As robotics gains popularity, interaction control becomes crucial for ensuring force tracking in manipulator-based tasks. Typically, traditional interaction controllers either require extensive tuning, or demand expert knowledge of the environment, which is often impractical in real-world applications. This work proposes a novel control strategy leveraging Neural Networks (NNs) to enhance the force-tracking behavior of a Direct Force Controller (DFC). Unlike similar previous approaches, it accounts for the manipulator's tangential velocity, a critical factor in force exertion, especially during fast motions. The method employs an ensemble of feedforward NNs to predict contact forces, then exploits the prediction to solve an optimization problem and generate an optimal residual action, which is added to the DFC output and applied to an impedance controller. The proposed Velocity-augmented Artificial intelligence Interaction Controller for Ambiguous Models (VAICAM) is validated in the Gazebo simulator on a Franka Emika Panda robot. Against a vast set of trajectories, VAICAM achieves superior performance compared to two baseline controllers.

This article presents the full dynamic model of the UR10 industrial robot. A triple-stage identification approach is adopted to estimate the manipulator's dynamic coefficients. First, linear parameters are computed using a standard linear regression algorithm. Subsequently, nonlinear friction parameters are estimated according to a sigmoidal model. Lastly, motor drive gains are devised to map estimated joint currents to torques. The overall identified model can be used for both control and planning purposes, as the accompanied robot operating system (ROS)2 software can be easily reconfigured to account for a generic payload. The estimated robot model is experimentally validated against a set of exciting trajectories and compared to the state-of-the-art model for the same manipulator, achieving higher current prediction accuracy (up to a factor of 4.43) and more precise motor gains. The related software is available athttps://codeocean.com/capsule/8515919/tree/v2.

Offline optimal planning of trajectories for redundant robots along prescribed task space paths is usually broken down into two consecutive processes: first, the task space path is inverted to obtain a joint space path, then, the latter is parametrized with a time law. If the two processes are separated, they cannot optimize the same objective function, ultimately providing sub-optimal results. In this paper, a unified approach is presented where dynamic programming is the underlying optimization technique. Its flexibility allows accommodating arbitrary constraints and objective functions, thus providing a generic framework for optimal planning of real systems. To demonstrate its applicability to a real world scenario, the framework is instantiated for time-optimality on Franka Emika’s Panda robot. The well-known issues associated with the execution of non-smooth trajectories on a real controller are partially addressed at planning level, through the enforcement of constraints, and partially through post-processing of the optimal solution. The experiments show that the proposed framework is able to effectively exploit kinematic redundancy to optimize the performance index defined at planning level and generate feasible trajectories that can be executed on real hardware with satisfactory results.Note to Practitioners—The common planning algorithms which consolidated over the years for generating trajectories for non-redundant robots are not adequate to fully exploit the more advanced capabilities offered by redundant robots. This is especially true in performance-demanding tasks, as for robots employed on assembly lines in manufacturing industries, repeatedly performing the same activity. Once the assembly line engineer has defined the tool path in the task space, our planning algorithm unifies inverse kinematics and time parametrization so as to bring the manipulator at its physical limits to achieve specific efficiency goals, being execution time the most typical one. The algorithm is configurable in terms of constraints to consider and objective functions to optimize, therefore it can be easily adapted to optimize other custom-defined efficiency indices, to better respond to the needs of the automation plant. Being based on discrete dynamic programming, the global optimum is guaranteed for a given resolution of the problem. This can be configured by the operator to achieve the desired trade-off between efficiency and planning time. In our experiments, we go through the whole process of planning and executing a time-optimal trajectory on a real robot, and discuss some practical details, such as trajectory smoothness and actuator saturation, aiding the practitioners in deploying our algorithm effectively. Currently, the algorithm’s applicability is limited to those cases where hours are available for planning, hence it is not well-suited for those cases where the robot activity has to change frequently. By replacing the underlying dynamic programming engine with a different methodology, such as randomized algorithms, the planning time could be controlled to be upper-bounded, thus returning the most efficient solution that can be achieved in the time available for reconfiguring the production. Other applications of interest include optimal ground control of space robotic assets and performance benchmarking of online planning algorithms.

The problem of time-optimal planning of redundant robots is commonly solved with a decoupled two-stage approach. Starting from a task space path, at the first stage, the kinematic redundancy is locally optimized according to some performance index, then, at the second stage, the time-optimal parametrization of the resulting joint space path is performed. The performance indices to consider, as well as the redundancy resolution technique to adopt, impact the overall trajectory duration. First- or second-order Jacobian-based local redundancy resolution does not always guarantee satisfactory results at the second stage, in terms of trajectory duration, due to the choice of the initial positions, tuning of algorithm parameters, difficult joint limits management, non-convexity of the optimization problem. In this paper, we propose a global (or integral) approach for redundancy resolution, based on discrete dynamic programming, which further reduces the trajectory tracking time. To cope with the discretization of the redundancy space, the proposed methodology includes a post-processing optimization stage, aimed at smoothing the resulting joint space trajectory, guaranteeing technical feasibility. The approach is validated, in simulation, on a three-degrees-of-freedom planar robot executing two-dimensional tasks.

In industrial settings, robots are typically employed to accurately track a reference force to exert on the surrounding environment to complete interaction tasks. Interaction controllers are typically used to achieve this goal. Still, they either require manual tuning, which demands a significant amount of time, or exact modeling of the environment the robot will interact with, thus possibly failing during the actual application. A significant advancement in this area would be a high-performance force controller that does not need operator calibration and is quick to be deployed in any scenario. With this aim, this paper proposes an Actor-Critic Model Predictive Force Controller (ACMPFC), which outputs the optimal setpoint to follow in order to guarantee force tracking, computed by continuously trained neural networks. This strategy is an extension of a reinforcement learning-based one, born in the context of human-robot collaboration, suitably adapted to robot-environment interaction. We validate the ACMPFC in a real-case scenario featuring a Franka Emika Panda robot. Compared with a base force controller and a learning-based approach, the proposed controller yields a reduction of the force tracking MSE, attaining fast convergence: with respect to the base force controller, ACMPFC reduces the MSE by a factor of 4.35.

Collaborative robotics has gained significant traction in the industrial scenario due to its ability to merge human cognitive abilities with robot strength and dexterity. One specific area where this technology is promising is the transportation of heavy and/or bulky objects. In the scenarios where the human leads, physical human-robot interaction triggers cognitive human-robot interaction, by which the robot is called to adapt its behavior to the collaborator’s intention. Based on this principle, this paper introduces a novel control architecture, namely assistive force control (AFC), by which the robot’s purpose is to alleviate the human collaborator’s effort during transportation. Instead of acting on the robot’s motion, the AFC acts on its causes, by intuitively defining assistive forces, which are input to a lower-level direct force controller. We validate the proposed architecture on two real-case transportation scenarios involving an industrial robot collaboratively carrying objects with different subjects. Our preliminary results show that low effort is required for human operators to manipulate heavy objects, confirming that the proposed architecture is well-suited for collaborative transportation in real-world scenarios.

In Smart Logistic Systems, a challenging problem is the control of vehicles that are making radical changes in these environments, allowing flexible adaptation to changing needs. Driven by their impressive capabilities, in this preliminary paper we propose auction-based mechanisms for the mission assignment problem to such vehicles. In addition, we analyse and evaluate different auction mechanisms, considering the fundamental characteristics of the studied application domain. Regarding the latter point, preliminary results to the problem of design/tuning appropriately the auction based algorithms are presented.

This paper deals with the identification of time Petri net systems. An identification algorithm for timed net systems must take into account that the firing of a transition requires not only that the enabling condition is met, as in untimed net systems, but it is also required that the firing interval of a transition is congruent with the observed firing instant times. The key idea behind the approach is to express these conditions by a set of logical propositions that can be directly transformed into linear mixed-integer inequalities. The identification algorithm consists of building the logical propositions from the observed behavior and solving a mixed-integer linear programming problem.

In the above paper[1]there are several errors.

Modern automated systems need breakdown robustness, reorganizability and reconfigurability. These requirements are leading to the development of decentralized control solutions. However, the development of effective decentralized warehouse control systems is still a complex task. Cyber-Physical Systems are emerging as a promising technology for the design of a new kind of manufacturing systems. They consist of smart physical actuators and/or sensors that are reachable with service-oriented technologies, cooperate and negotiate without any defined hierarchical structures. In this paper, cyber-physical systems, seen as service oriented multi-agents, are modeled to obtain a detailed representation of their behavior, in the perspective of replacing the traditional hierarchical architecture with a completely decentralized implementation of warehouse control systems.

In this paper, the model repair of timed discrete-event systems where anomalies may occur is considered. The nominal model is assumed to be known, and a set of observed timed sequences is given. The approach works with time Petri net models and is based on the formulation of a mixed-integer linear programming problem. The repaired model is obtained from the nominal one by adding fault transitions as well as by extending the firing interval of transitions. Note to Practitioners-The data collected from the observation of a discrete-event system are usually given in terms of behavioral sequences that may be fixed or may be increased in the course of the system operation (e.g., due to new experiments or simply to the system running). If this occurs, the nominal model of a system may reveal not consistent with these additional observations if they include anomalies. This paper presents an approach to repair the model of the system in order to make it able to also generate the observed timed faulty behavior. The repaired model can be used to perform fault diagnosis.

Nowadays, it is well recognized that flexibility, modularity and reconfigurability are the main challenges in the design of manufacturing systems. Automated warehouse systems play a key role in such systems and are currently controlled using hierarchical and centralized control architectures and conventional automation programming techniques. In this paper, we present preliminary results in developing a flexible, modular and distributed control architecture for automated warehouse systems using Function Blocks and a cyber-physical system perspective. The implementation can be carried on by both IEC 61131 and IEC 61499 standards.

In this paper a method is proposed to implement controllers and supervisors for manufacturing systems designed by Petri Nets (PNs) on Programmable Logic Controllers (PLCs) using Object Oriented Programming (OOP). In the resulting program each instruction is directly related to the evaluation of a transition, to the update of the marking, or to the enabling of a transition. This preserves the structure of PNs and entails reusability since it allows the user to easily modify the program directly, starting from the modifications made to the PN design.

This paper deals with the identification of time Petri net systems. The proposed algorithm identifies a time Petri net model on the basis of the observed behavior, extending existing approach for untimed systems. A mixed-integer programming problem is formulated to take into account that the firing of a transition requires that the enabling condition is met but, for time net systems, it is also required that the firing interval of a transition is congruent with the observed firing instant times.

Industrial automation is largely based on PLC-based control systems. PLCs are today mostly programmed in the languages of the IEC 61131 standard which are not ready to meet the new challenges of widely distributed automation systems. Currently, an extension of IEC 61131 which includes object oriented programming as well as the new standard IEC 61499 are available. Moreover, service-oriented paradigms where autonomous and interoperable resources provide their functionalities in the form of services that can be accessed externally by clients without knowing the underlining implementation have been presented in the literature. In the supervisory control theory, methodologies based on formal models have been developed to improve the coordination of concurrent and distributed systems. In this paper, an event-driven approach is proposed to improve the design of industrial control systems using commercial PLCs. At a lower level, basic sequences are coded in elementary software objects, called function blocks, providing their functionalities as services. At an upper level, a Petri Net (PN) controller forces the execution of such services according to desired sequences, while by a PN supervisor constraints on the sequences are satisfied.

This paper presents a new approach based on a new Petri net formalism that merges the concepts of Hybrid Petri Nets and Colored Petri Nets to obtain compact models for online monitoring of aerial service robots. The research activity is part of the ongoing European project AIRobots (Innovative Aerial Service Robot for Remote Inspection by Contact, www.airobots.eu). The goal of AIRobots is to develop a new generation of aerial service robots capable to support human beings in activities that require the ability to interact actively and safely with environments not constrained on ground but, indeed, airborne.

An automated warehouse system has two main components: an automated storage and retrieval subsystem consisting of a number of aisles, each one served by a crane, and a picking area which is formed by bays where stock units coming from the aisles are partially emptied by human operators. These two components are connected via an interface area consisting of carousels, conveyors, and buffers. This area is usually modeled as a discrete event system, while the overall system performance depends also on continuous time phenomena. Part I presents a hybrid model based on a new Petri net formalism that merges the concepts of Hybrid Petri Nets and Colored Petri Nets to obtain modular and compact models for these systems. An example is discussed in detail to motivate the introduction of a new formalism. A control oriented simulation tool is also presented. Part II will focus on the application of this formalism to automated warehouse systems analysis and performance evaluation. Finally, a real case study is considered to show the effectiveness of the approach.

An automated warehouse system has two main components: an automated storage and retrieval subsystem consisting of a number of aisles, each one served by a crane, and a picking area which is formed by bays where stock units coming from the aisles are partially emptied by human operators. These two components are connected via an interface area consisting of carousels, conveyors and buffers. This area is usually modeled as a discrete event system, while the overall system performance depends also on continuous time phenomena. In Part I, a hybrid modeling approach based on a new Petri net formalism and a freeware simulation tool have been presented. The concepts of Hybrid Petri Nets and Colored Petri Nets are merged to obtain modular and compact models for automated warehouse systems. Part II now focuses on the application of this formalism to automated warehouse systems analysis and performance evaluation. Liveness analysis is performed by means of a hybrid automaton obtained from the net model. A deadlock prevention policy is synthesized working on an aggregated model. Finally, a real case study is considered to show the effectiveness of the approach.

An automated warehouse system has two main components: an automated storage and retrieval subsystem consisting of a number of aisles, each one served by a crane, and a picking area which is formed by bays where stock units coming from the aisles are partially emptied by human operators. These two components are connected via an interface area consisting of carousels, conveyors and buffers. This area is usually modeled as a discrete event system, while the overall system performance depends also on continuous time phenomena. A hybrid model focusing on the interface area is proposed in this paper to improve the control and the analysis of complex automated warehouse systems. A real case study is considered to show the effectiveness of the approach.

Industrial automation is largely based on PLC-based control systems. PLCs are today mostly programmed in the languages of the IEC 61131 standard which are not ready to meet the new challenges of widely distributed automation systems. Currently, different solutions are available from industry and research: an extension of IEC 61131 which includes object oriented programming, the adoption of the new standard IEC 61499 or the implementation of supervisory control. All these solutions present different problems which are briefly recalled in the paper. To overcome some of these problems in this paper it is shown that a novel approach based on supervisory control and IEC 61131 can be immediately used to meet the new requirements.

Generalized warehouses denote complex warehousing systems where several kind of resources, automated vehicles, manned vehicles and on-foot storemen have to execute a list of moving orders. This paper presents an approach to control these systems, which have an increasing relevance in real world but have not been well studied from a scientific point of view. The approach is based on the simulation of a Colored Timed Petri Net model in order to evaluate a set of dispatching rules used to assign the orders to the available resources.

This paper deals with the problem of identifying a Petri net system given an observed sequence of events generated by it and an observed sequence of output vectors associated to the marking of the measurable places. The problem is not new in the literature. The original contribution of this work consists in the use of the timing information associated to the net so as to improve its identification. The fact that a transition has not fired for a time larger than its expected delay is here exploited to obtain a list of counterexamples, i.e. the strings that does not belong to the language, even if a whole language is not known. This accelerates the identification procedure. The problem of identifying a ¿-free labeled Petri net system is here considered, that is the identification of a net where a label may be associated to more than one transition, and no transition may by labelled with the empty string.

The use of UML throughout the design process of low-cost distributed control systems is here proposed. In absence of a unique standard for the hardware and the software the only way to proceed is to conduct the control system’s design at a high level of abstraction, deferring integration issues to the physical layer implementation phase. The first contribution of the paper is to show how UML can help doing this, by expressing, for example, system’s requirements. Moreover it is here discussed the idea of conferring an “intelligence” to real objects, making them smart objects by combining new technologies with UML, so that they can cooperate to fulfil the desired tasks in a distributed plant. To illustrate the proposal, a case study is presented.

This paper deals with the problem of enforcing disjunction of GMECs on a Petri net (PN) system. The problem of enforcing a conjunction of GMECs is well known; anyway, it has been proved that in presence of uncontrollable transitions a GMEC has to be transformed into a proper disjunction of GMECs to be enforced in a maximally permissive way. On the other hand, some control problems have to be directly specified as a disjunction of GMECs; this is the case when the legal marking belong to a non-convex region. In the literature the problem has been solved by extending the standard enabling rule for PNs by using OR-logic. In this paper we propose a standard PN based controller to enforce a limited non-convex legal marking region under some restrictions.

The problem of enforcing disjunction of Generalized Mutual Exclusion Constraints (GMECs) on a Petri Net (PN) system is discussed in this paper. It has been proved that in presence of uncontrollable transitions a GMEC has to be transformed into a proper disjunction of GMECs to be enforced in a maximally permissive way. In addition, some control problems have to be directly specified as a disjunction of GMECs; this is the case when the legal markings belong to a non-convex region. In the literature the problem has been solved by extending the standard enabling rule for PNs by using OR-logic and Inhibitor Arcs. In this paper we propose an algorithm to synthesize a standard PN based controller to enforce a limited non-convex legal marking region under some restrictions.

We consider a modern warehousing system composed of a number of aisles, each one is served by a crane and sided by a pair of racks, and a shuttle charging one unit load at a time moves along a mono-dimensional path placed orthogonally with respect to the aisles axis. Two control algorithms to optimize the operations of the cranes (moving within the aisles of the warehouse) and the operations of the shuttle (moving on a straight line placed between aisles and the picking/refilling area) respectively, are proposed. To evaluate the performance of the proposed control algorithms we define three different cost indices: the first two indices look to the crane/aisle and shuttle areas as they were independent systems, while the third index measures the performance of the whole warehousing system. Finally, extensive simulations are performed on the model developed by Basile at al. (2001) to show the effectiveness of the proposed control algorithms.

Discusses the problem of controlling a Petri net whose marking cannot be measured but is estimated using an observer. The control objective is that of enforcing a set of generalized mutual exclusion constraints (GMEC) and all transitions are assumed to be controllable. The use of marking estimates (as opposed to the exact knowledge of the actual marking of the plant) leads to a worse performance of the closed-loop system and it may also be the case that, as a result of this, the controlled system reaches a deadlock. We present a general approach, based on siphon analysis, to recover from such an "observer induced" deadlock. The most interesting feature of our approach is that the observer, controller and deadlock recovery algorithms are all based on the same linear algebraic techniques, thus allowing the overall problem to be solved using a single formalism.

Manipulability ellipsoids are effective tools to perform task space analysis of robotic manipulators in terms of velocities, accelerations and forces at the end effector. A new definition of dynamic manipulability ellipsoid for redundant manipulators is proposed which leads to more correct results in evaluating manipulator capabilities in terms of task-space accelerations. The case of manipulators in singular configurations is also analyzed. A case study is presented to illustrate the correctness of the proposed approach for an "easy-to understand" planar arm.

Recent results in the literature have provided an efficient control synthesis technique for the problem of enforcing generalized mutual exclusion constraints on place/transition nets. With this technique both the plant and the controller are described by Petri nets in order to have an useful linear algebraic model for control analysis and synthesis. The synthesis is not computation demanding since it involves only matrix multiplications. Moreover the method has been shown to be maximally permissive in the case of controllable specifications, otherwise the controller may be suboptimal and its structure may not be unique. This paper investigates on and provides an algorithm to compute these control structures and two criteria of suboptimality.

Polytopes are well-known tools to graphically represent force/motion capabilities for a manipulator. This paper focuses on the evaluation of velocity polytopes for redundant fully parallel robots for which a new intermediate space is introduced (in addition to classical joint space and task space) in order to take into account the specific kinematic relationships. In this space, additional constraint are imposed on the available velocities to be consistent with the kinematic constraints due to the redundant kinematic chains. An algorithm to correctly evaluate the task space velocity polytope is given, and numerical results are provided in the case of a planar redundant 3-DOF parallel arm.

Manipulability ellipsoids and polytopes are well-known tools to graphically represents these capabilities. This paper focuses on the evaluation of force capabilities for redundant manipulators, for which additional constraints must be imposed on the available joint torques in order to satisfy the static assumption. An algorithm to correctly evaluate the task space force polytope is given and a new definition of the force ellipsoid is proposed. The obtained results can be applied also to nonredundant manipulators in singular configurations. Numerical results are provided in the case of a planar redundant arm.

The manipulability ellipsoids are used in robotics as a measure of manipulators' performances achievable during task execution. The definition of these geometrical entities is based on the Jacobian of the manipulator, and the physical meaning given to the ellipsoids is related to the capability of the mechanism to apply forces with the end effector or to move the tool in some directions of the task-space. P. Chiacchio et al. (ibid., vol.7, p.678-685, 1991) have extended this concept to the case of cooperating manipulators. Nevertheless, some questions are still open, both with respect to the basic definition and use of manipulability ellipsoids. The commenter shows by simple examples that the use of manipulability ellipsoids for multiarm systems gives misleading results, and he disagrees with the above authors' definition of ellipsoids. The authors defend their work.>

Three schemes are developed which are aimed at achieving cooperative control of multiple arm systems manipulating a common object. The first scheme operates wholly on the object task space variables. The second scheme operates on the joint space variables that can be derived via a kinematic inversion from the cooperative task space variables. The third scheme combines the features of the two by solving the cooperation at the inverse kinematic level and acting the control at the object level. Simulation results are provided for a two-arm planar system to investigate the behavior of the controlled system in the case of inaccurate object modeling.>

New definitions of force and velocity manipulability ellipsoids for multiple-arm systems are given. A suitable kinetostatic formulation for multiple cooperating arms is adopted that allows a global task space description of external and internal forces as well as absolute and relative velocities at the object level. The concept of a force manipulability ellipsoid for a single arm is formally extended to the multi-arm case by regarding the whole system as a mechanical transformer from the extended joint space to the global task space. Kinetostatic duality properties are then exploited to derive velocity manipulability ellipsoids for the multiple-arm system. The proposed method is compared with other approaches using numerical examples.>