operators could efficiently achieve targets and complete interactive tasks.The experimental results show that the proposed methods are effective for robot teleoperation.Sensors and Materials, Vol. 29, No. 9 (2017) implemented VF using definite surfaces to assist in peg-in-hole tasks and thereby increased operator performance up to 70%.Haptic VFs are mainly categorized into two types, namely, forbidden region virtual fixtures (FRVFs) and guidance virtual fixtures (GVFs).These are systemgenerated forces that are fed back to the operators as motion regulation during robot manipulation.As the names imply, the FRVFs are used to restrict robot access to "forbidden" regions, while the GVFs assist operators or robots to move along desired paths or towards targets.The VFs are widely applied in robotic surgery, autonomous robotic manipulators, and robot teleoperation. (5)he potential benefits of VFs are that it is safer and faster to operate robots with them.In addition, VFs can reduce mental workload, time on task, and errors.VFs have been widely studied.Abbott focused mainly on control stability in classical teleoperation control architectures with VFs. (6)Park and Howard (7) proposed a methodology that employs vision-based GVF techniques for improving human performance in a teleoperated manipulation system.Kapoor and Taylor (8) introduced the notion of "soft" virtual fixture mechanisms for robotic surgical assistance.Most early reported studies concentrated on system performance using VFs (9,10) or VF applications based on a predefined geometric surface or path. (11,12)ore VF construction methods have been described in a recent survey. (5)Although these studies successfully implemented VFs, using VFs in a partially known and unstructured environment is still challenging.Recently, VF construction based on computer vision has been widely studied for adaptive applications, particularly for use in dynamic and unstructured environments.Yamamoto et al. (13) applied FRVFs to tissues based on shape recognition.However, this approach was offline and not adaptive.A construction method for real-time FRVFs during teleoperation from streaming point clouds obtained using an RGB-D camera has been proposed by Kosari et al. for robot teleoperation. (14)owever, they focused primarily on FRVFs for three architectures used in teleoperation.Furthermore, when human operators control the robot into contact with a real target, the dynamic properties of the environment are often hard to feed back to the VE.Estimating the dynamic properties of the contact between the robot and the object is essential for haptic rendering in the VE.Therefore, if the dynamic model can perfectly describe the real environment, human operators can perceive the virtual contact force directly in the VE during manipulation.However, implementing accurate dynamic modelling remains a challenge.Many studies have been done on the handling of these problems in robotic applications.Ni et al. (15) proposed a sliding-average least-squares algorithm-based environment identification method for contact interaction with