Task and Motion Planning (TAMP) is essential for efficient Human-Robot Collaboration (HRC) in industrial settings, yet existing approaches struggle to handle human interventions and dynamic environments. This paper presents a Human Adaptive Task and Motion Planning (HAD-TAMP) framework that seamlessly integrates human pose and actions into the planning process to quickly adapt to human requests or deviations from the process plan. The framework consists of three key modules: a task planning module, which generates and updates task sequences based on real-time human input, a motion planning module composed of a set of motion planners specialized for different phases of the collaboration (e.g., collaborative transportation of materials), and a context reasoner module which coordinates the overall process based on the sensory information available. A key contribution is using a receding horizon strategy, enabling real-time adaptation to human inputs and environmental changes. The approach is validated in a real industrial HRC scenario through two applications: gesture-based human-robot interaction and close human-robot collaboration in carbon fiber draping. Experimental results demonstrate the framework's effectiveness in ensuring adaptability to multiple human requests and efficiency: the re-planning time is 4 times and 5 times faster than the generation of a new plan.

In industrial environments like factories and warehouses, transportation of flexible materials that need the collaboration of several subjects is a typical activity. One instance is the handling of enormous fibre sheets in the fabrication of composite parts, which presents several difficulties, including handling flexible materials and needing to place the material with extreme precision. Recently, there has been a lot of interest in employing robots to help human workers carry such things. However, this typically entails the robot adopting a follower attitude just intended for passive support without fully using its accuracy and repeatability. To make the best possible use of the robot's and the operator's skills, it is necessary to use an intelligent motion planner that takes into account the ergonomics of the operator but at the same time ensures the precision required by the task. In this paper, we present a preliminary study for a Human-Aware Motion Planner for the cooperative transportation of materials.

The aim of this paper is to exploit the peculiarities of a previously presented mechanical model for the humanoid gait, especially considering the role that certain parameters could have in the design of a multi-link leg. Usually the approach is very straightforward; most of the effort is centered on the algorithm which drives the joints, given a geometrical configuration. Taking the advantage of the Lagrangian approach which combines the two potential energies, gravitational and spring-based, the analysis of the model features highlights a possible correspondence among those parameters and the structure of the link leg.

Current research in robot Task Planning aims to develop solvers which quickly adapt to new assign-ments and scenarios. To this aim, we extend an existing proto-Artificial General Intelligence system, namely OpenCog, and give it the ability to effectively solve manipulation tasks whose domains contain four actions: pick , place , stack , and unstack . To let OpenCog solve this class of problems, we exploit its modules as the foundation of a Knowledge Base that describes and stores domains, problems, and the interactions between them. Then, we equip the system with a Breadth-First Search algorithm that finds the sequence of actions that solve the assignments. To prove the goodness of our proposal, we include and analyze a manipulation task where a manipulator robot has to interact with a human user to assemble some industrial components. Obtained results show that our system is complete and generic in terms of the domain and problem under evaluation. Future work will improve the achieved computational time and performance.

This paper proposes RUR53: an Unmanned Ground Vehicle able to navigate through, identify, and reach areas of interest. There, it can recognize, localize, and manipulate work tools to perform both indoor and outdoor complex tasks. Indeed, a wide range of sensors composes the robot and enables it to perceive vast workspaces, reach distant targets, and face the uncertainties of the real world. Precise object detection is also guaranteed, essential to manipulate objects of different shapes and materials. Moreover, a customized 3-finger gripper makes the gripping mode suitable for any lightweight object. Two modalities are proposed: autonomous and teleoperated, letting both unskilled and skilled human operators easily adapt the system to complete personalized tasks. The paper exhaustively describes RUR53 architecture and demonstrates its good performance while executing both indoor and outdoor navigation and manipulation tasks. A specific case study is described where the proposed modular architecture allows to easily switch to a semi-teleoperated mode: the 2017 Mohamed Bin Zayed International Robotics Challenge, where our team ranked third in the Grand Challenge in collaboration with the Czech Technical University in Prague, the University of Pennsylvania, and the University of Lincoln (UK).

Complex manipulation tasks require careful integration of symbolic reasoning and motion planning. This problem, commonly referred to as Task and Motion Planning (TAMP), is even more challenging if the workspace is non-static, e.g. due to human interventions and perceived with noisy non-ideal sensors. This work proposes an online approximated TAMP method that combines a geometric reasoning module and a motion planner with a standard task planner in a receding horizon fashion. Our approach iteratively solves a reduced planning problem over a receding window of a limited number of future actions during the implementation of the actions. Thus, only the first action of the horizon is actually scheduled at each iteration, then the window is moved forward, and the problem is solved again. This procedure allows to naturally take into account potential changes in the scene while ensuring good runtime performance. We validate our approach within extensive experiments in a simulated environment. We showed that our approach is able to deal with unexpected changes in the environment while ensuring comparable performance with respect to other recent TAMP approaches in solving traditional static benchmarks. We release with this paper the open-source implementation of our method.

The robotic industry needs new, innovative, ideas to be globally competitive. Conventional industrial robots are not able to adapt to changes in the assembly processes. Flexible assembly applications are actually uncommon and only isolated attempts exploit industrial robots to perform tasks with variability in the parts. Variability aspects are emphasized when developing novel manufacturing applications involving human–robot collaboration which are the foundation of Industry 4.0 systems. In this chapter, we describe how variability can be considered and mathematically described as part of the problem to obtain a flexible robotic solution. The selected approach is based on a probabilistic representation of the task obtained starting from a set of demonstrations collected from humans. The chapter illustrates the different steps leading to the complete learning framework. We start by describing the strategies adopted during the data collection phase. From the raw data, the design of feature extraction procedures is provided alongside a set of preprocessing techniques used to remove noisy and incoherent information. The resulting data set is used to train a model of task by following a probabilistic approach. The output of the model is exploited to actuate an industrial manipulator in the context of significant production scenarios. The robot motion strategies are also analyzed depending on the level of flexibility requested from the specific use case. Two main use cases are introduced: (1) the automatic assembly of a car door with its module, and (2) the robotized manufacturing process of electric machines, in particular winding of coils on stator or rotor cores. Each problem is mathematically formulated by modeling both the robotic platform and the target. The influence of the scenario variability with respect to the computed robotic motion is considered. The system flexibility is evaluated by means of an extensive set of benchmarking tests by recording data and actuating robots in both simulated and real environments. Achievements are compared with respect to state-of-the-art solutions by defining a set of objectives and metrics. The goal is to measure the performance of the system, for example, in minimizing the time and energy needed to move the robot in the working space, in generating an effective human–robot interaction with low reaction time and high accuracy, and in providing an intuitive robot learning technique to easily allow the human to teach the robot new tasks. Dynamic online reconfigurability of the framework is considered by testing its capability to deal with novel situations and new products. The integration of the proposed technologies with current robotic systems is discussed and a solution based on the robot operating system is proposed to provide a good infrastructure for network communication as well as all the tools necessary for a modern distributed and heterogeneous system. The feasibility and cost-effectiveness of the developed solutions are taken into account in order to demonstrate the applicability of the proposed approach in actual industrial settings.

To complete a task consisting of a series of actions that involve human-robot interaction, it is necessary to plan a motion that considers each action individually as well as in relation to the following action. We then focus on the specific action of “approaching a group of people” in order to accurately obtain human data that is used to make the performance of tasks involving interactions with multiple people more smooth. The movement depends on the characteristics of the important sensors used for the task and on the placement of people at and around the destination. Considering the multiple tasks and placement of people, the pre-calculation of the destinations and paths is difficult. This paper thus presents a system of navigation that can accurately obtain human data based on sensor characteristics, task content, and real-time sensor data for processes involving human-robot interaction (HRI); this method does not navigate specifically toward a previously determined static point. Our goal was achieved by using a multimodal path planning based on integration of action modeling by considering both voice and image sensing of interacting people as well as obstacle avoidance. We experimentally verified our method by using a robot in a coffee shop environment.

Human-robot collaboration is one of the key elements in the Industry 4.0 revolution, aiming to a close and direct collaboration between robots and human workers to reach higher productivity and improved ergonomics. The first step toward such kind of collaboration in the industrial context is the removal of physical safety barriers usually surrounding standard robotic cells, so that human workers can approach and directly collaborate with robots. Anyway, human safety must be granted avoiding possible collisions with the robot. In this work, we propose the use of a people tracking algorithm to monitor people moving around a robot manipulator and recognize when a person is too close to the robot while performing a task. The system is implemented by a camera network system positioned around the robot workspace, and thoroughly evaluated in different industry-like settings in terms of both tracking accuracy and detection delay.

BACKGROUND:A proper modeling of human grasping and of hand movements is fundamental for robotics, prosthetics, physiology and rehabilitation. The taxonomies of hand grasps that have been proposed in scientific literature so far are based on qualitative analyses of the movements and thus they are usually not quantitatively justified.METHODS:This paper presents to the best of our knowledge the first quantitative taxonomy of hand grasps based on biomedical data measurements. The taxonomy is based on electromyography and kinematic data recorded from 40 healthy subjects performing 20 unique hand grasps. For each subject, a set of hierarchical trees are computed for several signal features. Afterwards, the trees are combined, first into modality-specific (i.e. muscular and kinematic) taxonomies of hand grasps and then into a general quantitative taxonomy of hand movements. The modality-specific taxonomies provide similar results despite describing different parameters of hand movements, one being muscular and the other kinematic.RESULTS:The general taxonomy merges the kinematic and muscular description into a comprehensive hierarchical structure. The obtained results clarify what has been proposed in the literature so far and they partially confirm the qualitative parameters used to create previous taxonomies of hand grasps. According to the results, hand movements can be divided into five movement categories defined based on the overall grasp shape, finger positioning and muscular activation. Part of the results appears qualitatively in accordance with previous results describing kinematic hand grasping synergies.CONCLUSIONS:The taxonomy of hand grasps proposed in this paper clarifies with quantitative measurements what has been proposed in the field on a qualitative basis, thus having a potential impact on several scientific fields.

This paper proposes a preliminary work on a Conditional Task and Motion Planning algorithm able to find a plan that minimizes robot efforts while solving assigned tasks. Unlike most of the existing approaches that replan a path only when it becomes unfeasible (e.g., no collision-free paths exist), the proposed algorithm takes into consideration a replanning procedure whenever an effort-saving is possible. The effort is here considered as the execution time, but it is extensible to the robot energy consumption. The computed plan is both conditional and dynamically adaptable to the unexpected environmental changes. Based on the theoretical analysis of the algorithm, authors expect their proposal to be complete and scalable. In progress experiments aim to prove this investigation.

Thanks to the increasing interest on robotics prosthetic devices controlled by means of physiological signals, a continuously increasing number of solutions are proposed. Usually the proposed solutions are very expensive and created ad-hoc for the final user. For this reason, a large part of the possible users can not afford this kind of technology. Furthermore, the software adaptation to the user is time consuming and physically stressing for the subject. The paper presents a low cost prosthesis framework, which covers the three fundamental aspects of a rehabilitation system, i.e. the prosthesis, the sensors used to record the physiological signals, and the software connecting the two previous points. To reduce the costs we chose a 3D printed prosthetic hand from an open-source project. We recorded Electromyography (EMG) signals from the subjects' muscles by using a low cost armband, a all-in-one solution easy to wear and remove. The EMG signals are preprocessed in order to be used online, and they are used to train a probabilistic model for classification purposes. Furthermore, the model is built on data from different subjects, in order to develop a subject-independent framework, which can be used by any subject, with no need of draining training phases. We test the goodness of our solution with a leave-one-out approach by classifying three different hand grasps. Finally, the 3D printed hand reproduces the movement performed by the subject. Data were recorded from four different subjects, each of them repeating the selected movements five times, and we obtained an overall accuracy of 76.8%.

This paper presents a graduate course project based on a challenging industrial task as a way to learn basic concepts in robotics. The students had to face a simplified version of a task proposed as part of an European Competition. The general aim is to identify an object and place a manipulator in a certain target position with respect to it. Students have to use information provided by a stereo camera in order to guarantee a good pose estimation. The object is not placed in a fixed and predefined spot, but the pose can vary in a range within a maximum of 5 cm in position and 3 degrees in rotation on each direction. The learning objectives of both course and project are introduced and compared with the students' background. We discuss the solutions proposed by the students, together with the amount of time they and their instructors dedicated to solve the task. Answers to a survey have been collected and discussed in order to better evaluate the students' experience.

The paper describes a human-robot cooperative installation methodology of heavy and bulky components based on marker-based visual servoing, force control, and human-robot cooperation. The main advance in the human-robot cooperation is achieved by a shared-control of the interaction during the installation task, relieving the human operator by the manipulated load and giving to the robot a partially autonomous behaviour in the force-tracking direction. Experimental results are shown in the context of the H2020 CleanSky 2 EURECA project in which a side-wall panel is installed in a 1:1 scale mock-up scenario of an A320 plane fuselage environment.

This paper aims at providing a preliminary description of ROS-Health, a novel framework for neurorobotics based on the middleware Robot Operating System (ROS). The increased interest in the neurorobotics field and the proliferation of several (neuro)physiological-based applications to control robotics devices made clear the importance to establish a standardized research platform in order to facilitate the distribution of the software, the replication of experimental results and the creation of an unified community to share and manage the code in the years. For this reason, we propose a common platform developed in the ROS ecosystem that takes advantage of its tools and capabilities. Furthermore, we describe the design guidelines that we are following in the preliminary definition of ROS-Health architecture. Finally, we present two illustrative use cases that highlight the advantages and benefits of the adoption of ROS-Health.

European electrical machines manufacturers need to increase the flexibility of production process, due to the high cost of equipment setup at the beginning of each new production batch. Overall, most of these European manufacturers are striving to reduce costs while preserving the quality of products, in order to face the competition by Far East companies. There is a strong need for increasing productivity, flexibility and quality. In particular, in wound coils manufacturing process, current technologies allow only to big international manufacturer to automate their production lines, due to high machinery cost and set-up time, while small and medium manufacturers are forced to direct themselves towards manual production. This work aims to reduce costs and increase flexibility with the following contributions: (1) important reduction of setup time and costs of the winding machine, thanks to the simplicity and flexibility of the proposed approach; (2) increase in the quality of the final motors, thanks to the increased amount of copper that the robot will be able to insert in each coil with respect to manual winding; (3) possibility to parallelize the winding operations, dramatically increasing production rate; (4) decreased number of defected cores, thanks to an advanced quality inspection system; (5) reduction of environmental impact of the production process, thanks to a reduction of wasted copper wire.

Ahstract- The interest on wearable prosthetic devices has boost the research for a robust framework to help injured subjects to regain their lost functionality. A great number of solutions exploit physiological human signals, such as Electromyography (EMG), to naturally control the prosthesis, reproducing what happens in the human limbs. In this paper, we propose for the first time a way to integrate EMG signals with Inertial Measurement Unit (IMU) information, as a way to improve subject-independent models for controlling robotic hands. EMG data are very sensitive to both physical and physiological variations, and this is particularly true between different subjects. The introduction of IMUs aims at enriching the subject-independent model, making it more robust with information not strictly dependent from the physiological characteristics of the subject. We compare three different models: the first based on EMG solely, the second merging data from EMG and the 2 best IMUs available, and the third using EMG and IMUs information corresponding to the same 3 electrodes. The three techniques are tested on two different movements executed by 35 healthy subjects, by using a leave-one-out approach. The framework is able to estimate online the bending angles of the joints involved in the motion, obtaining an accuracy up to 0.8634. The resulting joint angles are used to actuate a robotic hand in a simulated environment.