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.

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.

Operations involving safe interactions in unstructured environments require robots with adapting behaviors. Compliant manipulators are a promising technology to achieve this goal. Despite that, some classical control problems such as following a trajectory are still open. A typical solution is to compensate the system dynamics with feedback loops. However, this solution increases the effective robot stiffness and jeopardizes the safety property provided by the compliant design. On the other hand, purely feedforward approaches can achieve good tracking performance while preserving the robot intrinsic compliance. However, a feedforward control framework for robots with passive elastic joints is still missing. This article presents an iterative learning control algorithm for purely feedforward trajectory tracking for compliant underactuated arms. Each arm is composed of active elastic joints and a generic number of passive ones connected through rigid links. We prove the convergence of the iterative method, also in the presence of uncertainties and bounded disturbances. Different output functions are analyzed providing conditions, based on the system inertial properties that ensure the algorithm applicability. Additionally, an automatic selection of the learning gain is proposed. Finally, we extensively validate the theoretical results with simulations and experiments.

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.

Trajectory tracking of flexible link robots is a classical control problem. Historically, the link elasticity was considered as something to be removed. Hence, the control performance was guaranteed by adopting high-gain feedback loops and, possibly, a dynamic compensation with the result to stiffen up the dynamic behavior of the robot. Nowadays, robots are pushed more and more towards a safe physical interaction with a less and less structured environment. Hence, the design and control of the robots moved to an on-purpose introduction of highly compliant elements in the robot bodies, the so-called soft robotics, and towards control approaches that aim to provide the tracking performance without a substantial change in the robot dynamic behavior. Following this approach, we present an iterative learning control that relies mainly on a feedforward component, hence preserves the robot dynamics, for trajectory tracking of a one-link flexible arm. We provide a condition, based on the system dynamics and similar to the Strong Inertially Coupled property, that ensures the applicability of the proposed control method. Finally, we report simulation and experimental tests to validate the theoretical results.