Robotics

For visually impaired people, orientation and mobility are challenging and require a great deal of concentration. Especially unfamiliar routes are difficult to follow. This paper presents a navigation algorithm based on a geographic information system that can be optimally adapted to the needs of this user group. A three-stage process is being developed for this purpose. The first step is to check the map against ISO 19157, followed by map augmentation and the generation of a navigable graph. Finally, a new cost function for an A* algorithm is developed to make the best use of the modified map data and adapt it to the user’s needs. It is shown that map data from the geographic information system OpenStreetMap is well suited to the task, although the map data needs to be verified and augmented with additional information before being used. Finally, we compare the presented solution with a standard A* algorithm. Full article

This paper addresses the problem of relative position-based formation planning for a leader–follower multi-robot setup, where the robots adjust the formation parameters, such as size and three-dimensional orientation, to avoid collisions and progress toward their goal. Specifically, we develop a virtual sub-target-based obstacle avoidance method, which involves a transitional virtual sub-target that guides the robots to avoid obstacles according to obstacle information, target, and boundary. Moreover, we develop a changing formation strategy to determine the necessity to avoid collisions and a priority-based model to determine which robots move, thus dynamically adjusting the relative distance between the followers and the leader. The backstepping-based sliding motion controller guarantees that the trajectory and velocity tracking errors converge to zero. The proposed robot navigation method can be employed in various environments and types of obstacles, allowing for a formation change. Furthermore, it is efficient and scalable under various numbers of robots. The approach is experimentally verified using three real mobile robots and up to five mobile robots in simulated scenarios. Full article

Three decades ago, telepresence was presented as an idea in the context of remote work and manipulation. Since then, it has evolved into a field combining different technologies and allowing users to have more or less realistic perceptions of immersion in remote environments. This paper reviews telepresence and its recent advances. While not covering all the work conducted in telepresence, this paper provides an array of applications for which telepresence can be envisioned, providing a clear view of the differences between components and functionalities of robotic platforms conceived for telepresence and pointing to the dependence of telepresence on several technological areas. Furthermore, challenges faced by telepresence technologies are shown, with consideration of user experiences. We consider telepresence from different perspectives, focusing on specific parts, making it possible to foresee future directions of research and applications. This review will be useful for researchers working in telepresence and related fields. Full article

As we look towards the future of healthcare, integrating Care Robots (CRs) into health systems is a practical approach to address challenges such as an ageing population and caregiver shortages. However, ethical discussions about the impact of CRs on patients, caregivers, healthcare systems, and society are crucial. This normative research seeks to define an integrative and comprehensive ethical framework for CRs, encompassing a wide range of AI-related issues in healthcare. To build the framework, we combine principles of beneficence, non-maleficence, autonomy, justice, and explainability by integrating the AI4People framework for a Good AI Society and the traditional bioethics perspective. Using the integrated framework, we conduct an ethical assessment of CRs. Next, we identify three key ethical trade-offs and propose remediation strategies for the technology. Finally, we offer design recommendations for responsible development and usage of CRs. In conclusion, our research highlights the critical need for sector-specific ethical discussions in healthcare to fully grasp the potential implications of integrating AI technology. Full article

This paper develops a three-degree-of-freedom sagittal-plane hybrid dynamical systems model of a Groucho-style bounding quadrupedal run. Simple within-stance controls using a modular architecture yield a closed-form expression for a family of hybrid limit cycles that represent bounding behavior over a range of user-selected fore-aft speeds as a function of the model’s kinematic and dynamical parameters. Controls acting on the hybrid transitions are structured so as to achieve a cascade composition of in-place bounding driving the fore-aft degree of freedom, thereby decoupling the linearized dynamics of an approximation to the stride map. Careful selection of the feedback channels used to implement these controls affords infinitesimal deadbeat stability, which is relatively robust against parameter mismatch. Experiments with a physical quadruped reasonably closely match the bounding behavior predicted by the hybrid limit cycle and its stable linearized approximation. Full article

The efficient computation of viewpoints while considering various system and process constraints is a common challenge that any robot vision system is confronted with when trying to execute a vision task. Although fundamental research has provided solid and sound solutions for tackling this problem, a holistic framework that poses its formal description, considers the heterogeneity of robot vision systems, and offers an integrated solution remains unaddressed. Hence, this publication outlines the generation of viewpoints as a geometrical problem and introduces a generalized theoretical framework based on Feature-Based Constrained Spaces ($\mathcal{C}$-spaces) as the backbone for solving it. A $\mathcal{C}$-space can be understood as the topological space that a viewpoint constraint spans, where the sensor can be positioned for acquiring a feature while fulfilling the constraint. The present study demonstrates that many viewpoint constraints can be efficiently formulated as $\mathcal{C}$-spaces, providing geometric, deterministic, and closed solutions. The introduced $\mathcal{C}$-spaces are characterized based on generic domain and viewpoint constraints models to ease the transferability of the present framework to different applications and robot vision systems. The effectiveness and efficiency of the concepts introduced are verified on a simulation-based scenario and validated on a real robot vision system comprising two different sensors. Full article

Soft pneumatic grippers (SPGs) have garnered significant attention and recognition in various industries owing to their remarkable flexibility, safety, and adaptability. They excel in manipulating delicate, irregularly shaped, and soft objects, surpassing the limitations of conventional grippers. However, effective control techniques for managing the grasping profile of SPGs are still under development. Simple on–off pressure control using a regulator valve is inadequate for delicate gripping with pneumatic robot grippers. To address this, a synergy pressure control system was implemented. In addition, a proportional–integral–derivative control technique, complemented by an unknown input observer, was devised to control the volume of the soft pneumatic robotic gripper, ensuring its alignment with the desired volume level. The simulation and experimental results provide substantial evidence of the effectiveness of the developed control technique and the unknown input observer in managing the volume and pressure of the gripper. Consequently, this breakthrough empowers precise and delicate gripping actions, enabling the handling of delicate objects such as tofu. Full article

Exoskeleton devices are designed for applications such as rehabilitation, assistance, and haptics. Due to the nature of physical human–machine interaction, designing and operating these devices is quite challenging. Optimization methods lessen the severity of these challenges and help designers develop the device they need. In this paper, we present an extensive and systematic literature search on the optimization methods used for the mechanical design of exoskeletons. We completed the search in the IEEE, ACM, and MDPI databases between 2017 and 2023 using the keywords “exoskeleton”, “design”, and “optimization”. We categorized our findings in terms of which limb (i.e., hand, wrist, arm, or leg) and application (assistive, rehabilitation, or haptic) the exoskeleton was designed for, the optimization metrics (force transmission, workspace, size, and adjustability/calibration), and the optimization method (categorized as evolutionary computation or non-evolutionary computation methods). We discuss our observations with respect to how the optimization methods have been implemented based on our findings. We conclude our paper with suggestions for future research. Full article

This research proposes a robust nonlinear hybrid control approach to the speed control of a multi-input-and-multi-output separately excited DC motor (SEDCM). The motor that was under consideration experienced parametric uncertainties and load disturbances in the weak field region. The proposed technique aims to merge the benefits of adaptive backstepping (AB) and integral sliding mode control (ISMC) to enhance the overall system’s robustness. The unknown parameters with load disturbances are estimated using an adaptation law. These estimated parameters are incorporated into the controller design, to achieve a highly robust controller. The theoretical stability of the system is proved using the Lyapunov stability criteria. The effectiveness of the proposed AB–ISMC was demonstrated by simulation, to track the reference speed under parametric uncertainties and load disturbances. The control performance of the proposed technique was compared to that of feedback linearization (FBL), conventional sliding mode control (SMC), and AB control laws without and with the adaptation law. Regression parameters, such as integral square error, integral absolute error, and integral time absolute error, were calculated to quantitatively analyze the tracking performance and robustness of the implemented nonlinear control techniques. The simulation results demonstrated that the proposed controller could accurately track the reference speed and exhibited robustness, with steady-state error accuracy. Moreover, AB–ISMC overperformed, compared to the FBL, SMC, AB controller without adaptation law and AB controller with adaptation law, in reducing the settling time by factors of $27\%$, $67\%$, $23\%$, and $21\%$, respectively, thus highlighting the superior performance of the proposed controller. Full article

In recent years, Embodied AI has become one of the main topics in robotics. For the agent to operate in human-centric environments, it needs the ability to explore previously unseen areas and to navigate to objects that humans want the agent to interact with. This task, which can be formulated as ObjectGoal Navigation (ObjectNav), is the main focus of this work. To solve this challenging problem, we suggest a hybrid framework consisting of both not-learnable and learnable modules and a switcher between them—SkillFusion. The former are more accurate, while the latter are more robust to sensors’ noise. To mitigate the sim-to-real gap, which often arises with learnable methods, we suggest training them in such a way that they are less environment-dependent. As a result, our method showed top results in both the Habitat simulator and during the evaluations on a real robot. Full article

In this paper, we propose a solution for the problem of searching for multiple targets by a group of mobile agents with sensing errors of the first and the second types. The agents’ goal is to plan the search and follow its trajectories that lead to target detection in minimal time. Relying on real sensors’ properties, we assume that the agents can detect the targets in various directions and distances; however, they are exposed to first- and second-type statistical errors. Furthermore, we assume that the agents in the group have errorless communication with each other. No central station or coordinating agent is assumed to control the search. Thus, the search follows a fully distributed decision-making process, in which each agent plans its path independently based on the information about the targets, which is collected independently or received from the other agents. The suggested solution includes two algorithms: the Distributed Expected Information Gain (DEIG) algorithm, which implements dynamic Voronoi partitioning of the search space and plans the paths by maximizing the expected one-step look-ahead information per region, and the Collective Q-max (CQM) algorithm, which finds the shortest paths of the agents in the group by maximizing the cumulative information about the targets’ locations using deep Q-learning techniques. The developed algorithms are compared against previously developed reactive and learning methods, such as the greedy centralized Expected Information Gain (EIG) method. It is demonstrated that these algorithms, specifically the Collective Q-max algorithm, considerably outperform existing solutions. In particular, the proposed algorithms improve the results by 20% to 100% under different scenarios of noisy environments and sensors’ sensitivity. Full article

This manuscript presents an efficient algorithm for solving the inverse kinematics problem of a 6R robot manipulator to be deployed on embedded control hardware. The proposed method utilizes the geometric relationship between the end-effector and the base of the manipulator, resulting in a computationally efficient solution. The approach aims to minimize computational complexity and memory consumption while maintaining the accuracy and real-time performance demonstrated by simulations and verified by experimental results on an embedded system. Furthermore, the manipulator is analyzed in terms of singularities, limits, the workspace, and general solvability. Due to the simplicity of the algorithm, a platform-independent implementation is possible. As a result, the average calculation time is reduced by a factor of five to eight and the average error is decreased by a factor of fifty compared to a powerful analytical solver. Full article

In recent years, deep learning techniques for processing 3D point cloud data have seen significant advancements, given their unique ability to extract relevant features and handle unstructured data. These techniques find wide-ranging applications in fields like robotics, autonomous vehicles, and various other computer-vision applications. This paper reviews the recent literature on key tasks, including 3D object classification, tracking, pose estimation, segmentation, and point cloud completion. The review discusses the historical development of these methods, explores different model architectures, learning algorithms, and training datasets, and provides a comprehensive summary of the state-of-the-art in this domain. The paper presents a critical evaluation of the current limitations and challenges in the field, and identifies potential areas for future research. Furthermore, the emergence of transformative methodologies like PoinTr and SnowflakeNet is examined, highlighting their contributions and potential impact on the field. The potential cross-disciplinary applications of these techniques are also discussed, underscoring the broad scope and impact of these developments. This review fills a knowledge gap by offering a focused and comprehensive synthesis of recent research on deep learning techniques for 3D point cloud data processing, thereby serving as a useful resource for both novice and experienced researchers in the field. Full article

Emotion recognition is a vital part of human functioning. textcolorredIt enables individuals to respond suitably to environmental events and develop self-awareness. The fast-paced developments in brain–computer interfacing (BCI) technology necessitate that intelligent machines of the future be able to digitize and recognize human emotions. To achieve this, both humans and machines have relied on facial expressions, in addition to other visual cues. While facial expressions are effective in recognizing emotions, they can be artificially replicated and require constant monitoring. In recent years, the use of Electroencephalography (EEG) signals has become a popular method for emotion recognition, thanks to advances in deep learning and machine learning techniques. EEG-based systems for recognizing emotions involve measuring electrical activity in the brain of a subject who is exposed to emotional stimuli such as images, sounds, or videos. Machine learning algorithms are then used to extract features from the electrical activity data that correspond to specific emotional states. The quality of the extracted EEG signal is crucial, as it affects the overall complexity of the system and the accuracy of the machine learning algorithm. This article presents an approach to improve the accuracy of EEG-based emotion recognition systems while reducing their complexity. The approach involves optimizing the number of EEG channels, their placement on the human scalp, and the target frequency band of the measured signal to maximize the difference between high and low arousal levels. The optimization method, called the simplicial homology global optimization (SHGO), is used for this purpose. Experimental results demonstrate that a six-electrode configuration optimally placed can achieve a better level of accuracy than a 14-electrode configuration, resulting in an over 60% reduction in complexity in terms of the number of electrodes. This method demonstrates promising results in improving the efficiency and accuracy of EEG-based emotion recognition systems, which could have implications for various fields, including healthcare, psychology, and human–computer interfacing. Full article

The article proposes an approach for synthesizing hybrid (parallel-serial) manipulators with five degrees of freedom (5-DOF) using open kinematic chains. The method idea consists in taking an open kinematic chain, selecting a subchain within it, and replacing the subchain with a parallel mechanism. The article considers 5-DOF open chains and 3-DOF subchains, substituted for 3-DOF parallel mechanisms with the same motion pattern as the subchain. Thus, synthesized hybrid manipulators have a 3-DOF parallel part and a 2-DOF serial part. First, we grouped 26 structures of open chains with revolute and prismatic joints into five types and 78 subtypes. Next, for each type, we selected one subtype and presented several hybrid mechanisms that can correspond to it. We considered hybrid manipulators that included 3-DOF parallel mechanisms with planar, spherical, and other commonly used motion types. The suggested synthesis method is intuitive for a designer, and it does not need any mathematical formulations like screw theory or group theory approaches. Full article

The daVinci Surgical Robot has revolutionized minimally invasive surgery by enabling greater accuracy and less-invasive procedures. However, the system lacks the advanced features and autonomy necessary for it to function as a true partner. To enhance its usability, we introduce the implementation of a ChatGPT-based natural language robot interface. Overall, our integration of a ChatGPT-enabled daVinci Surgical Robot has potential to expand the utility of the surgical platform by supplying a more accessible interface. Our system can listen to the operator speak and, through the ChatGPT-enabled interface, translate the sentence and context to execute specific commands to alter the robot’s behavior or to activate certain features. For instance, the surgeon could say (even in Spanish) “please track my left tool” and the system will translate the sentence into a specific track command. This specific error-checked command will then be sent to the hardware, which will respond by controlling the camera of the system to continuously adjust and center the left tool in the field of view. We have implemented many commands, including “Find my tools” (tools that are not in the field of view) or start/stop recording, that can be triggered based on a natural conversational context. Here, we present the details of our prototype system, give some accuracy results, and explore its potential implications and limitations. We also discuss how artificial intelligence tools (such as ChatGPT) of the future could be leveraged by robotic surgeons to reduce errors and enhance the efficiency and safety of surgical procedures and even ask for help. Full article

Three-dimensional concrete printing technology is currently a very topical and developing subject. There is a large number of applications worldwide where this technology can be used. In connection with this technology, the development of custom industrial robotic systems and their control is essential. Conventional closed-loop control system platforms do not provide sufficiently flexible solutions. This paper presents a control system for a unique printing robot that, thanks to its openness and unified platform, will enable simple and fast analysis and testing of key aspects in terms of control and guidance of the printing robot for additive manufacturing applications in the construction industry. The aim of this paper is to introduce the concept of an open PLC-based control system and to demonstrate its usefulness in the task of designing and implementing model-based control. All steps, from the analysis of the printing robot itself and identification of inertial parameters to the actual design and implementation of the control, can be executed in a unified Matlab/Simulink environment using various add-ons and toolboxes thanks to the open control system platform. This solution brings significant savings in terms of programming and prototyping time. The open control system was used to control an experimental model of a printing robot, serving as a test bed for the final version of the printing robot, and the results obtained were evaluated. Full article

One of the most difficult parts of stroke therapy is hand mobility recovery. Indeed, stroke is a serious medical disorder that can seriously impair hand and locomotor movement. To improve hand function in stroke patients, new medical technologies, such as various wearable devices and rehabilitation therapies, are being developed. In this study, a new design of electromyography (EMG)-controlled 3D-printed hand exoskeleton is presented. The exoskeleton was created to help stroke victims with their gripping abilities. Computer-aided design software was used to create the device’s 3D architecture, which was then printed using a polylactic acid filament. For online classifications, the performance of two classifiers—the support vector machine (SVM) and the K-near neighbor (KNN)—was compared. The Robot Operating System (ROS) connects all the various system nodes and generates the decision for the hand exoskeleton. The selected classifiers had high accuracy, reaching up to 98% for online classification performed with healthy subjects. These findings imply that the new wearable exoskeleton, which could be controlled in accordance with the subjects’ motion intentions, could aid in hand rehabilitation for a wider motion range and greater dexterity. Full article