In this article, we propose FlexHRC+, a hierarchical human-robot cooperation architecture designed to provide collaborative robots with an extended degree of autonomy when supporting human operators in high-variability shop-floor tasks. The architecture encompasses three levels, namely for perception, representation, and action. Building up on previous work, here we focus on an in-the-loop decision-making process for the operations of collaborative robots coping with the variability of actions carried out by human operators, and the representation level, integrating a hierarchical and/or graph whose online behavior is formally specified using first-order logic. The architecture is accompanied by experiments including collaborative furniture assembly and object positioning tasks.

This paper presents the control and perception framework used in the ROBUST EU Horizon 2020 project, aimed at integrate different technologies for developing an underwater autonomous robotic system for exploring deep-sea mining sites. The vehicle firstly collects data of the initial zone of interest and it selects a sub area which is the most probable to contain a manganese nodule field; then it carries out a low altitude survey. When a possible nodule is detected by the cameras, the vehicle lands on the seafloor, allowing the following fixed based manipulation which is designed to perform the nodule analysis. The work reports the implemented control and perception architecture and the preliminary pool experiments results.

Within the European project DexROV the topic of underwater intervention is addressed. In particular, a remote control room is connected through a satellite communication link to surface vessel, which is in turn connected to an UVMS (Underwater Vehicle-Manipulator System) with an umbilical cable. The operator may interact with the system using a joystick or exoskeleton. Since a direct teleoperation is not feasible, a cognitive engine is in charge of handling communication latency or interruptions caused by the satellite link, and the UVMS should have sufficient autonomy in dealing with low level constraints or secondary objectives. To this purpose, a task-priority-based inverse kinematics algorithm has been developed in order to allow the operator to control only the end effector, while the algorithm is in charge of handling both operative and joint-space constraints. This paper describes some preliminary experimental results achieved during the DexROV campaign of July 2017 in Marseilles (France), where most of the components have been successfully integrated and the inverse kinematics nicely run.

Modern manufacturing paradigms introduce the need for robots able to naturally cooperate with humans in an unstructured and dynamic environment. In this article we extend FlexHRC, an architecture for flexible and collaborative manufacturing robots, with an online perception-simulation-planning framework that allows the robot to assess the status of the workspace, keeping track at all times of the stage at which the cooperative manufacturing process is, to identify its next action, to simulate it to check its feasibility and, as a consequence, to dynamically allocate tasks to itself or the human operator. We have tested the FlexHRC with a dual-arm manipulator cooperating with a person to assemble a table with one tabletop and four legs.

In this paper we present a novel co-operative control policy purely for the transportation of large objects in underwater environments using two free floating vehicles, each one endowed with a 7 d.o.f redundant manipulator. Due to the presence of harsh conditions in underwater scenarios, it is extremely important to realize algorithms that depend on a minimal amount of explicit information exchanged by the agent, or without any exchange of information at all. To achieve this goal the control policy proposed in the paper only requires the exchange of six numbers at each time instant, while however exhibiting extremely good performances, inspite of the restraints on the information exchange.

The need for actual autonomy in underwater robotic systems is rapidly increasing. Many challenging issues derive from such a trend, one in all the requirement of coordinately controlling the motion of an underwater floating I-AUV endowing a robotic arm, to accomplish complex manipulation tasks. This work is aimed to present a strategy based on the prioritization of tasks of equality and inequality type, once combined with Dynamic Programming techniques, for coordinately controlling the motion of such I-AUV. A general algorithmic framework is developed and simulative results supporting its resulting effectiveness are presented.

The present work considers modular robotic structures equipped with an embedded control architecture and proposes a distributed kinematic inversion technique enabling the execution of manipulation operations. The presented strategy is not based on role assignments; all the modules are identical from the control point of view and can be therefore added, removed or exchanged on wish, with no impact on the overall control architecture. In addition every module has just to a-priori know a very limited set of local information while can be totally unaware about the characteristics of the remaining part of the chain. All the information needed for coordination are indeed on-line obtained through communication with other modules. No external centralized controller knowing the overall robot geometry and kinematics is necessary. More simply a global self-coordinating behaviour is autonomously established and propagated along the chain through data-exchanges.

The present work deals with complex robotic structures characterized by the presence of an embedded distributed control system. More specifically, every single joint of the kinematic chain is assumed to be equipped with a simple local processing unit for properly driving its motion. As a consequence, each joint and the associated link may be considered as a defective "1-dof only" separately controlled atomic manipulator, which is required to act in team with all the other joints, in order to accomplish a global common task specified in the operational space. In this context the paper proposes a DP-based computationally distributed kinematic inversion technique that, based on a moderate data exchange among the processing units, allows the establishment of a global self-organizing behavior. In this way it becomes possible to execute any motion task by solely exploiting the control capabilities of each local processing unit, while not requiring any centralized knowledge about the overall structure geometry and kinematics

The present work deals with manipulator arms characterized by the presence of an internal, totally distributed, embedded control system. More specifically, every single joint is assumed to be equipped with a simple local processing unit devoted to properly drive its motion, thus allowing to consider each pair constituted by a single joint and the associated link as a defective “1-dof-only”, separately controlled, atomic manipulator. In this perspective, the paper proposes an effective, computationally distributed, control technique that, based on a repeated data exchange among the processing units, establishes a global self-organizing behaviour among the joints which allows to control the motion of the end-effector of the overall arm in the operational space, by solely exploiting the control capabilities of every local processing unit, while not requiring any centralized global knowledge about the overall arm geometry and kinematics.

The present work deals with manipulator arms characterized by the presence of an embedded distributed control system. More specifically, every single joint is assumed to be equipped with a simple local processing unit for properly driving its motion. As a consequence, each joint and the associated link may be considered as a defective "1-dof only" separately controlled atomic manipulator, which is required to act in team with all the other joints in order to accomplish a global common task specified in the operational space. In this context, the paper proposes a computationally distributed kinematic inversion technique that, via the on-line application of a dynamic programming technique (based on a moderate data exchange among the processing units), allows the establishment of a global self-organizing behavior; thus allowing to optimally execute the task by solely exploiting the control capabilities of each local processing unit, while not requiring any centralized knowledge about the overall arm geometry and kinematics.

The paper deals with the problem of suitably coordinating the manoeuvring of a non-holonomic vehicle and the motion of a supported manipulator when the overall system is commanded to execute a given grasping or manipulation task. The objective being clearly that of exploiting the extra dof's offered by the vehicle for cooperatively accomplishing to tasks that otherwise could not be executed by the arm only. Results are obtained by devising an effective technique that, while preserving system modularity, also allows system self-coordination, without explicitly keeping into account the kinematic structure of the underlying subsystems.

This paper describes the most significant design issues related with the development of an embedded FPGA-based control system for a human-like robotic hand. Specific features of the mechanical design of the system are discussed firstly, then a general approach to the problem of controlling the motion of the robotic device is provided. Finally a candidate distributed control architecture is presented.

The paper deals with the problem of suitably coordinating the manoeuvring of a non-holonomic vehicle and the motion of a supported manipulation system (composed by one or two arms) when the overall system is commanded to execute a given grasping or manipulation task. The objective being clearly that of suitably exploiting the extra dof's offered by the vehicle for better accomplishing the assigned task in a cooperative way.

A novel strategy to design time invariant motion controllers for underactuated mobile systems is applied to the position and attitude control of an underactuated 3D vehicle. The idea consists in defining a velocity vector field such that an ideal, fully actuated system would exponentially achieve the control objective by simply following such field. Then a steering law for the given underactuated system is designed such that it is exponentially stabilized parallel to the above mentioned velocity vector field. For the particular problem here addressed, due to the use of polar like coordinates, this method yields a discontinuous control law. Both the design process and the resulting solution have a most clear physical interpretation. Important practical requirements as approaching the target on a null curvature path, avoiding cusps in the whole path and moving in only one given forward direction are easily satisfied within this framework.

An underwater planar vehicle, actuated by rear thrusters and equipped with longitudinal control surfaces which allow the drag coefficient modulation in the sway direction, is considered in a dynamic setting. The maneuvring controls for the vehicle, in order to reach the required final position and attitude are devised by exploiting both the rear thrusters actuation and the capability offered by the admitted presence of the longitudinal, modulable, control surfaces.

The complete design and realization of an underwater manipulation system composed of two cooperating arms has been one of the main goals of the AMADEUS Phase II project, within the framework of the MAST III program on Marine Technology development. In this paper, the directions and methodologies followed within the design of the complete functional and algorithmic control architecture for the workcell is presented in some details, by mainly focusing on its various aspects of modularity, scalability and composability, which presently allow the execution of many different, cooperative, object manipulation tasks.

The problem of grasping and manipulating rigid objects using a multifingered robotic system is dealt with. A hierarchical two-level closed-loop trajectory tracking control strategy is presented in order to achieve the manipulation task, together with a complimentary force control, for object grasping, which makes use of a formulation of grasping forces decomposition.

AMADEUS is a dexterous subsea robot hand incorporating force and slip contact sensing, using fluid-filled tentacles for fingers. Hydraulic pressure variations in each of three flexible tubes (bellows) in each finger create a bending moment, and consequent motion or increase in contact force during grasping. Such fingers have inherent passive compliance, no moving parts, and are naturally depth pressure-compensated, making them ideal for reliable use in the deep ocean. In addition to the mechanical design, development of the hand has also been considered for closed loop finger position and force control, coordinated finger motion for grasping, force and slip sensor development/signal processing, and reactive world modelling/planning for supervisory "blind grasping". Initially, the application focus is for marine science tasks, but broader roles in offshore oil and gas, salvage, and military use are foreseen. Phase I of the project has been completed, with the construction of a first prototype. This paper summarizes the developments in Phase I. Further details for each area of investigation are referenced.

This paper presents the prototype of the DIST robotic hand developed at the Graal-Lab of the University of Genova. In the current version, the hand is formed by a palm supporting four fingers. Each finger has four rotational degrees of freedom, and is equipped with custom built position sensors using Hall-effect transducers. The actuation of each finger is done using six tendons driven by five computer controlled DC motors. A kinematic analysis has been performed using simulation tools, with the goal of building a mechanism with size and mobility similar to those of human hand.

The problem of driving a Dubin's car (1957) along a given path is considered. In order to model a realistic road-following problem, the car is supposed to move forward only and to have bounds on the turning radius (Dubin's car). We propose a discontinuous control scheme on the angular velocity of the vehicle, based on the theory of sliding modes, that achieves the goal of tracking an unknown path relying on measurements of the current distance from the path and of the heading angle error.

This paper presents a new control model, ideal for teleoperation, which takes into account the problems arising with heavily delayed communicating systems. This is done with the help of a general purpose neural network and a specific dedicated training structure which is able to train the network and to minimize the network errors due to convergence problems. The principal aim of this model is to give more stability to the data flux reducing the uncertainties due to a variable delay present in the communication support.

A two layered hierarchical architecture is considered as the fundamental scheme for the closed loop control of robots in operational space. By considering different kinds of information transfer from the outer to the inner controllers, it is shown that the architecture can take into account a wide class of control schemes, then the analysis of their stability and robustness properties can be performed in a unified framework. Then on this basis some general results concerning such properties are given.

In this work we propose a Lyapunov based design of velocity observers and the controller for stable trajectory tracking by a robotic manipulator. It is shown how the proposed design is exponentially stable over a finite domain, and, in the high gain approximation, exponentially stable in the large domain. Moreover, the design proposed leads naturally to a reduced order observer structure, with considerable implementation advantages.

This paper describes a technique for path planning in environments cluttered with obstacles for mobile robots with nonholonomic kinematics and bounded trajectory curvature (i.e., limited turning radius). The method is inspired by the results of Reeds and Shepp (1990) regarding shortest paths of bounded curvature in absence of obstacles. It is proved that, under suitable assumptions, the proposed technique provides the shortest path of bounded curvature among polygonal objects for a particular class of vehicles (circular unicycles of radius h and minimum turning radius /spl rho//sub min//spl les/h). Although the class of vehicles this theoretical result is restricted to is rather narrow, the proposed planner can be satisfactorily applied to other nonholonomic vehicles yielding good practical results.

The problem of grasping rigid objects using robot hands is addressed. In particular, a representation of the space of the internal forces is given. Within this framework, an algorithm is proposed for the determination of feasible grasp and manipulation forces. sufficient conditions for the solution of this problem of coordinated manipulation are given.>

Learning control algorithms rely on offline estimates of generalized position, velocity, and acceleration errors. This problem is addressed in the framework of the spectral analysis of singular descriptor systems, and a class of robust estimates is introduced. The use of these estimators in the implementation of learning control algorithms has been tested on a prototype manipulator, and experimental results are reported. It is concluded that iterative learning control can be successfully implemented on real manipulators provided that robust estimators for velocity and acceleration are given.>

One of the main problems in robotic research is planning. Different approaches have been considered, ranging from global to local planning. In the authors' approach, the key idea is that of planning movements of redundant robots, which involves both robotic and AI aspects. Pushing is used as a case study and sensorial feedback is considered in order to provide the planner with updated information on the dynamic evolution of the scene and to be able to deal with a priori unknown objects.>

An iterative learning technique is applied to robot manipulators, using an inherently nonlinear analysis of the learning procedure. In particularly, a 'high-gain feedback' point of view is utilized to prove the possibility of setting up uniform upper bounds to the trajectory errors occurring at each trial. The subsequent analysis of convergence shows that apart from minor conditions, the existence of a finite (but not necessarily narrow) bound on the trajectory deviations can substantially suffice to guarantee the zeroing of the errors after a sufficient number of trials. This in turn leaves open the possibility of obtained the exact tracking of the desired motion, even in the presence of moderate values assigned to the feedback gains.>

In the class of multiple-access schemes utilizing channel feedback information, one has generally to face a multistep control problem. In order to simplify the structure of the access strategies, a single-step horizon can be considered, so that the goal becomes the maximization of the probability of transmission at each slot. However, even in this case the optimal solution turns out to be intractable for a large number of stations. To avoid this intractability some simplifying assumptions can be made at the price of a certain degree of suboptimality. Our approach is based on an independence assumption and yields a scheme which can be termed an independent stations approach (ISA).

The paper presents the basic ideas underlying a learning methodology for the control of movements of robotic manipulators. It is based on the use of repeated trials of tracking of a preassigned trajectory. Sufficient conditions for the convergence of the algorithm are presented.