Sharing the control of a robotic system with an autonomous controller allows a human to reduce his/her cognitive and physical workload during the execution of a task. In recent years, the development of inference and learning techniques has widened the spectrum of applications of shared control (SC) approaches, leading to robotic systems that are capable of seamless adaptation of their autonomy level. In this perspective, shared autonomy (SA) can be defined as the design paradigm that enables this adapting behavior of the robotic system. This letter collects the latest results achieved by the research community in the field of SC and SA with special emphasis on physical human-robot interaction (pHRI). Architectures and methods developed for SC and SA are discussed throughout the paper, highlighting the key aspects of each methodology. A discussion about open issues concludes this letter.

Industry 4.0 demands the heavy usage of robotic mobile manipulators with high autonomy and intelligence. The goal is to accomplish dexterous manipulation tasks without prior knowledge of the object status in unstructured environments. It is important for the mobile manipulator to recognize and detect the objects, determine manipulation pose and adjust its pose in the workspace fast and accurately. In this research, we developed a stereo vision algorithm for the object pose estimation using point cloud data from multiple stereo vision systems. An improved iterative closest point algorithm and method is developed for the pose estimation. With the pose input, algorithms and several criteria are studied for the robot to select and adjust its pose by maximizing its manipulability on a given manipulation task. The performance of each technical modules and the complete robotic system is finally shown by virtual robot in the simulator and real robot in experiments.

This article proposes a shared-control teleoperation architecture for robot manipulators transporting an object on a tray. Differently from many existing studies about remotely operated robots with firm grasping capabilities, we consider the case in which, in principle, the object can break its contact with the robot end-effector. The proposed shared-control approach automatically regulates the remote robot motion commanded by the user and the end-effector orientation to prevent the object from sliding over the tray. Furthermore, the human operator is provided with haptic cues informing about the discrepancy between the commanded and executed robot motion, which assist the operator throughout the task execution. We carried out trajectory tracking experiments employing an autonomous 7-degree-of-freedom (DoF) manipulator and compared the results obtained using the proposed approach with two different control schemes (i.e., constant tray orientation and no motion adjustment). We also carried out a human-subjects study involving 18 participants in which a 3-DoF haptic device was used to teleoperate the robot linear motion and display haptic cues to the operator. In all experiments, the results clearly show that our control approach outperforms the other solutions in terms of sliding prevention, robustness, commands tracking, and user’s preference.

During robot-aided surgical interventions, the surgeon can benefit from the application of virtual fixtures (VF). Though very effective, this technique is very often not practicable in unstructured surgical environments. In order to comply with the environmental deformation, both the VFs geometry and the constraint enforcement parameters need to be online defined/adapted. This paper proposes a strategy for an effective use of VF assistance in minimally invasive robotic surgical tasks. An online VF generation technique based on interaction force measurements is presented. Pose and geometry adaptations of the VF are considered. Passivity of the overall system is guaranteed by using energy tanks passivity-based control. The proposed method is validated through experiments on the da Vinci Research Kit.

In this work we present a V-REP simulator for the da Vinci Research Kit (dVRK). The simulator contains a full robot kinematic model and integrated sensors. A robot operating system (ROS) interface has been created for easy use and development of common software components. Moreover, several scenes have been implemented to illustrate the performance and potentiality of the developed simulator. Both the simulator and the example scenes are available to the community as an open source software.