This work considers a large class of systems composed of multiple quadrotors manipulating deformable and extensible cables. The cable is described via a discretized representation, which decomposes it into linear springs interconnected through lumped-mass passive spherical joints. Sets of flat outputs are found for the systems. Numerical simulations support the findings by showing cable manipulation relying on flatness-based trajectories. Eventually, we present an experimental validation of the effectiveness of the proposed discretized cable model for a two-robot example. Moreover, a closed-loop controller based on the identified model and using cable-output feedback is experimentally tested.

In this work, we present a model-based optimal boundary control design for an aerial robotic system composed of a quadrotor carrying a flexible cable. The whole system is modeled by partial differential equations (PDEs) combined with boundary conditions described by ordinary differential equations (ODEs). The proper orthogonal decomposition (POD) method is adopted to project the original infinite-dimensional system on a finite low-dimensional space spanned by orthogonal basis functions. Based on such a reduced order model, nonlinear model predictive control (NMPC) is implemented online to realize both position and shape trajectory tracking of the flexible cable in an optimal predictive fashion. The proposed POD-based reduced modeling and optimal control paradigms are verified in simulation using an accurate high-dimensional finite difference method- (FDM) based model and experimentally using a real quadrotor and a cable. The results show the viability of the POD-based predictive control approach (allowing to close the control loop on the full system state) and its superior performance compared to an optimally tuned PID controller (allowing to close the control loop on the quadrotor state only).

This work studies how force measurement/estimation biases affect the force-based cooperative manipulation of a beam-like load suspended with cables by two aerial robots. Indeed, force biases are especially relevant in a force-based manipulation scenario in which direct communication is not relied upon. First, we compute the equilibrium configurations of the system. Then, we show that inducing an internal force in the load augments the robustness of the load attitude error and its sensitivity to force-bias variations. Eventually, we propose a method for zeroing the load position error. The results are validated through numerical simulations and experiments.

In recent years, robotics has been largely applied to improve the efficiency of logistic processes. Pallets cover a crucial role in the logistic flow, since they represent the main way to store and ship items. When put onto pallets, the items are wrapped with plastic films to protect them and prevent them from falling. Despite being the first and necessary operation for handling the stacked goods, unwrapping—the task of removing the plastic films wrapped around the goods—has not yet been satisfactorily automated. We propose the first robotic solution for autonomous unwrapping of generally shaped pallets, including both homogeneous and heterogeneous pallets. Force and torque measurements are exploited to retrieve information on the collisions between the end-effector and the wrapped items or the plastic film. Based on the contact information, we design a novel reactive planning strategy that makes the unwrapping task effective and robust on pallets with uncertain position or shape. We present the results of an extensive experimental campaign to validate the proposed method. Note to Practitioners—This work is motivated by the fact that unwrapping machines are not yet common on the market. The few commercial examples are usually bulky machines that lack the flexibility to adapt to different and irregularly shaped pallets. Thus, the crucial operation of removing the plastic film around palletized goods is still mainly performed by hand. Blade handling, ladders, and electrostatic shocks are sources of potential injury. We propose a flexible, autonomous unwrapping robot suitable for both cuboid and irregularly shaped pallets. The robot is composed of a robotic arm, a custom cutting end-effector, a vision module, and a suitable planning and control unit. The reduced dimensions allow it to be mounted on a mobile base. The robot has been successfully tested on different pallet configurations. However, extensive testing in real-world scenarios should be carried out to assess both reliability and time efficiency in more realistic working conditions. Moreover, real pallets can reach considerable heights. Thus, a prismatic joint should be integrated to address such cases. Finally, unwrapping in the presence of typical plastic straps and different types of film, e.g., the shrink one, is to be evaluated.

This article studies how parametric uncertainties affect the cooperative manipulation of a cable-suspended beam-shaped load by means of two aerial robots not explicitly communicating with each other. In particular, this article sheds light on the impact of the uncertain knowledge of the model parameters available to an established communicationless force-based controller. First, we find the closed-loop equilibrium configurations in the presence of the aforementioned uncertainties, and then, we study their stability. Hence, we show the fundamental role played in the robustness of the load attitude control by the internal force induced in the manipulated object by nonvertical cables. Furthermore, we formally study the sensitivity of the attitude error to such parametric variations, and we provide a method to act on the load position error in the presence of uncertainties. Eventually, we validate the results through an extensive set of numerical tests in a realistic simulation environment, including underactuated aerial vehicles and sagging-prone cables, and through hardware experiments.

With the improvements in their computational and physical intelligence, robots are now capable of operating in real-world environments. However, manipulation and grasping capabilities are still areas that require significant improvements. To address this, we introduce a new data-driven grasp planning algorithm called Grasp it Like a Pro 2.0. This algorithm utilizes a small number of human demonstrations to teach a robot how to grasp arbitrary objects. By decomposing objects into basic shapes, our algorithm generates candidate grasps that can generalize to different object's geometry. The algorithm selects the grasp to execute based on a selection policy that maximizes a novel grasp quality metric introduced in this article. This metric considers the complex interdependencies between the predicted grasp, the local approximation produced by the basic shape decomposition, and the gripper used. We evaluate our approach against multiple baselines using different grippers and objects. The results demonstrate the effectiveness of our method in generating and selecting high-quality and reliable grasps. With a soft underactuated robotic hand, our algorithm achieves a 94.0% success rate in 150 grasps across 30 different objects. Similarly, with a rigid gripper, it achieves an 85.0% success rate in 80 grasps across 16 different objects.

Formation control of multi-robot systems has been largely studied due to its wide application domain. Several methods in the literature rely on explicit communication among the robots, which in realistic scenarios may lead to reduced performance or even instability due to delays and packet loss or corruption. Nonetheless, multi-robot coordination based solely on implicit communication has been proposed in cooperative manipulation problems. Taking inspiration from this, we propose a method to solve the formation control problem for a group of ground robots not relying on direct communication among them. Instead, the robots are physically constrained to a common object through elastic cables in order to exploit forces as a means of indirect communication. After deriving the dynamic equations, the control and planning approaches are explained, and the stability of the controlled system is discussed using Lyapunov’s stability theory. Numerical simulations are presented to support the method.

Aerial robotic manipulation is an emergent trend that poses several challenges. To overcome some of these, the DLR cable-Suspended Aerial Manipulator (SAM) has been envisioned. SAM is composed of a fully actuated multi-rotor anchored to a main carrier through a cable and a KUKA LWR attached below the multi-rotor. This work presents a control method to allow SAM, which is a holonomically constrained system, to perform such interaction tasks using a hierarchical control framework. Within this framework, compliance control of the manipulator end-effector is considered to have the highest priority. The second priority is the control of the oscillations induced by, for example, the motion of the arm or physical contact with the environment. A third priority task is related to the internal motion of the manipulator. The proposed approach is validated through simulations and experiments.