Roberto Rossi

Bio: Roberto Rossi is an academic researcher from Polytechnic University of Milan. The author has contributed to research in topics: Industrial robot & Robot. The author has an hindex of 7, co-authored 9 publications receiving 139 citations.

Nonlinear model predictive control for aerial manipulation

Abstract: This paper presents a nonlinear model predictive controller to follow desired 3D trajectories with the end effector of an unmanned aerial manipulator (i.e., a multirotor with a serial arm attached). To the knowledge of the authors, this is the first time that such controller runs online and on board a limited computational unit to drive a kinematically augmented aerial vehicle. Besides the trajectory following target, we explore the possibility of accomplishing other tasks during flight by taking advantage of the system redundancy. We define several tasks designed for aerial manipulators and show in simulation case studies how they can be achieved by either a weighting strategy, within a main optimization process, or a hierarchical approach consisting on nested optimizations. Moreover, experiments are presented to demonstrate the performance of such controller in a real robot.

Towards an optimal avoidance strategy for collaborative robots

Abstract: Collaborative robots represent a game changer in manufacturing for their ease of use, the reduced need of safeguarding hardware and, consequently, their extremely fast payback time. However, most of the collaborative robots available on the market are power and force limiting (PFL) devices. The main disadvantage of this type of collaborative operation is that the robot is forced to stop when a collision occurs, as the only way the robot is aware of the presence of the human is through its embedded torque or motor current monitoring algorithms. Albeit tolerable from a safety point of view, these collisions might dramatically reduce the performance of the robot in terms of productivity, ultimately jeopardising the economic attractiveness of a collaborative workstation. This paper introduces an avoidance strategy that suggests the robot alternative paths to be traversed, that are both collision free and optimal in terms of minimum traversal time. The control strategy makes use of a depth camera in order to enhance robot perception of the environment. Moreover, by properly exploiting information coming from these sensors, the control strategy itself is able to communicate to the robot the best decision to take with respect to the presence of one or more human operators. The method is experimentally validated on a Universal Robots UR5.

A pre-collision control strategy for human-robot interaction based on dissipated energy in potential inelastic impacts

Abstract: Enabling human-robot collaboration raises new challenges in safety-oriented robot design and control. Indices that quantitatively describe human injury due to a human-robot collision are needed to propose suitable pre-collision control strategies. This paper presents a novel model-based injury index built on the concept of dissipated kinetic energy in a potential inelastic impact. This quantity represents the fracture energy lost when a human-robot collision occurs, modeling both clamped and unclamped cases. It depends on the robot reflected mass and velocity in the impact direction. The proposed index is expressed in analytical form suitable to be integrated in a constraint-based pre-collision control strategy. The exploited control architecture allows to perform a given robot task while simultaneously bounding our injury assessment and minimizing the reflected mass in the direction of the impact. Experiments have been performed on a lightweight robot ABB FRIDA to validate the proposed injury index as well as the pre-collision control strategy.

Trajectory Generation for Unmanned Aerial Manipulators Through Quadratic Programming

Abstract: In this paper, a trajectory generation approach using quadratic programming is described for aerial manipulation, i.e. , for the control of an aerial vehicle equipped with a robot arm. The proposed approach applies the online active set strategy to generate a feasible trajectory of the joints, in order to accomplish a set of tasks with defined bounds and constraint inequalities. The definition of the problem in the acceleration domain allows to integrate and perform a large set of tasks and, as a result, to obtain smooth motion of the joints. A weighting strategy, associated with a normalization procedure, allows us to easily define the relative importance of the tasks. This approach is useful to accomplish different phases of a mission with different redundancy resolution strategies. The performance of the proposed technique is demonstrated through real experiments with all the algorithms running onboard in real time. In particular, the aerial manipulator can successfully perform navigation and interaction phases, while keeping motion within prescribed bounds and avoiding collisions with external obstacles.

Implicit force control for an industrial robot based on stiffness estimation and compensation during motion

Abstract: Although force control algorithms have been studied for three decades, this technology is not largely exploited in industry yet. The present paper proposes a position-based adaptive force control strategy, that relies on a novel method for the on line estimation of the environment stiffness. The control design is targeted to industrial controller structures and it is theoretically proven to be robust to time varying estimation errors of the environment stiffness and joint friction disturbances. The estimation algorithm succeeds in identifying the environment stiffness even in presence of geometrical irregularities of the contact surface during motion. The identification and control approaches are experimentally validated on an industrial robot equipped with a force sensor.

Working Together: A Review on Safe Human-Robot Collaboration in Industrial Environments

Abstract: After many years of rigid conventional procedures of production, industrial manufacturing is going through a process of change toward flexible and intelligent manufacturing, the so-called Industry 4.0. In this paper, human–robot collaboration has an important role in smart factories since it contributes to the achievement of higher productivity and greater efficiency. However, this evolution means breaking with the established safety procedures as the separation of workspaces between robot and human is removed. These changes are reflected in safety standards related to industrial robotics since the last decade, and have led to the development of a wide field of research focusing on the prevention of human–robot impacts and/or the minimization of related risks or their consequences. This paper presents a review of the main safety systems that have been proposed and applied in industrial robotic environments that contribute to the achievement of safe collaborative human–robot work. Additionally, a review is provided of the current regulations along with new concepts that have been introduced in them. The discussion presented in this paper includes multi-disciplinary approaches, such as techniques for estimation and the evaluation of injuries in human–robot collisions, mechanical and software devices designed to minimize the consequences of human–robot impact, impact detection systems, and strategies to prevent collisions or minimize their consequences when they occur.

Data-Driven MPC for Quadrotors

Abstract: Aerodynamic forces render accurate high-speed trajectory tracking with quadrotors extremely challenging. These complex aerodynamic effects become a significant disturbance at high speeds, introducing large positional tracking errors, and are extremely difficult to model. To fly at high speeds, feedback control must be able to account for these aerodynamic effects in real-time. This necessitates a modeling procedure that is both accurate and efficient to evaluate. Therefore, we present an approach to model aerodynamic effects using Gaussian Processes, which we incorporate into a Model Predictive Controller to achieve efficient and precise real-time feedback control, leading to up to 70% reduction in trajectory tracking error at high speeds. We verify our method by extensive comparison to a state-of-the-art linear drag model in synthetic and real-world experiments at speeds of up to 14 m/s and accelerations beyond 4 g.

Safety assurance mechanisms of collaborative robotic systems in manufacturing

Abstract: Collaborative robots (cobots) are robots that are designed to collaborate with humans in an open workspace. In contrast to industrial robots in an enclosed environment, cobots need additional mechanisms to assure humans’ safety in collaborations. It is especially true when a cobot is used in manufacturing environment; since the workload or moving mass is usually large enough to hurt human when a contact occurs. In this article, we are interested in understanding the existing studies on cobots, and especially, the safety requirements, and the methods and challenges of safety assurance. The state of the art of safety assurance of cobots is discussed at the aspects of key functional requirements (FRs), collaboration variants, standardizations, and safety mechanisms. The identified technological bottlenecks are (1) acquiring, processing, and fusing diversified data for risk classification, (2) effectively updating the control to avoid any interference in a real-time mode, (3) developing new technologies for the improvement of HMI performances, especially, workloads and speeds, and (4) reducing the overall cost of safety assurance features. To promote cobots in manufacturing applications, the future researches are expected for (1) the systematic theory and methods to design and build cobots with the integration of ergonomic structures, sensing, real-time controls, and human-robot interfaces, (2) intuitive programming, task-driven programming, and skill-based programming which incorporate the risk management and the evaluations of biomechanical load and stopping distance, and (3) advanced instrumentations and algorithms for effective sensing, processing, and fusing of diversified data, and machine learning for high-level complexity and uncertainty. The needs of the safety assurance of integrated robotic systems are specially discussed with two development examples.