A decentralized strategy for object transportation is presented, assuming that the object is grasped by a team of N cooperative manipulators. The proposed strategy consists of two steps. First, each robot estimates the wrenches applied to the object by all the others robots, even without all-to-all communication. Second, an admittance control scheme is used to limit internal wrenches, preventing excessive stresses that could affect manipulation stability and object integrity. Stability is proven under the assumption of a spring connection between each robot end-effector and its grasping point on the object. A work cell with two 7-degree-of-freedom (DOF) and one 6-DOF robotic manipulators was used to validate the strategy. Experimental results show that the controller effectively reduces internal wrenches, confirming the feasibility and robustness of the decentralized approach in cooperative manipulation.

Artificial Intelligence (AI) is an enabling technology in the context of Industry 4.0. In particular, the automotive sector is among those who can benefit most of the use of AI in conjunction with advanced vision techniques. The scope of this work is to integrate deep learning algorithms in an industrial scenario involving a robotic Peg-in-Hole task. More in detail, we focus on a scenario where a human operator manually positions a carbon fiber automotive part in the workspace of a 7 Degrees of Freedom (DOF) manipulator. To cope with the uncertainty on the relative position between the robot and the workpiece, we adopt a three stage strategy. The first stage concerns the Three-Dimensional (3D) reconstruction of the workpiece using a registration algorithm based on the Iterative Closest Point (ICP) paradigm. Such a procedure is integrated with a semantic image segmentation neural network, which is in charge of removing the background of the scene to improve the registration. The adoption of such network allows to reduce the registration time of about 28.8%. In the second stage, the reconstructed surface is compared with a Computer Aided Design (CAD) model of the workpiece to locate the holes and their axes. In this stage, the adoption of a Convolutional Neural Network (CNN) allows to improve the holes’ position estimation of about 57.3%. The third stage concerns the insertion of the peg by implementing a search phase to handle the remaining estimation errors. Also in this case, the use of the CNN reduces the search phase duration of about 71.3%. Quantitative experiments, including a comparison with a previous approach without both the segmentation network and the CNN, have been conducted in a realistic scenario. The results show the effectiveness of the proposed approach and how the integration of AI techniques improves the success rate from 84.5% to 99.0%.

Social robots can be used for assisting children managing chronic illness through education and encouragement. In this paper, we present a study about the use of a NAO robot in the therapy with children diagnosed with an autism spectrum disorder (ASD). In particular, we propose an approach to track the gaze of the child while she/he is interacting with the robot. We adopt a two level architecture, where the high-levels task in the treatment protocol are decided by the therapist and the robot performs autonomously the low-level tasks. We carried out a preliminary evaluation of the proposed approach involving neurotypical and an autistic children.

In this paper, a novel decentralized leader-follower control scheme for multi-agent systems is devised, where each agent communicates only with a subset of neighboring mates. The goal is to track assigned trajectories for the centroid and the formation of the system. The desired trajectories are known only by a subset of agents, named leaders: the other agents, the followers, are required to estimate the desired trajectories based on a dynamic consensus scheme. Then, the desired trajectories to be tracked by each agent are computed from the estimated trajectories for the centroid and the formation and a simple local control loop is adopted to track the former. Stability and performance are analyzed and experiments are run on Robotarium platform to show the effectiveness of the approach and the effect of different parameters on the achieved performance.

In this paper an experimental study of set-based task-priority kinematic control for a dual-arm mobile robot is developed. The control strategy for the coordination of the two manipulators and the mobile base relies on the definition of a set of elementary tasks to be properly handled depending on their functional role. In particular, the tasks have been grouped into three categories: safety, operational and optimization tasks. The effectiveness of the resulting task hierarchy has been validated through experiments on a Kinova Movo robot, in a domestic use case scenario.

This paper presents a method to cope with autonomous assembly tasks in the presence of uncertainties. To this aim, a Peg-in-Hole operation is considered, where the target workpiece position is unknown and the peg-hole clearance is small. Deep learning based hole detection and 3D surface reconstruction techniques are combined for accurate workpiece localization. In detail, the hole is detected by using a convolutional neural network (CNN), while the target workpiece surface is reconstructed via 3D-Digital Image Correlation (3D-DIC). Peg insertion is performed via admittance control that confers the suitable compliance to the peg. Experiments on a collaborative manipulator confirm that the proposed approach can be promising for achieving a better degree of autonomy for a class of robotic tasks in partially structured environments.

The paper presents a multiple task-priority inverse kinematics algorithm for a dual-arm aerial manipulator. Both tasks defined as equality constraints and inequality constraints are handled by means of a singularity robust method based on the Null-Space based Behavioral control. The proposed schema is constituted by the inverse kinematics control, that receives the desired behavior of the system and outputs the reference values for the motion variables, i.e. the UAV pose and the arm joints position, and a motion control, that computes the vehicle thrusts and the joint torques. The method has been experimentally validated on a system composed by an underactuated aerial hexarotor vehicle equipped with two lightweight 4-DOF manipulators, involved in operations requiring the coordination of the two arms and the vehicle.

The paper presents a Distributed Fault Detection and Isolation strategy for a team of mobile manipulators performing a cooperative mission. The overall system relies on an observer-controller scheme where each robot estimates the global state of the team through a distributed observer; then, the global state estimate is used by each robot to compute the estimated local input so as to achieve a specific global task. The observer-controller scheme also allows to define a set of residual vectors that can be used by the robots to detect and isolate faults affecting any member of the team, even if not in direct communication, and without increasing the computational burden and the information exchange. The approach is validated via numerical simulations with a team of four mobile manipulators performing a transportation mission.

The paper presents a two stages distributed algorithm for cooperative manipulating an unknown object rigidly grasped by mobile manipulators, in the absence of both a central unit and any explicit information exchange among robots. In the first stage, robots cooperatively estimate the object kinematic and dynamic parameters by properly moving the object or applying specific contact wrenches. In the second stage, the estimated parameters are used in a distributed cooperative algorithm aimed at controlling the object pose while limiting both the squeezing wrenches exerted by the manipulators and the wrench exerted by the environment on the object. Numerical simulations demonstrate the feasibility of the approach.

This paper presents the design, control, and experimental validation of a novel fully-actuated aerial robot for physically interactive tasks, named Tilt-Hex. We show how the Tilt-Hex, a tilted-propeller hexarotor is able to control the full pose (position and orientation independently) using a geometric control, and to exert a full-wrench (force and torque independently) with a rigidly attached end-effector using an admittance control paradigm. An outer loop control governs the desired admittance behavior and an inner loop based on geometric control ensures pose tracking. The interaction forces are estimated by a momentum based observer. Control and observation are made possible by a precise control and measurement of the speed of each propeller. An extensive experimental campaign shows that the Tilt-Hex is able to outperform the classical underactuated multi-rotors in terms of stability, accuracy and dexterity and represent one of the best choice at date for tasks requiring aerial physical interaction.

In this paper, a general framework to control in a distributed way a system composed by multiple robots is proposed. Each robot is characterized by a discrete-time linear dynamics, and the whole system is controlled via a linear static feedback law with a feed-forward term. Usually, this form of the global control input requires a central unit or an all-to-all communication for computing the local control input of each robot. To counteract the lack of a central unit, each robot estimates, via a local observer, the overall state of the team, and such an estimate is used to compute its local control input as in the case a central unit was present. Two simulations case studies are provided in the framework of multi-robot optimal control and formation control.

In this paper a three layer control architecture for multiple aerial robotic manipulators is presented. The top layer, on the basis of the desired mission, determines the end-effector desired trajectory for each manipulator, while the middle layer is in charge of computing the motion references in order to track such end-effectors trajectories coming from the upper layer. Finally the bottom layer is a low level motion controller, which tracks the motion references. The overall mission is decomposed in a set of elementary behaviors which are combined together, through the Null Space-based Behavioral (NSB) approach, into more complex compounds behaviors. The proposed framework has been tested conducting an experimental campaign.

In this paper, an impedance control scheme for aerial robotic manipulators is proposed, with the aim of reducing the end-effector interaction forces with the environment. The proposed control has a multi-level architecture, in detail the outer loop is composed by a trajectory generator and an impedance filter that modifies the trajectory to achieve a complaint behaviour in the end-effector space; a middle loop is used to generate the joint space variables through an inverse kinematic algorithm; finally the inner loop is aimed at ensuring the motion tracking. The proposed control architecture has been experimentally tested.

This work tackles the problem of controlling an Unmanned Aerial Vehicle equipped with a robotic Manipulator and it has been developed within the framework of the EU-funded ARCAS (Aerial Robotics Cooperative Assembly System) project. A behavioral control, based on the Null Space-based Behavioral (NSB) paradigm, is proposed to tackle the coordination between the arm and vehicle motions. To this aim, a set of basic functionalities (called elementary behaviors) are designed and combined in a priority order to attain complex tasks (called compound behaviors). The proposed controller has been experimentally validated on a multirotor aircraft with an attached 6 Degree of Freedoms manipulator. Two experimental case studies, involving several compound behaviors, have been reported and the results show the effectiveness of the approach.

In this paper, an impedance control scheme for cooperative quadrotors with robotic arms is proposed in order to limit both the contact forces, due to the object/environment interaction, and the internal forces, due to the manipulators/ object interaction. To this aim, two impedance filters are used to determine the reference trajectories for manipulator end effectors: the first is aimed at conferring a compliant behavior at the object level (external impedance), while the second filter, is aimed at avoiding large internal loading of the object (internal impedance). Such trajectories are fed to a motion controller including an inverse kinematics algorithm and a PD controller with gravity compensation. The effectiveness of the proposed approach is then verified in simulation.

This paper presents a solution to the problem of decentralized control, fault detection and isolation for teams of cooperative autonomous mobile vehicles. The strategy is carried out in the discrete time domain. A local observer is used by each agent to estimate the overall state of the team. This estimate is, then, used both to compute its local control input and isolate faulty teammates, even in absence of direct communication with them. For diagnosis purposes, a set of residual vectors, each of them sensible to a fault occurring on a single vehicle, is designed and an adaptive threshold is derived in order to avoid false alarms. The approach is validated via numerical simulations involving 4 vehicles moving in formation in a 3D environment.

This paper presents a control framework for arm/hand systems aimed at controlling internal forces exchanged between the fingers and the grasped object, and enforcing a compliant behavior in presence of environmental interactions. A dynamic planner computes the motion references for the fingers by using the feedback of the contact forces, while an impedance control, in which dynamic effects exerted by the hand on the wrist are explicitly taken into account, is designed for the arm. The approach is experimentally validated on a 7-DOFs Barrett WAM with a Barrett Hand280.

The paper deals with the problem of decentralized fault detection, isolation and recovery for teams of networked robots. The proposed strategy is a combination of distributed and local approaches that allow the robots to deal with both recoverable and unrecoverable faults. A local adaptive fault observer is used to locally compensate recoverable faults, while a distributed fault detection and isolation strategy is used to allow each robot to detect unrecoverable faults on other teammates even if not directly connected; once the faulty robots have been isolated, they are removed from the team and the mission is rearranged. Results of numerical simulations and experiments involving a team of 5 mobile robots are provided to show the effectiveness of the approach.

In this paper, a kinematic model for motion coordination and control of a redundant robotic dual-arm/hand system is derived, which allows to compute the object pose from the joint variables of each arm and each finger as well as from a suitable set of contact variables. This model is used to design a two-stage control scheme to achieve a desired object motion and maintain desired normal contact forces applied to the object. Several secondary tasks are accomplished through a prioritized task sequencing management of the whole system redundancy. A simulation case study is presented to demonstrate the effectiveness of the proposed approach.