Abstract: ...

Abstract: In the marine industry, treating vessel surfaces affected by biofouling enhances their hydrodynamic efficiency and lifespan, while the use of automated approaches eliminates the inherent risks and limitations of a human-based treatment. In this work, we deal with the problem of underwater propeller cleaning using an Underwater Vehicle Manipulator System (UVMS) equipped with a rotating cleaning tool. We propose a cascaded whole-body motion planning scheme that drives the cleaning tool to eventually cover the whole surface of interest while respecting the robot hardware limitations, field-of-view constraints and robot safety in terms of collision and self-collision avoidance. The effectiveness of the proposed method is demonstrated through simulation results.

Abstract: This paper presents a novel method to maintain the dynamic balance of a Mobile Manipulator (MM) during the pick-up of heavy objects. The approach entails the generation of a preliminary reach-to-grasp trajectory, which is subsequently refined by an Optimization-Based Controller (OBC) formulated as a Quadratic Program (QP). The trajectory is modified in a minimal fashion to ensure that the robot maintains balance during the reaching phase and remains balanced when the payload is grasped. This is accomplished by incorporating a balance constraint into the OBC that predicts the Zero Moment Point (ZMP) position of the robot at the beginning of the pick-up phase. This accounts for the gravitational and inertial effects that the object has on the robot. The method is validated through simulations conducted with the TIAGo robot in Gazebo. The results demonstrate that the proposed approach effectively prevents the robot from tipping over when the payload is considered.

Abstract: We propose a sensor-based scheme for safe robot navigation in a crowd of moving humans. It consists of two modules, i.e., the crowd prediction and motion generation module, which run sequentially during every sampling interval. Using information acquired online by an on-board sensor, the crowd prediction module foresees the future motion of the humans in the robot surroundings. Based on such prediction, the motion generation module produces feasible commands to safely drive the robot among the humans by combining a nonlinear Model Predictive Control (NMPC) algorithm with collision avoidance constraints formulated via discrete-time Control Barrier Functions (CBFs). We show the effectiveness of the proposed approach via simulations obtained in CoppeliaSim on the Pioneer 3-DX mobile robot in scenarios of different complexity. 

Abstract: We present a novel real-time motion generation approach for mobile manipulators which maintains balance even when the robot is called to execute aggressive motions. The proposed approach is based on Nonlinear Model Predictive Control (NMPC) and uses the robot full dynamics as prediction model. Robot balance is maintained by enforcing a constraint that restricts the feasible set of robot motions to those generating non-negative moments around the edges of the support polygon. This balance constraint, inherently nonlinear, is linearized using the NMPC solution of the previous iteration. In this way we facilitate the solution of the NMPC and we achieve real-time performance without compromising robot safety.  We validate our approach in scenarios of increasing difficulty and compare its performance with two other methods from the literature. The simulation results show that our method can generate motions that maintain balance in challenging situations where the other techniques fail.

Abstract: We present a novel method for mobile robot navigation among obstacles. Our approach is based on Nonlinear Model Predictive Control (NMPC) and uses a dynamics-aware collision avoidance constraint. The constraint, built upon the notion of avoidable collision state, considers not only the robot-obstacle distance but also their velocity as well as the robot actuation capabilities. To highlight the effectiveness of this constraint, we compare the proposed method with a version of the NMPC that uses a constraint purely based on distance information, showing that the first achieves better performance than the second, especially when the robot travels at higher speed among several moving obstacles. Results indicate that the method can work with relatively short prediction horizons and is therefore amenable to real-time implementation.

Abstract: We present a novel method for navigation of mobile robots in challenging dynamic environments. The method, which is based on Nonlinear Model Predictive Control (NMPC), hinges upon a specially devised constraint for dynamics-aware collision avoidance. In particular, the constraint builds on the notion of avoidable collision state, taking into account the robot actuation capabilities in addition to the robot–obstacle relative distance and velocity. The proposed approach is applied to both ground and flying robots and tested in a variety of static and dynamic environments. Comparative simulations with an NMPC using a purely distance-based collision avoidance constraint confirm the superiority of the dynamics-aware version, especially for high-speed navigation among moving obstacles. Moreover, the results indicate that the method can work with relatively short prediction horizons and is therefore amenable to real-time implementation.