In this paper, we propose a closed-loop sensitivity-based approach to enhance the robustness of robotic non-prehensile dynamic manipulation tasks. The proposed method aims at fulfilling the transportation of an object, that is free to move on a tray-shaped robot end-effector, in face of not perfectly known nominal dynamic parameters. The approach is built up on taking the parameterized reference trajectory to be tracked as the optimization variable minimizing a norm of the task closed-loop sensitivity. The resulting optimal reference trajectory is inherently more robust to the parametric variations of object dynamic properties compared to a baseline trajectory execution. The tracking performance is assessed and validated along hardware experiments and an extensive simulation campaign assessing the superior robustness of our approach.

The adoption of robots outside their cages in conventional industrial scenarios requires not only safe human-robot interaction but also intuitive human-robot interactive communication. In human-robot collaborative tasks, the objective is to help humans in performing their job with less physical and cognitive effort. A collaborative task can involve the exchange of objects between the robot and the operator. However, the handover operation should be sufficiently intuitive, fluid, and natural for being accepted by the involved humans. Naturalness strongly depends on the speed of the object exchange and the way of communication. For the latter aspect, this paper proposes a multi-modal communication based on visual and haptic cues. Concerning the handover speed requirement, the paper proposes a high-performance visual servoing based on an Extended Kalman Filter (EKF) estimating object speed during the handover and a homography-based object tracking. The object safety is ensured by proper control of the robot grasp force based on a model-based approach exploiting tactile measurements. The same perception modality is also used as a source of haptic cues that make the handover intuitive and natural. Experiments of human-robot handovers through haptic and visual cues communication demonstrate the effectiveness of the proposed approach.

This paper tackles the problem of nonprehensile object transportation through a legged manipulator. A whole-body control architecture is devised to prevent sliding of the object placed on the tray at the manipulator's end-effector and retain the legged robot balance during walking. The controller solves a quadratic optimization problem to realize the sought transportation task while maintaining the contact forces between the tray and the object and between the legs and the ground within their respective friction cones, also considering limits on the input torques. An extensive simulation campaign confirmed the feasibility of the approach and evaluated the control performance through a thorough statistical analysis conducted varying mass, friction, and the dimension of the transported object.

This work addresses the problem of transporting an object along a desired planar trajectory by pushing with mobile robots. More specifically, we concentrate on establishing optimal contacts between the object and the robots to execute the given task with minimum effort. We present a task-oriented contact placement optimization strategy for object pushing that allows calculating optimal contact points minimizing the amplitude of forces required to execute the task. Exploiting the optimized contact configuration, a motion controller uses the computed contact forces in feed-forward and position error feedback terms to realize the desired trajectory tracking task. Simulations and real experiments results confirm the validity of our approach.

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.

Executing multiple tasks concurrently is important in many robotic applications. Moreover, the prioritization of tasks is essential in applications where safety-critical tasks need to precede application-related objectives, in order to protect both the robot from its surroundings and vice versa. Furthermore, the possibility of switching the priority of tasks during their execution gives the robotic system the flexibility of changing its objectives over time. In this paper, we present an optimization-based task execution and prioritization framework that lends itself to the case of time-varying priorities as well as variable number of tasks. We introduce the concept of extended set-based tasks, encode them using control barrier functions, and execute them by means of a constrained-optimization problem, which can be efficiently solved in an online fashion. Finally, we show the application of the proposed approach to the case of a redundant robotic manipulator.

The field of soft robotics is grounded on the idea that, due to their inherent compliance, soft robots can safely interact with the environment. Thus, the development of effective planning and control pipelines for soft robots should incorporate reliable robot-environment interaction models. This strategy enables soft robots to effectively exploit contacts to autonomously navigate and accomplish tasks in the environment. However, for a class of soft robots, namely vine-inspired, tip-extending or "vine" robots, such interaction models and the resulting planning and control strategies do not exist. In this paper, we analyze the behavior of vine robots interacting with their environment and propose an obstacle-interaction model that characterizes the bending and wrinkling deformation induced by the environment. Starting from this, we devise a novel obstacle-interaction planning method for these robots. We show how obstacle interactions can be effectively leveraged to enlarge the set of reachable workspace for the robot tip, and verify our findings with both simulated and real experiments. Our work improves the capabilities of this new class of soft robot, helping to advance the field of soft robotics.

Robot teleoperation is widely used for several hazardous applications. To increase teleoperator capabilities shared-control methods can be employed. In this paper, we present a passive task-prioritized shared-control method for remote telemanipulation of redundant robots. The proposed method fuses the task-prioritized control architecture with haptic guidance techniques to realize a shared-control framework for teleoperation systems. To preserve the semi-autonomous telerobotic system safety, passivity is analyzed and an energy-tanks passivity-based controller is developed. The proposed theoretical results are validated through experiments involving a real haptic device and a simulated slave robot.

Polyp dissection requires very accurate detection of the region of interest and high-precision cutting with adequate safety margins. Robot-assisted polyp dissection is a solution to accomplish high-quality intervention. This paper proposes a method to constrain the robot to follow an accurate dissection path based on Virtual Fixtures (VF). The VFs are created via specific control points obtained directly from images of the surgical scene and are updated by the vision algorithm. The VF constraints can autonomously adapt themselves to environment changing during the surgical intervention. The entire pipeline is validated through experiments on the da Vinci Research Kit (dVRK) robot.

During suturing tasks performed with minimally invasive surgical robots, configuration singularities and joint limits often force surgeons to interrupt the task and re-grasp the needle using dual-arm movements. This yields an increased operator's cognitive load, time-to-completion and performance degradation. In this paper, we propose a haptic-guided shared control method for grasping the needle with the Patient Side Manipulator (PSM) of the da Vinci robot avoiding such issues. We suggest a cost function consisting of (i) the distance from robot joint limits and (ii) the task-oriented manipulability along the suturing trajectory. Evaluating the cost and its gradient on the needle grasping manifold allows us to obtain the optimal grasping pose for joint-limit and singularity free robot movements during suturing. We compute force cues and display them through the Master Tool Manipulator (MTM) to guide the surgeon towards the optimal grasp. As such, our system helps the operator to choose a grasping configuration that allows the robot to avoid joint limits and singularities during post-grasp suturing movements. We show the effectiveness of the proposed haptic-guided shared control method during suturing using both simulated and real experiments. The results illustrate that our approach significantly improves the performance in terms of needle re-grasping.

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 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 module and the complete robotic system is finally shown by the virtual robot in the simulator and real robot in experiments. This study demonstrates a setup of autonomous mobile manipulator for various flexible manufacturing and logistical scenarios.

This paper presents eligere, a new open-source distributed software platform for group decision making in engineering design. It is based on the fuzzy analytical hierarchy process (fuzzy AHP), a multiple criteria decision making method used in group selection processes to rank a discrete set of alternatives with respect to some evaluation criteria. eligere is built following the paradigm of distributed cyber-physical systems. It provides several features of interest in group decision making problems: a web-application where experts express their opinion on the alternatives using the natural language, a fuzzy AHP calculation module for transforming qualitative into quantitative data, a database for collecting both the experts' answers and the results of the calculations. The resulting software platform is: distributed, interactive, multi-platform, multi-language and open-source. Eligere is a flexible cyber-physical information system useful in various multiple criteria decision making problems: in this paper we highlight its key concepts and illustrate its potential through a case study, i.e., the optimum selection of design alternatives in a robotic product design.

In this paper we present an interactive system to enhance bilateral teleoperation through online virtual fixtures generation and task switching. This is achieved using a stereo camera system which provides accurate information of the surrounding environment of the robot and of the tasks that have to be performed in it. The use of the proposed approach aims at improving the performances of bilateral teleoperation systems by reducing the human operator workload and increasing both the implementation and the execution efficiency. In fact, using our method virtual guidances do not need to be programmed a priori but they can be instead automatically generated and updated making the system suitable for unstructured environments. We strengthen the proposed method using passivity control in order to safely switch between different tasks while teleoperating under active constraints. A series of experiments emulating real industrial scenarios are used to show that the switch between multiple tasks can be passively and safely achieved and handled by the system.