In this article, an optimal controller for achieving constrained admittance control is proposed. This controller strictly adheres to the constraint boundaries while ensuring minimal variations in kinematic energy. The proposed method integrates admittance control for human-robot interaction with the Udwadia-Kalaba equations for constrained motion into a unified framework. The proposed architecture has been tested and validated both with simulations and real tests on a 6-DoF UR5e robot. The results demonstrate that the proposed architecture outperforms virtual fixtures, one of the most commonly used techniques to implement effective path-following control.

Ensuring safe and effective physical human-robot interaction (pHRI) remains a critical challenge in industrial robotics, particularly in ensuring compliance with ISO/TS 15066 safety standards. This paper proposes a novel framework to achieve a safe and robust physical human-robot interaction (pHRI). The framework adapts the parameters of a variable admittance controller online in order to guarantee passivity and compliance with ISO/TS 15066. Passivity is guaranteed using an energy tank, while a safety constraint explicitly handles the Power and Force Limiting (PFL) energy limit. Experimental validation on an industrial robot demonstrates the effectiveness of the framework.

Within the context of Robotic Minimally Invasive Surgery (R-MIS), we propose a novel linear model predictive controller formulation for the coordination of multiple autonomous robotic arms. The controller is synthesized by formulating a linear approximation of non-linear constraints, which allows the controller to be both computationally faster and better performing due to the increased prediction horizon allowed within the real-time control requirements for the proposed surgical application. The solution is validated under the expected constraints of a surgical scenario in which multiple laparoscopic tools must move and coordinate in a shared environment.

In robotic applications where the autonomy is shared between the human and the robot, the autonomous behavior of the robotic system is determined considering mainly the task to be executed and the data collected from the environment using, e.g., formal methods and machine learning techniques. Nevertheless, it is important to correctly translate high-level decision into low-level control inputs in order to avoid an unstable behavior due to a naive implementation of the autonomy. In this article, we propose an energy-based architecture for shared autonomy that allows to reproduce as closely as possible the desired behavior, while ensuring a robust stability of the robotic system. The proposed architecture is experimentally validated in two application scenarios: shared control of a multirobot system and variable admittance control in human robot collaboration

In this paper we present a novel strategy for motion planning of autonomous robotic arms in Robotic Minimally Invasive Surgery (R-MIS). We consider a scenario where several laparoscopic tools must move and coordinate in a shared environment. The motion planner is based on a Model Predictive Controller (MPC) that predicts the future behavior of the robots and allows to move them avoiding collisions between the tools and satisfying the velocity limitations. In order to avoid the local minima that could affect the MPC, we propose a strategy for driving it through a sequence of waypoints. The proposed control strategy is validated on a realistic surgical scenario.

Collaborative robots are one of the key drivers in Industry 4.0 and they have evolved considerably since the last decades of the 20th century. With respect to the industrial robots, collaborative robots are more productive, flexible, versatile and safer. In the recent years, many industrial robot producers and startups entered the segment of collaborative robots. In this paper, we propose a methodology for developing a comparative analysis of the collaborative robots currently available in the market. The goal of the paper is to provide a framework for allowing the benchmarking, based on common robot parameters and standardized experiments that can be performed with the robot under investigation. An experimental technological review of three different collaborative robots is provided, to show how the methodology can be applied in real cases.

In this paper, a two-layer architecture for the bilateral teleoperation of multi-arms systems with communication delay is presented. We extend the single-master-single-slave two layer approach proposed in [1] by connecting multiple robots to a single energy tank. This allows to minimize the conservativeness due to passivity preservation and to increment the level of transparency that can be achieved. The proposed approach is implemented on a realistic surgical scenario developed within the EU-funded SARAS project.

The possibility of adapting online the way a robot interacts with the environment is becoming more and more important. In this paper we introduce the tank based admittance controller. We show that all the admittance controllers can be modeled as an energy optimization problem and then we introduce a novel admittance control strategy that allows to change online the interactive behavior while preserving a stable interaction with the environment. The effectiveness of the proposed architecture is experimentally validated.

The raise of collaborative robotics has allowed to create new spaces where robots and humans work in proximity. Consequently, to predict human movements and his/her final intention becomes crucial to anticipate robot next move, preserving safety and increasing efficiency. In this paper we propose a human-arm prediction algorithm that allows to infer if the human operator is moving towards the robot to intentionally interact with it. The human hand position is tracked by an RGB-D camera online. By combining the Minimum Jerk model with Semi-Adaptable Neural Networks we obtain a reliable prediction of the human hand trajectory and final target in a short amount of time. The proposed algorithm was tested in a multi-movements scenario with FANUC LR Mate 200iD/7L industrial robot.

The development of robotic systems with a certain level of autonomy to be used in critical scenarios, such as an operating room, necessarily requires a seamless integration of multiple state-of-the-art technologies. In this paper we propose a cognitive robotic architecture that is able to help an operator accomplish a specific task. The architecture integrates an action recognition module to understand the scene, a supervisory control to make decisions, and a model predictive control to plan collision-free trajectory for the robotic arm taking into account obstacles and model uncertainty. The proposed approach has been validated on a simplified scenario involving only a da VinciO surgical robot and a novel manipulator holding standard laparoscopic tools.

The paper describes an estimation and identification procedure that allows to reconstruct the inertial parameters of a rigid load attached to the end-effector of an industrial manipulator. In particular, the proposed method adopts a multirate quaternion-based Kalman filter, fusing measurements obtained from robot kinematics and inertial sensors at possibly different sampling frequencies, to estimate linear accelerations and angular velocities/accelerations of the load. Then, a recursive total least-squares (RTLS) process is executed to identify the load parameters. Both steps of the estimation and identification procedure are performed in real-time, without the need for offline post-processing of measured data.

In order to make robot programming more easy and immediate, walk-through programming techniques can be exploited. However, a modification of a portion of the trajectory usually means to execute the path from the beginning. In this paper we propose a passivity-based framework to modify the trajectory online, manually driving the robot throughout the desired correction. The system follows the initial trajectory, encoded with Dynamical Movement Primitives, by setting high gains in the admittance control. When the human operator grabs the end-effector, the robot becomes compliant and the user can easily teach the desired correction, until he/she releases it at the end of the modification. Finally, the correction is optimally joined to the initial trajectory, restarting the path tracking. To avoid unsafe behaviors, the variation of the admittance parameters is performed exploiting energy tanks, in order to preserve the passivity of the interaction.

In physical human-robot interaction, the coexistence of robots and humans in the same workspace requires the guarantee of a stable interaction, trying to minimize the effort for the operator. To this aim, the admittance control is widely used and the appropriate selection of the its parameters is crucial, since they affect both the stability and the ability of the robot to interact with the user. In this paper, we present a strategy for detecting deviations from the nominal behavior of an admittance-controlled robot and for adapting the parameters of the controller while guaranteeing the passivity. The proposed methodology is validated on a KUKA LWR 4+.

Admittance control is a widely used approach for guaranteeing a compliant behavior of the robot in physical human-robot interaction. When an admittance-controlled robot is coupled with a human, the dynamics of the human can cause deviations from the desired behavior of the robot, mainly due to a stiffening of the human arm, and thus generate high-frequency unsafe oscillations of the robot. In this paper we present a novel methodology for detecting the rising oscillations in the human-robot interaction. Furthermore, we propose a passivity-preserving strategy to adapt the parameter of the admittance control in order to get rid of the high-frequency oscillations and, when possible, to restore the desired interaction model. A thorough experimental validation of the proposed strategy is performed on a group of 26 users performing a cooperative task.

Wave variables are a very popular approach for dealing with communication delay in bilateral teleoperation because of their effectiveness and of their simplicity. Nevertheless, the inherent dynamics of wave based communication channels is often deleterious for the transparency of the teleoperation system. Recently proposed architectures like TDPN, PSPM and two layers approach allow to achieve a high transparency at the price of a complex architecture, with some parameters to tune empirically. In this paper we propose a novel wave based architecture that blends the high performance that can be achieved by recently proposed architectures with the simplicity of wave based bilateral teleoperation.

This paper describes a walk-through programming technique, based on admittance control and tool dynamics compensation, to ease and simplify the process of trajectory learning in common industrial setups. In the walk-through programming, the human operator grabs the tool attached at the robot end-effector and “walks” the robot through the desired positions. During the teaching phase, the robot records the positions and then it will be able to interpolate them to reproduce the trajectory back. In the proposed control architecture, the admittance control allows to provide a compliant behavior during the interaction between the human operator and the robot end-effector, while the algorithm of compensation of the tool dynamics allows to directly use the real tool in the teaching phase. In this way, the setup used for the teaching can directly be the one used for performing the reproduction task. Experiments have been performed to validate the proposed control architecture and a pick and place example has been implemented to show a possible application in the industrial field.

Because of their simplicity, wave variables have become almost a standard strategy for stabilizing delayed bilateral teleoperation systems. However, the price to pay for a stable behavior is a degradation in the performance of the teleoperation system. Recently, more flexible and transparency oriented bilateral architectures have been proposed (e.g. TDPN, PSPM, Two-Layer approach) but they are complex to implement and to tune. In [1], a strategy for blending the high performance of the new control methodologies with the simplicity of wave based bilateral teleoperation has been proposed. Nevertheless, while appealing in terms of simplicity, this method is conservative in terms of the transparency that can be achieved. In this paper, we extend the architecture in [1] in order to optimize the use of the energy and for achieving a coupling that is as close as possible to the desired one while preserving the passivity of the overall system.

The paper describes a passivity based approach to the generation of virtual fixtures for robotic teleoperation schemes involving multiple masters and multiple slaves. Virtual fixtures considered in the paper aim to guide the user towards a geometric path, which is assumed to be collision-free by design, describing a desired execution of a given task. To preserve safe distance from obstacles and at the same time suggest the user a preferred direction to progress along the path, the virtual fixtures are generated by arbitrarily redirecting assistive forces obtained by summation of attractive and repulsive potential fields. The main result of the paper is the definition of a passivity preserving condition for this redirection, so that the behavior of the teleoperated systems remains safe and stable. The proposed assisted mode of teleoperation has been tested on a surgical robot prototype with dual arms configuration, since robotic surgery represents a suitable application domain for such control schemes.

Introducing some form of autonomy in robotic surgery is being considered by the medical community to better exploit the potential of robots in the operating room. However, significant technological steps have to occur before even the smallest autonomous task is ready to be presented to the regulatory authorities. In this paper, we address the initial steps of this process, in particular the development of control concepts satisfying the basic safety requirements of robotic surgery, i.e., providing the robot with the necessary dexterity and a stable and smooth behavior of the surgical tool. Two specific situations are considered: the automatic adaptation to changing tissue stiffness and the transition from autonomous to teleoperated mode. These situations replicate real-life cases when the surgeon adapts the stiffness of her/his arm to penetrate tissues of different consistency and when, due to an unexpected event, the surgeon has to take over the control of the surgical robot. To address the first case, we propose a passivity-based interactive control architecture that allows us to implement stable time-varying interactive behaviors. For the second case, we present a two-layered bilateral control architecture that ensures a stable behavior during the transition between autonomy and teleoperation and, after the switch, limits the effect of initial mismatch between master and slave poses. The proposed solutions are validated in the realistic surgical scenario developed within the EU-funded I-SUR project, using a surgical robot prototype specifically designed for the autonomous execution of surgical tasks like the insertion of needles into the human body.

The paper describes a software architecture for control and simulation of a generic robotic manipulator. The algorithmic part of the system is implemented using the Orocos component-based framework and its related library for robotic applications, while the graphical animation of the robot is developed with Blender. The proposed control and simulation framework is modular, reconfigurable and computationally efficient. Moreover, it can be seamlessly integrated into a more complex control architecture for a complete intelligent robotic system.

In this paper, we present a new impedance control strategy that allows to reproduce a time-varying stiffness. By properly controlling the energy exchanged during the action, we guarantee the system passivity for any choice of the stiffness matrix, especially in case of time-varying stiffness, and therefore a stable behavior of the robot both in free motion and in interaction with an environment.