In human-robot collaboration, robots must adapt their motions to dynamic environments while ensuring safety, predictability, and compliance with physical constraints. Frequent trajectory replanning can compromise motion smoothness and lead to unsafe or uncomfortable interactions. This paper introduces THOR (Trajectory receding HOrizon interpolatoR), a model predictive control algorithm in joint space that explicitly minimizes jerk during execution to generate smooth, dynamically feasible trajectories. THOR continuously adapts the trajectory in response to real-time changes, such as path replanning or safety-induced slowdowns, while respecting joint limits on position, velocity and acceleration. THOR is validated through extensive simulation and real-world experiments with a 6-DoF collaborative robotic cell. Results show that THOR significantly reduces jerk and improves motion continuity compared to standard approaches, making it particularly well-suited for responsive and safe behavior in human-robot collaboration scenarios.

This article aims to design an assistive controller for physical Human-Robot Interaction (pHRI) based on Dynamic Cooperative Game Theory (DCGT). In particular, a distributed Model Predictive Control (dMPC) is formulated based on the DCGT principles (GT-dMPC). For proper implementation, one crucial piece of information regards human intention, which is defined as the desired trajectory that a human wants to follow over a finite rolling prediction horizon. To predict the desired human trajectory, a learning model is composed of cascaded Long-Short Term Memory (LSTM) and Fully Connected (FC) layers (RNN$+$FC). Iterative training and Transfer Learning (TL) techniques are proposed to adapt the model to different users. The behavior of the proposed GT-dMPC framework is thoroughly analyzed with simulations to understand its applicability and the tuning of its parameters for a pHRI assistive controller. Moreover, real-world experiments were carried out on a UR5 robotic arm equipped with a force sensor was installed. First, a brief validation of the RNN$+$FC model integrated with the GT-dMPC is proposed for the iterative procedure and the TL. Finally, an application scenario is proposed for co-manipulating two objects and comparing the obtained results with other controllers typically used in the pHRI. Results show that the proposed controller reduces the required force of the human in completing tasks, even in the presence of unknown and different loads and inertia. Moreover, the proposed controller allows for precise reaching of the target point and does not introduce any undesirable oscillations. Finally, a subjective questionnaire shows that the proposed controller is, in general, preferred by different users.Note to Practitioners—This work presents a method to design an assistive controller to help a human perform physically coupled shared tasks with a robot. The target applications of this work are co-handling tasks of large or heavy objects. Such tasks require two agents to be performed easily, and the proposed work aims to make the robot a companion for the human partner. The proposed approach also quickly adapts to new users or tasks, making it feasible for real production systems or daily scenarios. Another possible target application is the co-manipulating large flexible components such as carbon fiber plies. This application would require small modifications, particularly in how the force is exchanged. Some additional/different sensors should be used, such as vision to map object deformations with virtual forces. The present work does not directly consider these kinds of applications. Indeed, this work strictly relies on force measurements that are not reliable when dealing with flexible materials, at least in a compression state. Such an issue will be investigated in future works by using vision systems to measure a virtual force that allows this method to be applicable even in the case of flexible components.

This paper addresses motion replanning in human-robot collaborative scenarios, with an emphasis on reactivity and safety-compliant efficiency. While existing human-aware motion planners perform well in structured environments, they often struggle with unpredictable human behavior. This can result in safety measures that hinder the robot’s performance and overall throughput. This study combines reactive path replanning and a safety-aware cost function, enabling the robot to adapt its path to the changes in the scene in real-time. This solution reduces the execution time and trajectory slowdowns while ensuring safety. Simulations and real-world experiments show the method’s effectiveness compared to standard human-robot cooperation approaches, with efficiency enhancements of up to 60%.

This work presents a novel impedance controller for physical human-robot interaction. The controller exhibits two behaviors based on the force applied to the end-effector. Low forces result in temporary (elastic) motion, canceled once the force is removed to accommodate for the temporary need for displacement. In contrast, high forces give rise to permanent (plastic) deformations of the trajectory to deal with permanent path modification. The control method is based on a LuGre friction model, which exhibits a combination of the two behaviors sought. The model has been improved to distinguish clearly between elastic and plastic motion and extended to the 3D Cartesian space. Experiments on a real robot have been performed to validate the proposed method.

Predicting human motion is vital for enhancing safety and efficiency in human-robot collaboration. Researchers have dedicated significant efforts to developing accurate human models, often involving optimization and task-specific information. However, regardless of complexity, all models come with uncertainties that robots need to recognize to make informed decisions. This paper examines the performance of two simple models using the Unscented Kalman Filter (UKF) to filter and predict future human poses. Moreover, a combined version of the models is implemented using an Interacting Multiple Model (IMM) estimator. The objective is to evaluate the algorithms' prediction accuracy and uncertainty across various human-robot interaction scenarios under different operating conditions. This analysis identifies suitable settings where the simple model can be effective and highlights situations where a more complex system might be necessary.

Thermal Energy Storage (TES) systems represent a cornerstone in the development of sustainable energy solutions, enhancing the flexibility in energy management. Heat exchangers are pivotal components of TES systems and they require precise control to optimize efficiency. Proportional-Integral-Derivative (PID) controllers are commonly employed for heat exchangers control but, since these systems are subject to time-delays and nonlinearities, their performance leaves room for enhancement. This paper explores the potential of Proportional-Integral-Derivative-Acceleration (PIDA) control, also known as PIDD or PIDD2, that introduces a double derivative (acceleration) component to the traditional PID structure. The capability of PIDA control in improving system performance compared to conventional PID controllers is evaluated through a simulation study performed on a realistic heat exchanger simulator. The obtained results show the efficacy of PIDA control in enhancing control performance without adding additional complexity or tuning efforts.

The industry needs controllers that allow smooth and natural physical Human-Robot Interaction (pHRI) to make production scenarios more flexible and user-friendly. Within this context, particularly interesting is Role Arbitration, which is the mechanism that assigns the role of the leader to either the human or the robot. This paper investigates Game-Theory (GT) to model pHRI, and specifically, Cooperative Game Theory (CGT) and Non-Cooperative Game Theory (NCGT) are considered. This work proposes a possible solution to the Role Arbitration problem and defines a Role Arbitration framework based on differential game theory to allow pHRI. The proposed method can allow trajectory deformation according to human will, avoiding reaching dangerous situations such as collisions with environmental features, robot joints and workspace limits, and possibly safety constraints. Three sets of experiments are proposed to evaluate different situations and compared with two other standard methods for pHRI, the Impedance Control, and the Manual Guidance. Experiments show that with our Role Arbitration method, different situations can be handled safely and smoothly with a low human effort. In particular, the performances of the IMP and MG vary according to the task. In some cases, MG performs well, and IMP does not. In some others, IMP performs excellently, and MG does not. The proposed Role Arbitration controller performs well in all the cases, showing its superiority and generality. The proposed method generally requires less force and ensures better accuracy in performing all tasks than standard controllers.Note to Practitioners—This work presents a method that allows role arbitration for physical Human-Robot Interaction, motivated by the need to adjust the role of leader/follower in a shared task according to the specific phase of the task or the knowledge of one of the two agents. This method suits applications such as object co-transportation, which requires final precise positioning but allows some trajectory deformation on the fly. It can also handle situations where the carried obstacle occludes human sight, and the robot helps the human to avoid possible environmental obstacles and position the objects at the target pose precisely. Currently, this method does not consider external contact, which is likely to arise in many situations. Future studies will investigate the modeling and detection of external contacts to include them in the interaction models this work addresses.

Combining symbolic and geometric reasoning in multiagent systems is a challenging task that involves planning, scheduling, and synchronization problems. Existing works overlooked the variability of task duration and geometric feasibility intrinsic to these systems because of the interaction between agents and the environment. We propose a combined task and motion planning approach to optimize the sequencing, assignment, and execution of tasks under temporal and spatial variability. The framework relies on decoupling tasks and actions, where an action is one possible geometric realization of a symbolic task. At the task level, timeline-based planning deals with temporal constraints, duration variability, and synergic assignment of tasks. At the action level, online motion planning plans for the actual movements dealing with environmental changes. We demonstrate the approach's effectiveness in a collaborative manufacturing scenario, in which a robotic arm and a human worker shall assemble a mosaic in the shortest time possible. Compared with existing works, our approach applies to a broader range of applications and reduces the execution time of the process.

The programming complexity of industrial robots significantly limits their expansion in complex industrial applications. Consequently, research has focused extensively on the development of intuitive programming methods.This article proposes a framework for task-oriented programming introducing an intuitive and modular task structure. The framework provides an algorithm able to optimize the execution parameter of the tasks. A physical simulation environment allows accurate parameter optimization in a virtual environment providing feasible and safe results. Efficiency tests demonstrated the method’s effectiveness, and a comparison with genetic and Bayesian -based ones have been conducted.

Autotuning and system parameters monitoring are crucial aspects of modern motion control algorithms. At-the-edge controllers need to detect system changes and perform autotuning autonomously without requiring excessive computational burdens. Two algorithms to estimate the main dynamics of mechanical systems using integral figures of merit are proposed. The algorithms implementations are analyzed in offline and online modes. They have been tested in simulation, using an elastic model as a testbed, and in a hardware-in-the-loop PLC-controlled system. The results show the effectiveness of the methods compared to a recursive least square method.

With the spread of robots in unstructured, dynamic environments, the topic of path replanning has gained importance in the robotics community. Although the number of replanning strategies has significantly increased, there is a lack of agreed-upon libraries and tools, making the use, development, and benchmarking of new algorithms arduous. This paper introduces OpenMORE, a new open-source ROS-based C++ library for sampling-based path replanning algorithms. The library builds a framework that allows for continuous replanning and collision checking of the traversed path during the execution of the robot trajectory. Users can solve replanning tasks exploiting the already available algorithms and can easily integrate new ones, leveraging the library to manage the entire execution.

This work addresses human intention identification during physical Human-Robot Interaction (pHRI) tasks to include this information in an assistive controller. To this purpose, human intention is defined as the desired trajectory that the human wants to follow over a finite rolling prediction horizon so that the robot can assist in pursuing it. This work investigates a Recurrent Neural Network (RNN), specifically, Long-Short Term Memory (LSTM) cascaded with a Fully Connected layer. In particular, we propose an iterative training procedure to adapt the model. Such an iterative procedure is powerful in reducing the prediction error. Still, it has the drawback that it is time-consuming and does not generalize to different users or different co-manipulated objects. To overcome this issue, Transfer Learning (TL) adapts the pre-trained model to new trajectories, users, and co-manipulated objects by freezing the LSTM layer and fine-tuning the last FC layer, which makes the procedure faster. Experiments show that the iterative procedure adapts the model and reduces prediction error. Experiments also show that TL adapts to different users and to the co-manipulation of a large object. Finally, to check the utility of adopting the proposed method, we compare the proposed controller enhanced by the intention prediction with the other two standard controllers of pHRI.

The goal of the control system in lift application in high-speed/high-rise applications is to maximize speed without sacrificing passengers comfort. Lifts are affected by position-based disturbances, which could be compensated by employing a repetitive control strategy. Lifts also have nonlinearities due to the rope mass and stiffness variation with respect to the position. This paper analyzes the performance of LTI position-based repetitive control strategies in lift applications. Simulation with a nonlinear model of the lift shows that repetitive control strategies could improve control performance. In particular, Gaussian Process Repetitive Control is less sensitive to the nonlinearities and provides fewer oscillations.

Nowadays, many applications involving humans and robots working together require physical interaction. It is known that, during an interaction, the mutual understanding and knowledge of the partner’s goal improves and allows natural interaction. For this purpose, this work proposes Inverse Optimal Control (IOC) to recover the cost function of a human performing a reaching task with a robot in passive impedance control. This work presents the potentialities and limitations of the presented IOC method to describe human objectives. This work represents a preparatory study toward smooth and natural physical Human-Robot Interaction (pHRI), intending to understand the basic information on humans’ behavior.

The problem addressed in this work is the arbitration of the role between a robot and a human during physical Human-Robot Interaction, sharing a common task. The system is modeled as a Cartesian impedance, with two separate external forces provided by the human and the robot. The problem is then reformulated as a Cooperative Differential Game, which possibly has multiple solutions on the Pareto frontier. Finally, the bargaining problem is addressed by proposing a solution depending on the interaction force, interpreted as the human will to lead or follow. This defines the arbitration law and assigns the role of leader or follower to the robot. Experiments show the feasibility and capabilities of the proposed control in managing the human-robot arbitration during a shared- trajectory following task.

The paper introduces a robotic manipulation framework suitable for the execution of manipulation tasks. Based on the ROS platform, the framework provides advanced motion planning and control functionalities for robotic systems to guarantee a high level of autonomy during the execution of an action. The integrated motion planning module can handle multiple motion planners to generate collision-free trajectories for a given planning scene that can be dynamically uploaded. In the same way, the robot controllers can be changed online on the base of the robot behavior required by the action under execution. The motion control of the robotic system is fully demanded to the manipulation framework relieving the upper control layers from the management of low-level functionalities and the task geometrical information. The framework can be used downstream to a task planner or as a standalone library to simplify the robot programming in complex manipulation tasks.

Robots working in proximity of humans often need to change their motion to avoid collisions and interference with the operators. This paper uses a path re-planning approach to change the robot path online when the human operator is in the robot way. The method exploits a set of pre-computed paths to compute a new feasible path in case of obstruction to enhance the trajectory’s readability. Moreover, the algorithm iteratively optimizes the current solution in an anytime fashion to deal with strict computing time requirements. Experimental results show the method’s effectiveness in a collaborative cell, compared with industry best practices.

Worker monitoring and protection in collaborative robot (cobots) industrial environments requires advanced sensing capabilities and flexible solutions to monitor the movements of the operator in close proximity of moving robots. Collaborative robotics is an active research area where Internet of Things (IoT) and novel sensing technologies are expected to play a critical role. Considering that no single technology can currently solve the problem of continuous worker monitoring, the article targets the development of an IoT multisensor data fusion (MDF) platform. It is based on an edge-cloud architecture that supports the combination and transformation of multiple sensing technologies to enable the passive and anonymous detection of workers. Multidimensional data acquisition from different IoT sources, signal preprocessing, feature extraction, data distribution, and fusion, along with machine learning (ML) and computing methods are described. The proposed IoT platform also comprises a practical solution for data fusion and analytics. It is able to perform opportunistic and real-time perception of workers by fusing and analyzing radio signals obtained from several interconnected IoT components, namely, a multiantenna WiFi installation (2.4-5 GHz), a sub-THz imaging camera (100 GHz), a network of radars (122 GHz) and infrared sensors (8-13 μm). The performance of the proposed IoT platform is validated through real use case scenarios inside a pilot industrial plant in which protective human-robot distance must be guaranteed considering latency and detection uncertainties.

In this paper we present a simple technique to improve the set-point following performance of the basic Predictive Functional Control algorithm. The technique is based on a first-order-plus-dead-time model of the process and on a suitable selection of the set-point signal, so that a process variable transition from a steady-state value to another one is achieved in a predefined time interval. The additional design effort, which is practically negligible, is discussed. Illustrative examples are given to demonstrate the effectiveness of the proposed solution.

Combining task and motion planning efficiently in human-robot collaboration (HRC) entails several challenges because of the uncertainty conveyed by the human behavior. Tasks plan execution should be continuously monitored and updated based on the actual behavior of the human and the robot to maintain productivity and safety. We propose control-based approach based on two layers, i.e., task planning and action planning. Each layer reasons at a different level of abstraction: task planning considers high-level operations without taking into account their motion properties; action planning optimizes the execution of high-level operations based on current human state and geometric reasoning. The result is a hierarchical framework where the bottom layer gives feedback to top layer about the feasibility of each task, and the top layer uses this feedback to (re)optimize the process plan. The method is applied to an industrial case study in which a robot and a human worker cooperate to assemble a mosaic.

Human robot collaboration requires new planning strategies to guarantee an efficient and safe coexistence of robots and humans in the workspace. We propose a framework based on a model predictive control approach to trajectory scaling and inverse kinematics. The online modification of the velocity override slows down the task to ensure safety and the redundancy of the system is exploited to maximize the distance from the operator. Experimental results on a 7-degree-of-freedom robotic system prove the effectiveness of the method.

The paper presents a fast online predictive method to solve the task-priority differential inverse kinematics of redundant manipulators under kinematic constraints. It implements a task-scaling technique to preserve the desired geometrical task, when the trajectory is infeasible for the robot capabilities. Simulation results demonstrate the effectiveness of the methodology.

In this paper we present a new method to reduce the computational complexity of model predictive control algorithms with online optimization. The formulation of the predictive equations is performed in the continuous-time domain, while the control inputs are parametrized as piecewise constant functions, with less steps than the control horizon. The continuous-time formulation permits the arbitrary choice of the prediction and control time-instants, disregarding the sampling period of the system, and this improves the goodness of the approximation. Moreover, since the inputs are forced to be piecewise constant, the resulting controller can be directly implemented in discretetime. A tuning method for the choice of the prediction and control instants is proposed, minimizing the deviation with respect to the non-approximated controller. Numerical results show the effectiveness of this strategy against other methods with same reduction of computational complexity.

In this paper we analyze the tuning of a PIDPlus event-based control scheme with a noise-filtering event generator. In particular, different well-known PID tuning rules have been considered with different processes. A comparison with a standard PID control scheme has been also considered to evaluate the event generator effectiveness. It is shown that, independently from the employed tuning rule, the application of the devised event generator provides the advantage of reducing the variability of the manipulated variable in the presence of high-frequency measurement noise.

In this paper we propose the use of a dynamic model in which the effects of temperature on friction are considered to develop a virtual force sensor for industrial robot manipulators. The estimation of the inertial parameters and of the friction model are explained. The effectiveness of the virtual force sensor has been proven in a polishing task. In fact, the interaction forces between the robot and the environment has been measured both with the virtual force sensor and a common load cell. Moreover, the advantages provided by considering the temperature dependency are highlighted.

In this paper, we present a new approach to manipulability maximisation for a dual-arm manipulator, which takes into account the manipulability of the overall task. This method tries to overcome the drawbacks given by traditional approaches, which optimise the manipulability of the local configuration of the manipulator, but do not take into account the rest of the task, even though it is known a priori. In this way, it is possible to improve the average manipulability index over the task. The method is applied to a dual-arm system, wherein the task is expressed in terms of relative poses between the end-effectors. For this reason, the kinematic of the system is solved by means of the relative Jacobian.

In this paper we present a strategy developed to compensate for multiple sinusoidal disturbances affecting the pressure output of an industrial revamped plastic injection molding machine. The method is based on the suitable implementation of an adaptive feed-forward control technique and has the characteristic that it can be added to the already existing standard control structures such as the normal feedback structure or the cascade one without modifying any part of them. Experimental results show that the technique allows the significant reduction of multiple sinusoidal disturbances affecting the same part of the plant.

In this paper we present a Hardware-In-the-Loop setup for the simulation of complex mechatronic systems. The setup consists of two coupled brushless motors. One of them is the motor under test, which is used to design the control algorithm and to test the control software, while the other one simulates the device to be controlled. Libraries of mechanical and hydraulic components have been implemented in an IEC61131-3 language so that a complex system can be simulated in a relatively easy way and this allows for a rapid control prototyping. Practical issues are discussed and an illustrative example is shown to confirm the effectiveness of the setup.

The identification of the dynamic model of a robotic manipulator represents a fundamental step for designing high performance model-based controllers. Despite the huge number of works presented on this topic, the symbolic dynamic model reduction (i.e., the identification of the set of parameters observable through the measure of joint torques and positions) still remain a challenging task, characterized from tailored solutions, adapted from time to time to specific families of mechanisms. The work here presented, introduces an automatic and analytical reduction of the dynamic model, based on a multi-dimensional Fourier series decomposition of the dynamic equations. The procedure enables to obtain symbolically the base dynamic parameters (BP) starting from a given kinematic structure. The Fourier based model reduction can be applied indifferently both to open- and closed-chain kinematics. A simulated example shows the effectiveness of the proposed algorithm.

In this paper we present a new friction model for industrial robot manipulators that takes into account temperature effects. In particular, after having shown that friction might change very significantly during robot operations, two solutions based on a polynomial description of the joint friction are proposed and compared. In both cases the models proposed do not need a measurement of the joint temperature, but just of the environmental temperature, so as to be easily applied in industry. Experimental results demonstrate the effectiveness of the applied methodology.

In this work a virtual and remote lab developed to explore the properties of event-based PID controllers in a flexible link system is presented. The architecture is based on the decomposition of the system in three tiers or layers: the server, the client, and a middleware. The server layer is directly connected to the plant, the client side implements the controller and the graphical interface, and the middle-tier acts as interface between client and server. The implementation of the remote lab allows the control of a flexible link plant through a generic network. The platform is used to evaluate, with a particular case, the effectiveness of an algorithm that allows the user to find numerically the limit cycles in control schemes where the feedback is done through a level crossing sampling, and which can be applied to LTI systems with delay.

A two-degree-of-freedom constrained control strategy for a solar furnace is proposed in this paper with the goals of attaining a minimum-time transition between two values of the temperature subject to constraints on both the saturation and slew rate level of the process input. Disturbance compensation and the model mismatches are handled by using a feedback linearization and a PID controller. Simulation results demonstrate the effectiveness of the methodology using data from the solar furnace of the Plataforma Solar de Almería.