Università degli studi di Modena e Reggio Emilia

While aerial manipulation has witnessed noticeable growth as a field in the last decade, most works investigated forms of interaction with static and rigid environments only. Whenever dynamic environments were considered, the employed methods often relied on the knowledge of the model of the environment, which in most real applications cannot be obtained. In this work, we propose an adaptive controller for a fully actuated UAV performing stable and efficient physical interaction tasks with unmodeled and dynamic objects moving in unknown environments. We develop a passive time-varying impedance controller and wrench tracking controller, whose adaptable parameters allow us to minimize tracking error and instabilities during the execution of the interaction task. Robust stability is guaranteed by energy tanks, with the addition of a task-based formulation for adapting online the tank parameters in order to always provide the system with an adequate amount of energy. The control framework is validated both in simulations and experimentally by interacting with an unmodeled cart moving in passive time-varying environments, while subjected to unknown disturbances.

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

A fruitful collaboration is based on the mutual knowledge of each other skills and on the possibility of communicating their own limits and proposing alternatives to adapt the execution of a task to the capabilities of the collaborators. This paper aims at reproducing such a scenario in a human-robot collaboration setting by proposing a novel communication control architecture. Exploiting control barrier functions, the robot is made aware of its (dynamic) skills and limits and, thanks to a local predictor, it is able to assess if it is possible to execute a requested task and, if not, to propose alternative by relaxing some constraints. The controller is interfaced with a communication infrastructure that enables human and robot to set up a bidirectional communication about the task to execute and the human to take an informed decision on the behavior of the robot. A comparative experimental validation is proposed.

In Human-Robot Collaboration, the robot operates in a highly dynamic environment. Thus, it is pivotal to guarantee the robust stability of the system during the interaction but also a high flexibility of the robot behavior in order to ensure safety and reactivity to the variable conditions of the collaborative scenario. In this paper we propose a control architecture capable of maximizing the flexibility of the robot while guaranteeing a stable behavior when physically interacting with the environment. This is achieved by combining an energy tank based variable admittance architecture with control barrier functions. The proposed architecture is experimentally validated on a collaborative robot.