• Depalletizing
• Logistics Automation
• Robotics

THAT the passive compliance embedded in their mechanical design and the active compliance conferred by their control laws have been allowing industrial robots out of the cages in which they were traditionally constrained is acknowledged.
What the robots can do once out of their cages is an unfolding story that inspires this Thesis.
Robotics is trying to respond to new industrial needs, such as high flexibility required in warehouses, due especially to e-commerce, the necessity of lifting human workers from the harsh working conditions that new efficiency standards impose, and reducing the environmental impact of our activities. To do so, robots need to move in uncertain and changing environments, sometimes shared with humans, and in general not suitable for the old paradigms of high-velocity and high-precision predefined movements. Physical contacts with the environment are not to be confined at all costs anymore, but they become a useful means for adapting to an uncertain environment, thus enhancing the robot capabilities.
The focus of this Thesis is the study of the interaction forces between the robot and its surroundings, exploiting the role played by those forces in enabling novel logistic applications. The topics range from depalletizing and object picking to object delivery.
First, this Thesis presents a strategy for autonomous pallet unwrapping, namely the removal of the plastic film enclosing the parcels stacked on pallets. This is a key procedure of the intralogistic flow, the automation of which has not yet received much attention. Currently, unwrapping is performed mainly by human operators, due also to the complexity of its planning and control phases. The unwrapping robot considered in this Thesis is composed of a robotic manipulator equipped with a custom cutting end-effector.
A planning method aimed to ensure a successful task execution even when the film profile is uncertain and irregularly shaped is presented. The proposed planning strategy leverages information regarding the robot collisions with the environment. Experimental results are presented to test the method.
After unwrapping the pallets, the underlying items become accessible for manipulation.
This Thesis also studies a planner that enables a novel depalletizing strategy for the depalletizer robot WRAPP-up. The envisioned planner, exploiting contact force information, allows the dual-arm depalletizer to handle a large variety of goods even with non-perfectly known positions. Results of experiments conducted on different items provided by a food-delivery company are reported.
Besides bi-manual depalletizing, grasping smaller objects with a single end-effector is a relevant task also in logistics and one that WRAPP-up can accomplish, too. The problem of grasping a single object with an ad-hoc end-effector has been extensively studied in the literature both from theoretical and experimental viewpoints but it still represents an open problem, especially when no prior model of the objects is provided.
This Thesis presents a method for object picking, particularly suited for soft hands, that can be used to grasp previously unseen objects. The major contribution is a data-driven planner that generates suitable grasps by relying on a reduced database of grasps performed by a skilled operator using the robotic hand. The basic idea is to exploit a skilled human performing experiments using the robotic hand to grasp only a set of basic shapes instead of general objects, dramatically reducing the number of trials.
The approach is then generalized to grasp unknown objects by relying on state-of-the-art decomposition algorithms that allow approximating an object with such basic shapes. The method has been tested with the PISA/IIT SoftHand mounted on a Panda robot and has shown a success percentage of 86.7% over 105 grasps on 21 previously unseen objects.
Physical interaction capabilities can be entrusted also to aerial robots, enabling aerial object manipulation. This may have rather interesting applications in logistics, especially for lastmile delivery in the freight sector, allowing for a schedule not affected by the unpredictable traffic jam, as in the case of road transportation. Cooperative approaches to aerial object manipulation have been widely studied in the literature since they may increase manipulation capabilities and would allow overcoming payload limitations.
However, communication among the robots, required for their coordination, typically increases the system complexity and represents a possible source of low performance and stability issues due to data loss, corruption, and delays.
This Thesis presents a method for the cooperative manipulation of cable-suspended objects not requiring any explicit communication among the robots. Instead, the coordination is enabled by an implicit form of communication that relies on contact-force sensing. The method exploits a leader-follower scheme on admittance-controlled multi-rotors.
First, two robots and a beam load are considered. The stability and passivity of the controlled system are discussed, as well as the effects of parametric uncertainties.
The specific role of the internal force induced on the object by the robots through the cables is highlighted. An extension of the method to more than two robots is presented.
Eventually, the theoretical analysis is validated through numerical simulations, and the results of preliminary experiments on two robots are presented as well.