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This paper improves the performance of RRT ∗ -like sampling-based path planners by combining admissible informed sampling and local sampling (i.e., sampling the neighborhood of the current solution). An adaptive strategy regulates the trade-off between exploration (admissible informed sampling) and exploitation (local sampling) based on online rewards from previous samples. The paper demonstrates that the algorithm is asymptotically optimal and has a better convergence rate than state-of-the-art path planners (e.g., Informed-RRT ∗ ) in several simulated and real-world scenarios. An open-source, ROS-compatible implementation of the algorithm is publicly available. [ABSTRACT FROM AUTHOR]

The ease of use of robot programming interfaces represents a barrier to robot adoption in several manufacturing sectors because of the need for more expertise from the end-users. Current robot programming methods are mostly the past heritage, with robot programmers reluctant to adopt new programming paradigms. This work aims to evaluate the impact on non-expert users of introducing a new task-oriented programming interface that hides the complexity of a programming framework based on ROS. The paper compares the programming performance of such an interface with a classic robot-oriented programming method based on a state-of-the-art robot teach pendant. An experimental campaign involved 22 non-expert users working on the programming of two industrial tasks. Task-oriented and robot-oriented programming showed comparable learning time, programming time and the number of questions raised during the programming phases, highlighting the possibility of a smooth introduction to task-oriented programming even to non-expert users. [ABSTRACT FROM AUTHOR]

Industry 4.0 is pushing forward the need for symbiotic interactions between physical and virtual entities of production environments to realize increasingly flexible and customizable production processes. This holds especially for human–robot collaboration in manufacturing, which needs continuous interaction between humans and robots. The coexistence of human and autonomous robotic agents raises several methodological and technological challenges for the design of effective, safe, and reliable control paradigms. This work proposes the integration of novel technologies from Artificial Intelligence, Control and Augmented Reality to enhance the flexibility and adaptability of collaborative systems. We present the basis to advance the classical human-aware control paradigm in favor of a user-aware control paradigm and thus personalize and adapt the synthesis and execution of collaborative processes following a user-centric approach. We leverage a manufacturing case study to show a possible deployment of the proposed framework in a real-world industrial scenario. [ABSTRACT FROM AUTHOR]

With the ongoing Industry 4.0 (I4.0) revolution, plant management and supervision play a key role in the development and (re-)design of industrial plants. In the arising scenarios, the need to coordinate human workers and autonomous systems, sharing the same environment, teaming together, becomes a fundamental requirement. Indeed, even though automation in standard assembly lines has reached high efficiency and reliability, for complex and new applications, a certain amount of failure must be considered for future addressing. This paper presents a framework for the flexible coordination of such a complex and heterogeneous cyber-physical system. A digital Ttwin mirrors in real-time the plant system, while a dashboard displays plant status, providing the human operators with fundamental tools for supervision and prompt intervention in case of failure. The framework was developed and tested in an industrially relevant environment, specifically for the assembly of the interior of an aircraft fuselage. [ABSTRACT FROM AUTHOR]

This document is a correction notice for an article titled "Adaptive hybrid local-global sampling for fast informed sampling-based optimal path planning" published in the journal Autonomous Robots. The correction states that there was an error in the title of the original article, which incorrectly published as "Accelerating sampling-based optimal path planning via adaptive informed sampling." The correct title should be "Adaptive hybrid local-global sampling for fast informed sampling-based optimal path planning." The correction notice also includes a note from the publisher, Springer Nature, stating their neutrality regarding jurisdictional claims and institutional affiliations. [Extracted from the article]

This paper presents a novel self-localization technique for mobile robots based on image feature matching from omnidirectional vision. The proposed method first constructs a virtual space with synthetic omnidirectional imaging to simulate a mobile robot equipped with an omnidirectional vision system in the real world. In the virtual space, a number of vertical and horizontal lines are generated according to the structure of the environment. They are imaged by the virtual omnidirectional camera using the catadioptric projection model. The omnidirectional images derived from the virtual and real environments are then used to match the synthetic lines and real scene edges. Finally, the pose and trajectory of the mobile robot in the real world are estimated by the efficient perspective-n-point (EPnP) algorithm based on the line feature matching. In our experiments, the effectiveness of the proposed self-localization technique was validated by the navigation of a mobile robot in a real world environment. [ABSTRACT FROM AUTHOR]

Industry 4.0 is taking human-robot collaboration at the center of the production environment. Collaborative robots enhance productivity and flexibility while reducing human's fatigue and the risk of injuries, exploiting advanced control methodologies. However, there is a lack of real-time model-based controllers accounting for the complex human-robot interaction dynamics. With this aim, this paper proposes a Model-Based Reinforcement Learning (MBRL) variable impedance controller to assist human operators in collaborative tasks. More in details, an ensemble of Artificial Neural Networks (ANNs) is used to learn a human-robot interaction dynamic model, capturing uncertainties. Such a learned model is kept updated during collaborative tasks execution. In addition, the learned model is used by a Model Predictive Controller (MPC) with Cross-Entropy Method (CEM). The aim of the MPC+CEM is to online optimize the stiffness and damping impedance control parameters minimizing the human effort (i.e, minimizing the human-robot interaction forces). The proposed approach has been validated through an experimental procedure. A lifting task has been considered as the reference validation application (weight of the manipulated part: 10 kg unknown to the robot controller). A KUKA LBR iiwa 14 R820 has been used as a test platform. Qualitative performance (i.e, questionnaire on perceived collaboration) have been evaluated. Achieved results have been compared with previous developed offline model-free optimized controllers and with the robot manual guidance controller. The proposed MBRL variable impedance controller shows improved human-robot collaboration. The proposed controller is capable to actively assist the human in the target task, compensating for the unknown part weight. The human-robot interaction dynamic model has been trained with a few initial experiments (30 initial experiments). In addition, the possibility to keep the learning of the human-robot interaction dynamics active allows accounting for the adaptation of human motor system. [ABSTRACT FROM AUTHOR]

Handling and assembling applications with small batch size and high production mix require requires high adaptability, reconfigurability and flexibility. Thus, human-robot collaboration could be an effective solution to ensure production performance and operator satisfaction. This scenario requires human-awareness in different levels of the software framework, from the robot control to the task planning. The goal is to assign high added value activities to the human as much as possible, while the robot has to be able to substitute the human when needed. Team PIROS faces this goal by designing a IEC 61499/ROS-based architecture which integrate safety assessment, advanced force control, human-aware motion planning, gesture recognition, and task scheduling. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper deals with the use of an industrial robot (IR) for the deburring of hard material items (i.e. cast iron items). The control strategies introduced in this paper aim to mimic the human behaviour during the manual deburring. On the basis of force feedback, provided from a 1-axis load cell, the nominal deburring trajectory is optimised and deformed making multiple repetitions. The deburring trajectory is repeated until completing the nominal deburring path. The removal of thin layers of materials allows the robot to operate at high feed rates avoiding spindle stall and without exciting elastics effects on the mechanical structure of the system. Furthermore, a method to automatically detect the force changepoints, related to the presence of a burr, without tuning force thresholds, is discussed. The human mimicking control strategy is compared with a standard industrial approach demonstrating a reduction of the task cycle time and an improvement of the finishing quality. [ABSTRACT FROM AUTHOR]

SUMMARY: Cladding through laser metal deposition is a promising application of additive manufacturing. On the one hand, industrial robots are increasingly used in cladding because they provide wide wrist reorientation, which enables manufacturing of complex geometries. On the other hand, limitations in robot dynamics may prevent cladding of sharp edges and large objects. To overcome these issues, this paper aims at exploiting the residual degrees of freedom granted by the cladding process for the optimization of the deposition orientation. The proposed method optimizes the robot head orientation along a predefined path while coping with kino-dynamic constraints as well as process constraints. Experimental tests and results are reported and used to validate the approach. [ABSTRACT FROM AUTHOR]

Task time is essential information for the optimal planning and scheduling of industrial scenarios, such as assembly cells. In Human-Robot Collaboration (HRC), the robot execution time, i.e. the robot task time, depends on the task the human is executing simultaneously to the robot and on the human movements. Indeed, the robot may be requested to modify its speed along a predefined path (i.e. to slow down or to stop its motion) in order to avoid possible collisions with the human. This paper presents an approach for the estimation of the robot execution time, when the robot path and the human task are assigned. Specifically, a workspace segmentation is performed considering the volume occupied by the human and the robot during their motion. Then, this segmentation is exploited for the definition of a set of Markov chains modeling human-robot interaction and allowing the estimation of the robot execution time. Simulated and real test beds are presented and discussed. [ABSTRACT FROM AUTHOR]

The presented paper proposes an analytical force overshoots free control architecture for standard industrial manipulators involved in high-accuracy industrial assembly tasks ( i.e. , with tight mounting tolerances). As in many industrial scenarios, the robot manipulates components through (compliant) external grippers and interacts with partially unknown compliant environments. In such a context, a force overshoot may result in task failures ( e.g. , gripper losses the component, component damages), representing a critical control issue. To face such problem, the proposed control architecture makes use of the force measurements as a feedback (obtained using a force/torque sensor at the robot end-effector) and of the estimation of the equivalent interacting elastic system stiffness ( i.e. , force sensor– compliant gripper–compliant environment equivalent stiffness) defining two control levels: (i) an internal impedance controller with inner position and orientation loop and (ii) an external impedance shaping force tracking controller. A theoretical analysis of the method has been performed. Then, the method has been experimentally validated in an industrial-like assembly task with tight mounting tolerances ( i.e. , H7/h6 mounting). A standard industrial robot (a Universal Robot UR 10 manipulator) has been used as a test-platform, equipped with an external force/torque sensor Robotiq FT 300 at the robot end-effector and with a Robotiq Adaptive Gripper C-Model to manipulate target components. ROS framework has been adopted to implement the proposed control architecture. Experimental results show the avoidance of force overshoots and the achieved target dynamic performance. [ABSTRACT FROM AUTHOR]

The editorial titled "Digital Twin for Industry 4.0" discusses the application of virtual reality (VR) and digital twin technology in various industries. It highlights how VR is transforming product design and development by improving communication, optimizing handling and operation times, and reducing the need for physical prototypes. The editorial also mentions specific papers that explore the use of VR in the construction sector, including the creation of a VR learning environment for workers and the development of efficient and sustainable industrial constructions. Additionally, it discusses the use of augmented reality (AR) in home environments and the practical methods for reconstructing 3D models using a simple tablet. Overall, the editorial emphasizes the potential of VR and digital twin technology in enhancing industry processes and learning experiences. [Extracted from the article]

Light-weight manipulators are used in industrial tasks mounted on mobile platforms to improve flexibility. However, such mountings introduce compliance affecting the tasks. This work deals with such scenarios by designing a controller that also takes into account compliant environments. The controller allows the tracking of a target force using the estimation of the environment stiffness (EKF) and the estimation of the base position (KF), compensating the robot base deformation. The closed-loop stability has been analyzed. Observers and the control law have been validated in experiments. An assembly task is considered with a standard industrial non-actuated mobile platform. Control laws with and without base compensation are compared. [ABSTRACT FROM AUTHOR]

Nowadays, light-weight manipulators are widely adopted in many applications requiring manipulation/interaction with compliant/fragile objects. Reduced inertia and controlled compliance, indeed, make such manipulators particularly attractive when compliant mountings (or mobile platforms) are adopted and contact force overshoot may compromise the application. The here presented work proposes the design of a force-tracking controller for interaction tasks allowing to systematically avoid any force overshoot for lightweight robots mounted on compliant bases. The developed algorithm allows to compensate for the compliant robot base dynamics that affects the interaction. The control gains are calculated to track a target force reference through the estimation of the robot base state and the interacting environment stiffness. Closed-loop stability and control gains calculation are described. The control law has been validated in a probing task involving a compliant robot base and a compliant environment to show the obtained performance. [ABSTRACT FROM AUTHOR]

The aim of the presented article is to overcome the force overshoot issue in impedance based force tracking applications. Nowadays, light-weight manipulators are involved in high-accurate force control applications (such as polishing tasks), where the force overshoot issue is critical (i.e. damaging the component causing a production waste), exploiting the impedance control. Two main force tracking impedance control approaches are described in literature: (a) set-point deformation and (b) variable stiffness approaches. However, no contributions are directly related to the force overshoot issue. The presented article extends both such methodologies to analytically achieve the force overshoots avoidance in interaction tasks based on the on-line estimation of the interacting environment stiffness (available through an EKF). Both the proposed control algorithms allow to achieve a linear closed-loop dynamics for the coupled robot-environment system. Therefore, control gains can be analytically on-line calculated to achieve an over-damped closed-loop dynamics of the controlled coupled system. Control strategies have been validated in experiments, involving a KUKA LWR 4þ. A probing task has been performed, representative of many industrial tasks (e.g. assembly tasks), in which a main force task direction is defined. [ABSTRACT FROM AUTHOR]

Upper-limb movement analysis is important to monitor objectively rehabilitation interventions, contributing to improving the overall treatments outcomes. Simple, fast, easy-to-use, and applicable methods are required to allow routinely functional evaluation of patients with different pathologies and clinical conditions. This paper describes the Reaching and Hand-to-Mouth Evaluation Method, a fast procedure to assess the upper-limb motor control and functional ability, providing a set of normative data from 42 healthy subjects of different ages, evaluated for both the dominant and the nondominant limb motor performance. Sixteen of them were reevaluated after two weeks to perform test-retest reliability analysis. Data were clustered into three subgroups of different ages to test the method sensitivity to motor control differences. Experimental data show notable test-retest reliability in all tasks. Data from older and younger subjects show significant differences in the measures related to the ability for coordination thus showing the high sensitivity of the method to motor control differences. The presented method, provided with control data from healthy subjects, appears to be a suitable and reliable tool for the upper-limb functional assessment in the clinical environment. [ABSTRACT FROM AUTHOR]

The fashion footwear industry is strongly characterized by hand-crafted fabrication. Companies operating in this field are usually reluctant to employ industrial robots in their facilities, where the integration of automated solutions is often an element of disruption. It is also still difficult to perform many of the operations involved with industrial robots. The roughing of the uppers of fashion shoes is a typical example: the great inaccuracy in shoe dimensions and shape makes definition of an automated path a critical aspect, and this is reflected in the complexity of robot programming. In addition, standard industrial position-based control solutions are unsuitable in force-tracking application as roughing by the variable stiffness of the environment and by arm flexibilities. In this scenario, the paper describes an innovative robotic cell for roughing operations with a layout conceived for installation in artisan-based production. The robot controller is an admittance control loop with a fuzzy regulator designed to be integrated with standard robot controller by exploiting the so-called sensor-tracking option. The design of the robot controller is grounded on an in-depth analysis of the manual strategies used by workers skilled in roughing operations. The results of a series of experiments on different kinds of shoes demonstrate the feasibility of this approach. [ABSTRACT FROM AUTHOR]

Abstract: Robot-assisted neurorehabilitation often involves networked systems of sensors (“sensory rooms”) and powerful devices in physical interaction with weak users. Safety is unquestionably a primary concern. Some lightweight robot platforms and devices designed on purpose include safety properties using redundant sensors or intrinsic safety design (e.g. compliance and backdrivability, limited exchange of energy). Nonetheless, the entire “sensory room” shall be required to be fail-safe and safely monitored as a system at large. Yet, sensor capabilities and control algorithms used in functional therapies require, in general, frequent updates or re-configurations, making a safety-grade release of such devices hardly sustainable in cost-effectiveness and development time. As such, promising integrated platforms for human-in-the-loop therapies could not find clinical application and manufacturing support because of lacking in the maintenance of global fail-safe properties. Under the general context of cross-machinery safety standards, the paper presents a methodology called SafeNet for helping in extending the safety rate of Human Robot Interaction (HRI) systems using unsafe components, including sensors and controllers. SafeNet considers, in fact, the robotic system as a device at large and applies the principles of functional safety (as in ISO 13489-1) through a set of architectural procedures and implementation rules. The enabled capability of monitoring a network of unsafe devices through redundant computational nodes, allows the usage of any custom sensors and algorithms, usually planned and assembled at therapy planning-time rather than at platform design-time. A case study is presented with an actual implementation of the proposed methodology. A specific architectural solution is applied to an example of robot-assisted upper-limb rehabilitation with online motion tracking. [Copyright &y& Elsevier]

The study of human–machine interaction as a unique control system was one of the first research interests in the engineering field, with almost a century having passed since the first works appeared in this area. At the same time, it is a crucial aspect of the most recent technological developments made in application fields such as collaborative robotics and artificial intelligence. Learning the processes and dynamics underlying human control strategies when interacting with controlled elements or objects of a different nature has been the subject of research in neuroscience, aerospace, robotics, and artificial intelligence. The cross-domain nature of this field of study can cause difficulties in finding a guiding line that links motor control theory, modelling approaches in physiological control systems, and identifying human–machine general control models in manipulative tasks. The discussed models have varying levels of complexity, from the first quasi-linear model in the frequency domain to the successive optimal control model. These models include detailed descriptions of physiologic subsystems and biomechanics. The motivation behind this work is to provide a complete view of the linear models that could be easily handled both in the time domain and in the frequency domain by using a well-established methodology in the classical linear systems and control theory. [ABSTRACT FROM AUTHOR]

The standard EN ISO10218 is fostering the implementation of hybrid production systems, i.e., production systems characterized by a close relationship among human operators and robots in cooperative tasks. Human-robot hybrid systems could have a big economic benefit in small and medium sized production, even if this new paradigm introduces mandatory, challenging safety aspects. Among various requirements for collaborative workspaces, safety-assurance involves two different application layers; the algorithms enabling safe space-sharing between humans and robots and the enabling technologies allowing acquisition data from sensor fusion and environmental data analysing. This paper addresses both the problems: a collision avoidance strategy allowing on-line re-planning of robot motion and a safe network of unsafe devices as a suggested infrastructure for functional safety achievement. [ABSTRACT FROM AUTHOR]

The paper deals with the generation of optimal trajectories for industrial robots in machining and additive manufacturing applications. The proposed method uses an Ant Colony algorithm to solve a kinodynamic motion planning problem. It exploits the kinematic redundancy that is often present in these applications to optimize the execution of trajectory. At the same time, the robot kinematics and dynamics constraints are respected and robot collisions are avoided. To reduce the computational burden, the task workspace is discretized enabling the use of efficient network solver based on Ant Colony theory. The proposed method is validated in robotic milling and additive manufacturing real-world scenarios. [ABSTRACT FROM AUTHOR]

Recent advances in planning and control of robot manipulators make an increasing use of optimization-based techniques, such as model predictive control. In this framework, ensuring the feasibility of the online optimal control problem is a key issue. In the case of manipulators with bounded joint positions, velocities, and accelerations, feasibility can be guaranteed by limiting the set of admissible velocities and positions to a viable set. However, this results in the imposition of nonlinear optimization constraints. In this paper, we analyze the feasibility of the optimal control problem and we propose a method to construct a viable convex polyhedral that ensures feasibility of the optimal control problem by means of a given number of linear constraints. Experimental and numerical results on an industrial manipulator show the validity of the proposed approach. [ABSTRACT FROM AUTHOR]

• Look-ahead technique to modify the trajectory online to satisfy the robot limits. • Geometrical path is preserved under velocity, acceleration, and torque constraints. • Significant path-following error improvement compared with non-look ahead methods. • Computational time compatible with the robot controller. • Simulation and experimental results on a 6-degree-of-freedom manipulator. Robotized assembly and manufacturing often require to modify the robot motion at runtime. When the primary constraint is to preserve the geometrical path as much as possible, it is convenient to scale the nominal trajectory in time to meet the robot constraints. Look-ahead techniques are computationally heavy, while non-look-ahead ones usually show poor performance in critical circumstances. This paper proposes a novel technique that can be embedded in non-look-ahead scaling algorithms to improve their performance. The proposed method takes into account the robot velocity, acceleration, and torque limits and modifies the velocity profile based on an approximated look-ahead criterion. To do this, it considers only the last point of a look-ahead window and, by linearizing the problem, it computes the maximum admissible robot velocity. The technique can be applied to existing trajectory scaling algorithms to confer look-ahead properties on them. Simulation and experimental results on a 6-degree-of-freedom manipulator show that the proposed method significantly reduces path-following errors. [ABSTRACT FROM AUTHOR]