Bilateral teleoperation systems are often used in safety–critical scenarios where human operators may interact with the environment remotely, as in robotic-assisted surgery or nuclear plant maintenance. Teleoperation's stability and transparency are the two most important properties to be satisfied, but they cannot be optimized independently since they are in contrast. This article presents a passive linear MPC control scheme to implement bilateral teleoperation that optimizes the tradeoff between stability and transparency (a.k.a. performance). First, we introduce a linear virtual energy tank with a novel energy-sharing policy, allowing us to define a passive linear model predictive control (MPC). Second, we provide conditions to guarantee the stability of the nonlinear closed-loop system. We validate the proposed approach in a teleoperation scheme using two 7-degree of freedom manipulators while performing an assembly task. This novel passivity-based bilateral teleoperation using linear MPC and linearized energy tank reduces the computational effort of existing passive nonlinear MPC controllers.

In this paper, we propose a novel bilateral teleoperation architecture that allows to optimally render the remote interaction force at the local side while guaranteeing a robustly stable behaviour. Stability is guaranteed by ensuring a proper energy exchange between the local and the remote sides. Desired performance is obtained by optimizing the way energy is exploited for generating the behaviour at each side. The effectiveness of the proposed architecture is experimentally validated on a torque-controlled manipulator and in a surgical scenario, using the da Vinci®Research Kit (dVRK).

We present a toolehain based on Docker and KubeEdge that enables containerization and orchestration of ROS-based robotic SW applications on heterogeneous and hierarchical HW architectures. The toolehain allows for verification of functional and real-time constraints through HW-in-the-loop simulation, and for automatic mapping exploration of the SW across Cloud-Server-Edge architectures. We present the results obtained for the deployment of a real case of study composed by an ORB-SLAM application combined to local/global planners with obstacle avoidance for a mobile robot navigation.

This paper addresses the generation of collision-free trajectories for the autonomous execution of assistive tasks in Robotic Minimally Invasive Surgery (R-MIS). The proposed approach takes into account geometric constraints related to the desired task, like for example the direction to approach the final target and the presence of moving obstacles. The developed motion planner is structured as a two-layer architecture: a global level computes smooth spline-based trajectories that are continuously updated using virtual potential fields; a local level, exploiting Dynamical Systems based obstacle avoidance, ensures collision free connections among the spline control points. The proposed architecture is validated in a realistic surgical scenario.

Bilateral teleoperation systems allow the telepresence of an operator while working remotely. Such ability becomes crucial when dealing with critical environments like space, nuclear plants, rescue, and surgery. The main properties of a teleoperation system are the stability and the transparency which, in general, are in contrast and they cannot be fully achieved at the same time. In this paper, we will present a novel model predictive controller that implements a passivity-based bilateral teleoperation algorithm. Our solution mitigates the chattering issue arising when resorting to the energy tank (or reservoir) mechanism by forcing the passivity as a hard constraint on the system evolution.

In automated warehouses, path planning is a crucial topic to improve automation and efficiency. This kind of planning is usually computed off-line knowing the planimetry of the warehouse and the starting and target points of each agent. However, this global approach is not able to manage unexpected static/dynamic obstacles and other agents moving in the same area. For this reason in multi-robot systems global planners are usually integrated with local collision avoidance algorithms. In this paper we use the Voronoi diagram as global planner and the Velocity Obstacle (VO) method as collision avoidance algorithm. The goal of this paper is to extend such hybrid motion planner by enforcing mechanical constraints between agents in order to execute a task that cannot be performed by a single agent. We will focus on the cooperative task of carrying a payload, such as a bar. Two agents are constrained to move at the end points of the bar. We will improve the original algorithms by taking into account dynamically the constrained motion both at the global and at the collision avoidance level.

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 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.