Robotics, Volume 13, Issue 6 (June 2024) – 11 articles

As the population of older adults increases, there is an anticipated rise in the utilization of hospice and palliative care. Many significant advancements in technology have been used to address the unique needs of this demographic; however, an unexplored area of research is the use of robotic animals as part of end-of-life care. The purpose of this scoping review was to examine the state of the literature on robotic animal use among older adults enrolled in palliative or hospice care and to offer a framework for future research. Following a guide for scoping reviews, we identified relevant studies and then charted, collated, summarized, and reported the data. Two articles were selected for final review. The results found that decreased medication use, behavior change, and emotional benefits were potential outcomes of robotic animal use in hospice and palliative care. Perceptions of the robot and ethical considerations were also discussed. Overall, the study findings point toward the potential uses of robotic animals as part of end-of-life care, however, more empirical research is critically needed. Full article

Because they are safe and easy to use, collaborative robots are revolutionizing many sectors, including industry, medicine, and agriculture. Controlling their dynamics, movements, and postures are key points in this evolution. Inverse kinematics is then crucial for robot motion planning. In 6R serial robots, achieving a desired pose is possible with different joint combinations. In this paper, our focus lies in studying forward and, mainly, inverse kinematics of the FANUC CRX-10iA cobot, a 6R cobotic arm with a non-spherical wrist. Its specific structural parameters implies that no analytical solutions exist except for some particular situations. FANUC does not provide the complete set of inverse kinematic solutions, even when 16 solutions are possible, only 8 of them are provided in Roboguide software. Furthermore, the existing literature on joints-to-workspace mapping for CRX cobots is currently very limited. It either lacks or provides partial or inconsistent inverse kinematics analysis. We present and detail a novel fully geometric method for numerically solving inverse kinematics meeting the requirement of high precision and a fast response. This approach provides both the exact number of inverse kinematics solutions and the sets of joint angles even for singular configuration. Its effectiveness was verified through simulations using the Roboguide Software and experimentation on the actual CRX-10iA cobot. Several examples (8, 12, or 16 inverse kinematic solutions) have enabled us to validate and prove the robustness and reliability of this geometric approach. Full article

In cases where vision is not sufficiently reliable for robots to recognize an object, tactile sensing can be a promising alternative for estimating the object’s pose. In this paper, we consider the task of a robot estimating the pose of a container aperture in order to select an object. In such a task, if the robot can determine whether its hand with equipped contact sensor is inside or outside the container, estimation of the object’s pose can be improved by reflecting the discrimination to the robotic hand’s exploration strategy. We propose an exploration strategy and an estimation method using discrete state recognition on the basis of a particle filter. The proposed method achieves superior estimation in terms of the number of contact actions, operation time, and stability of estimation efficiency. The pose is estimated with sufficient accuracy that the hand can be inserted into the container. Full article

Master–slave teleoperation systems with haptic feedback enable human operators to interact with objects or perform tasks in remote environments. This paper presents a sliding-mode control scheme tailored for bilateral teleoperation systems operating in the presence of unknown uncertainties and time-varying delays. To address unknown but bounded uncertainties, adaptive laws are derived alongside controller design. Additionally, a linear matrix inequality is solved to determine the allowable bound of delays. Stability of the closed-loop system is ensured through Lyapunov–Krasovskii functional analysis. Two-degree-of-freedom mechanisms are self-built as haptic devices. Free-motion and force-perception scenarios are examined, with experimental results validating and comparing performances. The proposed adaptive-sliding-control method increases the position performance from 58.48% to 82.55% and the force performance from 83.48% to 99.77%. The proposed control scheme demonstrates enhanced position tracking and force perception in bilateral teleoperation systems. Full article

Industry 4.0 has become one of the most dominant research areas in industrial science today. Many industrial machinery units do not have modern standards that allow for the use of image analysis techniques in their commissioning. Intelligent material handling, sorting, and object recognition are not possible with the machinery we have. We therefore propose a novel deep learning approach for existing robotic devices that can be applied to future robots without modification. In the implementation, 3D CAD models of the PCB relay modules to be recognized are also designed for the implantation machine. Alternatively, we developed and manufactured parts for the assembly of aluminum profiles using FDM 3D printing technology, specifically for sorting purposes. We also apply deep learning algorithms based on the 3D CAD models to generate a dataset of objects for categorization using CGI rendering. We generate two datasets and apply image-to-image translation techniques to train deep learning algorithms. The synthesis achieved sufficient information content and quality in the synthesized images to train deep learning algorithms efficiently with them. As a result, we propose a dataset translation method that is suitable for situations in which regenerating the original dataset can be challenging. The results obtained are analyzed and evaluated for the dataset. Full article

The spherical shape is an interesting approach to develop exploration robots, or rovers, thanks to its capability of ensuring omnidirectional motion and of being basically unsensitive to possible rollovers. This works intends to propose a novel detailed design for such a kind of robot and to discuss the performance that can be reached by adopting this solution. The work hence introduces the requirements assumed for the design of the robot and discloses the general layout that was selected, which includes a pendulum for motion transmission and two coupled gyroscopes to overcome high, steep obstacles, such as steps. The paper then summarizes the functional design computation carried out to size and selects the components of the system. Eventually, a control algorithm is described and tested on a complete multibody model of the robot. The results in the execution of standard maneuvers such as motion on a horizontal plane, as well as in the overcome of a step, are shown. The energetic balance of the rover is described, and some preliminary consideration about mission planning are reported in the final discussion. Full article

Recent advancements in quadrupedal robotics have explored the motor potential of these machines beyond simple walking, enabling highly dynamic skills such as jumping, backflips, and even bipedal locomotion. While reinforcement learning has demonstrated excellent performance in this domain, it often relies on complex reward function tuning and prolonged training times, and the interpretability is not satisfactory. Riemannian motion policies, a reactive control method, excel in handling highly dynamic systems but are generally limited to fully actuated systems, making their application to underactuated quadrupedal robots challenging. To address these limitations, we propose a novel framework that treats each leg of a quadrupedal robot as an intelligent agent and employs multi-agent reinforcement learning to coordinate the motion of all four legs. This decomposition satisfies the conditions for utilizing Riemannian motion policies and eliminates the need for complex reward functions, simplifying the learning process for high-level motion modalities. Our simulation experiments demonstrate that the proposed method enables quadrupedal robots to learn stable locomotion using three, two, or even a single leg, offering advantages in training speed, success rate, and stability compared to traditional approaches, and better interpretability. This research explores the possibility of developing more efficient and adaptable control policies for quadrupedal robots. Full article

Multiple-suction-cup grasping can improve the efficiency of bin picking in cluttered scenes. In this paper, we propose a grasp planner for a vacuum gripper to use multiple suction cups to simultaneously grasp multiple objects or an object with a large surface. To take on the challenge of determining where to grasp and which cups to activate when grasping, we used 3D convolution to convolve the affordable areas inferred by a neural network with the gripper kernel in order to find graspable positions of sampled gripper orientations. The kernel used for 3D convolution in this work was encoded, including cup ID information, which helps to directly determine which cups to activate by decoding the convolution results. Furthermore, a sorting algorithm is proposed to determine the optimal grasp among the candidates. Our planner exhibited good generality and successfully found multiple-cup grasps in previous affordance map datasets. Our planner also exhibited improved picking efficiency using multiple suction cups in physical robot-picking experiments. Compared with single-object (single-cup) grasping, multiple-cup grasping contributed to $1.45\times$, $1.65\times$, and $1.16\times$ increases in efficiency for picking boxes, fruits, and daily necessities, respectively. Full article

Automatic control of robots with flexible links has been a pivotal subject in control engineering and robotics due to the challenges posed by vibrations during repetitive movements. These vibrations affect the system’s performance and accuracy, potentially causing errors, wear, and failures. LQR control is a common technique for vibration control, but determining the optimal weight matrices [Q] and [R] is a complex and crucial task. This paper proposes a methodology based on genetic algorithms to define the [Q] and [R] matrices according to design requirements. MATLAB and Simulink, along with data provided by Quanser, will be used to model and evaluate the performance of the proposed approach. The process will include testing and iterative adjustments to optimize performance. The work aims to improve the control of robots with flexible links, offering a methodology that allows for the design of LQR control under the design requirements of controllers used in classical control through the use of genetic algorithms. Full article

Over the last years, a rapid evolution of unmanned aerial vehicle (UAV) usage in various applications has been observed. Their use in indoor environments requires a precise perception of the surrounding area, immediate response to its changes, and, consequently, a robust position estimation. This paper provides an implementation of navigation algorithms for solving the problem of fast, reliable, and low-cost inventorying in the logistics industry. The drone localization is achieved with a particle filter algorithm that uses an array of distance sensors and an inertial measurement unit (IMU) sensor. Navigation is based on a proportional–integral–derivative (PID) position controller that ensures an obstacle-free path within the known 3D map. As for the full 3D coverage, an extraction of the targets and then their final succession towards optimal coverage is performed. Finally, a series of experiments are carried out to examine the robustness of the positioning system using different motion patterns and velocities. At the same time, various ways of traversing the environment are examined by using different configurations of the sensor that is used to perform the area coverage. Full article

Loss of hand functions, often manifesting in the form of weakness or spasticity from conditions like stroke or multiple sclerosis, poses challenges in performing activities of daily living (ADLs). The broad area of rehabilitation robotics provides the tools and knowledge necessary for implementing efficient restorative therapies. These therapies aim to improve hand functionality with minimal therapist intervention. However, the human hand evolved for various precision and power gripping tasks, with its intricate anatomy featuring a large number of degrees of freedom—up to 31—which hinder its modeling in many rehabilitation scenarios. In the process of designing prosthetic devices, instrumented gloves, and rehabilitation devices, there is a clear need to obtain simplified rehabilitation-oriented hand models without compromising their representativeness across the population. This is where the concept of kinematic reduction, focusing on specific grasps, becomes essential. Thus, the objective of this study is to uncover the intra-finger dependencies during finger flexion/extension by analyzing a comprehensive database containing recorded trajectories for 23 different functional movements related to ADLs, involving 77 test subjects. The initial phase involves data wrangling, followed by correlation analysis aimed at selecting 116 dependency-movement relationships across all grasps. A regularized generalized linear model is then applied to select uncorrelated predictors, while a linear mixed-effect model, with reductions based on both predictor significance and effect size, is used for modeling the dependencies. As a final step, agglomerative clustering of models is performed to further facilitate flexibility in tradeoffs in hand model accuracy/reduction, allowing the modeling of finger flexion extensions using 5–15 degrees of freedom only. Full article