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.

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.

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.