The human-robot collaboration scenarios are characterized by the presence of human operators and robots that work in close contact with each other. As a consequence, the safety regulations have been updated in order to provide guidelines on how to asses safety in these new scenarios. In particular, Power and Force Limiting (PFL) collaborative mode describes how the energy should be regulated during the collaboration. Based on these guidelines, we propose a new optimal trajectory planner which, by exploiting the variability of the robot’s inertia as a function of its configuration, is able to return trajectories that can be travelled at greater speed and in less time, while guaranteeing the safety limits according to the standard. The proposed planner was validated first in simulation, comparing completion times with other state-of-the-art planning algorithms, and then experimentally, demonstrating the performance of the planned trajectories during physical interaction with the environment. Both validations confirm the effectiveness of the proposed planner, which returns shorter completion times while ensuring safe interaction.

In this paper we present a novel strategy for reactive collision-free feasible motion planning for robotic manipulators operating inside an environment populated by moving obstacles. The proposed strategy embeds the Dynamical System (DS) based obstacle avoidance algorithm into a constrained non-linear optimization problem following the Model Predictive Control (MPC) approach. The solution of the problem allows the robot to avoid undesired collision with moving obstacles ensuring at the same time that its motion is feasible and does not overcome the designed constraints on velocity and acceleration. Simulations demonstrate that the introduction of the MPC prediction horizon helps the optimization solver in finding the solution leading to obstacle avoidance in situations where a non predictive implementation of the DS-based method would fail. Finally, the proposed strategy has been validated in an experimental work-cell using a Franka-Emika Panda robot.

In this paper, we propose a novel torque controller for the implementation virtual remote center of motion. The controller allows the system to implement the required behavior and guarantees the satisfaction of the remote center of motion constraint. Exploiting the Udwadia-Kalaba equation for constrained dynamic systems, the controller is synthesized considering the dynamic effect the constraint produces on the manipulator, achieving more effective control with respect to kinematic strategies, and allowing the implementation of compliance behaviors. Simulations and experimental validation with a KUKA LWR 4+ with 7 degrees of freedom has been performed to check the performances of the proposed controller. Results show the effectiveness of the proposed controller with different control action, and the capability to interact with the environment by implementing compliant motion control.

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 this paper we propose a novel flexible and optimization-free controller for standard torque-controlled manipulator for Robotic-Assisted Minimally Invasive Surgery. A novel method has been developed to model the constraint introduced by the laparoscopic tool, i.e. the remote center of motion, exploiting closed chain manipulators theory, and the final controller was synthesized considering the effects the constraint produces at a dynamic level. A set of simulations has been performed in a trajectory tracking task to validate the performances of the proposed controller. Performances have been also tested in a real experimental scenario with a KUKA LWR 4+ with 7 degrees of freedom endowed with a laparoscopic-like tool. Results show the effectiveness of the proposed controller and its capability of modifying the trajectory in order to preserve the RCM constraint.

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.

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.

Robot swarms herald the ability to solve complex tasks using a large collection of simple devices. However, engineering a robotic swarm is far from trivial, with a major hurdle being the definition of the control laws leading to the desired globally coordinated behavior. Communication is a key element for coordination and it is considered one of the current most important challenges for swarm robotics. In this paper, we study the problem of maintaining robust swarm connectivity while performing a coverage task based on the Voronoi tessellation of an area of interest. We implement our methodology in a team of eight Khepera IV robots. With the assumptions that robots have a limited sensing and communication range-and cannot rely on centralized processing-we propose a tri-objective control law that outperforms other simpler strategies (e.g. a potential-based coverage) in terms of network connectivity, robustness to failure, and area coverage.

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.