Object Manipulation Research Articles

Elements of decorative prose in the novel by Alexander Sobolev “Griffins guard the lyre”

The topic of the article is relevant because it considers the specific poetics of the modern neo-modernist work. It is worth noting that the novel “Griffins Guard the Lyre” is a part of a new wave of modernism that has emerged in the Russian literature over the past few decades. In addition to A. Sobolev, this wave includes such writers as E. Vodolazkin, S. Bolmat, A. Varlamov and others. The purpose of the article is to describe the functions of the ornamental prose elements in A. Sobolev’s work “Griffins Guard the Lyre.” The main research methods used in the article are semiotic and intertextual analyses. Besides A. Sobolev's novel, the works of V. Nabokov, Y. Olesha, and F. Sologub served as the empirical materials. In the course of considering the work of A. Sobolev from the point of view of the criteria applied to the ornamental prose, it was found that the novel has the following features: a) an organic fusion of the epic and lyrical principles; b) destruction and deconstruction of genre forms. The fairy tale principle is also present in A. Sobolev’s novel, but it performs completely different functions. The author seems to use the tale as an object of manipulation. We can conclude that A. Sobolev borrowed some techniques from domestic postmodernists/conceptualists. Since the elements of the ornamental writing in the novel “The Griffins Guard the Lyre” have not been considered before, we can confidently state that all the obtained scientific results are new.

Development of a Two-Finger Haptic Robotic Hand with Novel Stiffness Detection and Impedance Control.

Haptic hands and grippers, designed to enable skillful object manipulation, are pivotal for high-precision interaction with environments. These technologies are particularly vital in fields such as minimally invasive surgery, where they enhance surgical accuracy and tactile feedback: in the development of advanced prosthetic limbs, offering users improved functionality and a more natural sense of touch, and within industrial automation and manufacturing, they contribute to more efficient, safe, and flexible production processes. This paper presents the development of a two-finger robotic hand that employs simple yet precise strategies to manipulate objects without damaging or dropping them. Our innovative approach fused force-sensitive resistor (FSR) sensors with the average current of servomotors to enhance both the speed and accuracy of grasping. Therefore, we aim to create a grasping mechanism that is more dexterous than grippers and less complex than robotic hands. To achieve this goal, we designed a two-finger robotic hand with two degrees of freedom on each finger; an FSR was integrated into each fingertip to enable object categorization and the detection of the initial contact. Subsequently, servomotor currents were monitored continuously to implement impedance control and maintain the grasp of objects in a wide range of stiffness. The proposed hand categorized objects' stiffness upon initial contact and exerted accurate force by fusing FSR and the motor currents. An experimental test was conducted using a Yale-CMU-Berkeley (YCB) object set consisted of a foam ball, an empty soda can, an apple, a glass cup, a plastic cup, and a small milk packet. The robotic hand successfully picked up these objects from a table and sat them down without inflicting any damage or dropping them midway. Our results represent a significant step forward in developing haptic robotic hands with advanced object perception and manipulation capabilities.

Permainan Edukasi Puzzle Math Untuk Menstimulasi Motorik Siswa

This service examines the necessity and pertinence of adopting a pedagogical approach that incorporates educational game components in order to promote motor development and enhance student engagement in the learning process. On November 18, 2023, this service activity was conducted at UPTD. SD Negeri 014672 Tanjung Alam. This activity is structured into three distinct phases during its execution: preparation, implementation, and practice. Based on empirical findings, engaging in direct interaction with mathematical puzzles has been found to enhance hand-motor coordination, finger dexterity, and precision control during object manipulation. Ninety-five percent of students were extremely interested in this educational game; eighty-five percent felt that math puzzle games improved their fine motor skills (hand and finger movements); and ninety-two percent intend to continue playing math puzzles on other materials in the future, according to the results of the questionnaire assessing the experience of playing with math puzzles. Students' fine motor skills improved significantly as a result of the implementation of this game, it was concluded.

Programmable photoacoustic patterning of microparticles in air.

Optical and acoustic tweezers, despite operating on different physical principles, offer non-contact manipulation of microscopic and mesoscopic objects, making them essential in fields like cell biology, medicine, and nanotechnology. The advantages and limitations of optical and acoustic manipulation complement each other, particularly in terms of trapping size, force intensity, and flexibility. We use photoacoustic effects to generate localized Lamb wave fields capable of mapping arbitrary laser pattern shapes. By using localized Lamb waves to vibrate the surface of the multilayer membrane, we can pattern tens of thousands of microscopic particles into the desired pattern simultaneously. Moreover, by quickly and successively adjusting the laser shape, microparticles flow dynamically along the corresponding elastic wave fields, creating a frame-by-frame animation. Our approach merges the programmable adaptability of optical tweezers with the potent manipulation capabilities of acoustic waves, paving the way for wave-based manipulation techniques, such as microparticle assembly, biological synthesis, and microsystems.

Vision-Based Object Manipulation for Activities of Daily Living Assistance Using Assistive Robot

The increasing prevalence of upper and lower extremity (ULE) functional deficiencies presents a significant challenge, as it restricts individuals’ ability to perform daily tasks independently. Robotic devices are emerging as assistive devices to assist individuals with limited ULE functionalities in activities of daily living (ADLs). While assistive manipulators are available, manual control through traditional methods like joysticks can be cumbersome, particularly for individuals with severe hand impairments and vision limitations. Therefore, autonomous/semi-autonomous control of a robotic assistive device to perform any ADL task is open to research. This study addresses the necessity of fostering independence in ADLs by proposing a creative approach. We present a vision-based control system for a six-degrees-of-freedom (DoF) robotic manipulator designed for semi-autonomous “pick-and-place” tasks, one of the most common activities among ADLs. Our approach involves selecting and training a deep-learning-based object detection model with a dataset of 47 ADL objects, forming the base for a 3D ADL object localization algorithm. The proposed vision-based control system integrates this localization technique to identify and manipulate ADL objects (e.g., apples, oranges, capsicums, and cups) in real time, returning them to specific locations to complete the “pick-and-place” task. Experimental validation involving an xArm6 (six DoF) robot from UFACTORY in diverse settings demonstrates the system’s adaptability and effectiveness, achieving an overall 72.9% success rate in detecting, localizing, and executing ADL tasks. This research contributes to the growing field of autonomous assistive devices, enhancing independence for individuals with functional impairments.

Development of a Three-Finger Adaptive Robotic Gripper to Assist Activities of Daily Living.

A significant number of individuals in the United States use assistive devices to enhance their mobility, and a considerable portion of those who depend on such aids require assistance from another individual in performing daily living activities. The introduction of robotic grippers has emerged as a transformative intervention, significantly contributing to the cultivation of independence. However, there are few grippers in the fields, which help with mimicking human hand-like movements (mostly grasping and pinching, with adoptive force control) to grasp and carry objects. Additionally, the data are not available even on how many Activities of Daily Living (ADL) objects they can handle. The goal of the research is to offer a new three-fingered gripper for daily living assistance, which can both grasp and pinch with adaptive force, enabling the capabilities of handling wide-ranging ADL objects with a minimal footprint. It is designed to handle 90 selective essential ADL objects of different shapes (cylindrical, irregular, rectangular, and round), sizes, weights, and textures (smooth, rough, bumpy, and rubbery). The gripper boasts a meticulously engineered yet simple design, facilitating seamless manufacturing through 3D printing technology without compromising its operational efficacy. The gripper extends its functionality beyond conventional grasping, featuring the capability to pinch (such as holding a credit card) and securely hold lightweight objects. Moreover, the gripper is adaptable to grasping various objects with different shapes and weights with controlled forces. In evaluation, the developed gripper went through rigorous load tests and usability tests. The results demonstrated that the users picked and placed 75 objects out of 90 daily objects. The gripper held and manipulated objects with dimensions from 25 mm to 80 mm and up to 2.9 kg. For heavy-weight objects (like books) where the centroid is far apart from the grasping areas, it is difficult to hold them due to high torque. However, objects' textures have no significant effect on grasping performance. Users perceived the simplicity of the gripper. Further investigation is required to assess the utility and longevity of grippers. This study contributes to developing assistive robots designed to enhance object manipulation, thereby improving individuals' independence and overall quality of life.

Effects of simple cage enrichment and its removal on the behavior and welfare of American mink (Neogale vison)

ABSTRACT Environmental enrichment may reduce stereotypies in fur-farmed mink. North American mink standards require manipulable enrichment objects within cages. However, mink can rapidly destroy objects inhibiting continuous enrichment presence, which may have negative welfare impacts. This experimental study determined the effects of removing simple cage manipulable enrichments (plastic chains and dumbbells), either short-term or longer-term, on the behavioral expression of welfare in fur-farmed mink. Locomotor stereotypies, normal activity, sub-types of inactivity related to boredom, and tail fur-chewing were recorded across four treatment groups with either (1) no enrichment, (2) continuous enrichment, (3) short (temporary), or (4) long-term enrichment removal. Contrary to predictions, locomotor stereotypies, and scrabbling were not reduced by the enrichments, nor affected by the enrichment removal. Observations at the beginning of the trial showed the non-enriched mink spent the least amount of time lying with their eyes open (i.e., the least bored). The lack of enrichment may have increased fur-chewing on the tail, but larger sample sizes would be needed for statistical confirmation. This research contributes to the literature on evaluating simple, practical enrichments for improving fur-farmed mink welfare.

The Relation between Infants’ Manual Lateralization and Their Performance of Object Manipulation and Tool Use

Previous research yielded inconsistent findings regarding whether manual lateralization (e.g., a distinct and consistent hand preference) affects manual performance during infancy and early childhood. The aim of the current study was to determine whether manual lateralization, viewed as a marker of hemispheric lateralization, is associated with infants’ performance in role-differentiated bimanual manipulation (RDBM) and tool use. This longitudinal study assessed 158 typically developing infants (91 males, aged 9.13 ± 0.15 months at baseline) monthly during the 9–14-month period. Developmental trajectories for manual lateralization in object acquisition were related to those for RDBM and tool use, even after accounting for potential sex differences. All statistical analyses were conducted using Hierarchical Linear Modeling software (version 6). Advanced RDBM performance was associated with a lower magnitude of manual lateralization and a higher tendency among infants to use both hands for object acquisition. No significant relation was found between the magnitude of manual lateralization and tool-use performance. Thus, the current results highlight the importance of hand coupling for enhanced RDBM performance. Moreover, across all ages, females outperformed males in sophisticated RDBMs, possibly due to their less pronounced manual lateralization and a greater inclination towards bimanual object acquisition—factors that appear to facilitate RDBM performance.

THIẾT KẾ, CHẾ TẠO GĂNG TAY HỖ TRỢ PHỤC HỒI CHỨC NĂNG CHO NGƯỜI KHUYẾT TẬT SỬ DỤNG CÁC NGÓN MỀM

This article presents the development and production process of a glove designed for rehabilitation, and enhancing object manipulation capabilities for individuals with hand mobility limitations, using soft finger design. The soft fingers are constructed from a composite structure consisting of silicone rubber material. An automated calculation program is created in Python and integrated with Abaqus/CAE software, enabling the concurrent exploration of numerous parameters governing the soft fingers to choose a rational design of glove. Experimental tests reveal that the error of the experiment and simulation results is less than 7.8%. Furthermore, these deformable soft fingers can closely mimic the contours of human fingers. Leveraging the successful construction of the soft fingers, a corresponding soft glove is manufactured. Preliminary validated experiments demonstrate the effectiveness of these soft gloves in aiding patients with diverse exercise rehabilitation. Additionally, they provide support for basic tasks such as grasping objects weighing approximately 600 grams.

Arm muscle synergies enhance hand posture prediction in combination with forearm muscle synergies

Objective. We analyze and interpret arm and forearm muscle activity in relation with the kinematics of hand pre-shaping during reaching and grasping from the perspective of human synergistic motor control. Approach. Ten subjects performed six tasks involving reaching, grasping and object manipulation. We recorded electromyographic (EMG) signals from arm and forearm muscles with a mix of bipolar electrodes and high-density grids of electrodes. Motion capture was concurrently recorded to estimate hand kinematics. Muscle synergies were extracted separately for arm and forearm muscles, and postural synergies were extracted from hand joint angles. We assessed whether activation coefficients of postural synergies positively correlate with and can be regressed from activation coefficients of muscle synergies. Each type of synergies was clustered across subjects. Main results. We found consistency of the identified synergies across subjects, and we functionally evaluated synergy clusters computed across subjects to identify synergies representative of all subjects. We found a positive correlation between pairs of activation coefficients of muscle and postural synergies with important functional implications. We demonstrated a significant positive contribution in the combination between arm and forearm muscle synergies in estimating hand postural synergies with respect to estimation based on muscle synergies of only one body segment, either arm or forearm (p < 0.01). We found that dimensionality reduction of multi-muscle EMG root mean square (RMS) signals did not significantly affect hand posture estimation, as demonstrated by comparable results with regression of hand angles from EMG RMS signals. Significance. We demonstrated that hand posture prediction improves by combining activity of arm and forearm muscles and we evaluate, for the first time, correlation and regression between activation coefficients of arm muscle and hand postural synergies. Our findings can be beneficial for myoelectric control of hand prosthesis and upper-limb exoskeletons, and for biomarker evaluation during neurorehabilitation.

Activities of daily living with grasp types and force measurements during object manipulation

Background: Limited scientific evidence guides hand rehabilitation towards improved hand function, and safe early return to work. Grasp types, the subunits of object manipulation and the forces which may improve functional outcomes, have been identified as missing links that may inform rehabilitation after second to fifth metacarpal fractures, through progression of basic and instrumental activities of daily living (ADL). The aim of the study was to collect ADL forces through grasp types to inform hand rehabilitation for second to fifth metacarpal fractures. Methods: This cross-sectional, quantitative study included 32 conveniently sampled healthy adults aged 20 and 59 years. Thirty-one (31) ADLs, both basic and instrumental, each associated with a predominant grasp type, were tested. The participants donned two testing gloves, with force sensing resistors (FSRs) glued to the fingertips, the ADLs were performed, and forces measured. The researcher imported the force data into an Excel spreadsheet for both descriptive and inferential analyses with STATA. Results: Fourteen males and 18 females, with a mean age of 37 years, participated. Statistically significant differences between genders were found for seven grasp types. Three thumb-adducted power palm grasps, three thumb-abduction precision pad grasps, and one thumb-abduction power palm grasp constituted the testing. Light and heavy ADLs and their associated grasp types were identified. Conclusion: To ensure optimal hand function, early safe return to ADLs should be the goal of second to fifth metacarpal fracture rehabilitation and may be achieved by incorporating active grasp types as exercises with graded resisted grasp types and ADLs. Implications for practice • Occupational therapists can incorporate grasp types in their observation during assessments of individuals who sustained second to fifth metacarpal fractures to determine the subcomponents of functional deficits. • Hand rehabilitation may be augmented by incorporating grasp types in isolation during unresisted active joint range of motion. • Occupational therapists, guided by the analysis of hand functioning, can incorporate ADLs with the predominant grasp types which may improve hand function. • Grasp force data may guide safe and early return to function.

A New Manipulating Method of Metal Micro-Components Based on Electrochemistry

This article presents a new manipulating method of metal micro-components based on electrochemistry aiming at the problems of inaccurate release and easy damage to manipulated objects by conventional micro gripping tools and vacuum adsorption tools. This method uses a small-sized micropipette to manipulate large-sized metal micro-components. The reliable picking up is achieved by forming a strong chemical bond between the small-sized pipette and the manipulation object. The accurate and non-destructive release is achieved by converting the chemical bonding force into a more easily overcome capillary force through electrolysis. The electrochemical and the force models of the metal microcomponents based on electrochemistry are simulated by COMSOL and Abaqus, to obtain optimal electrochemical parameters and manipulation strategies. The experiments of picking up and releasing were performed to demonstrate the performance of the proposed method. The results show that this method can enable reliable and accurate manipulation of large-size objects with small-size tools and have a high success rate and efficiency.

Integrated Modular Neural Control for Versatile Locomotion and Object Transportation of a Dung Beetle-Like Robot.

Dung beetles can effectively transport dung pallets of various sizes in any direction across uneven terrain. While this impressive ability can inspire new locomotion and object transportation solutions in multilegged (insect-like) robots, to date, most existing robots use their legs primarily to perform locomotion. Only a few robots can use their legs to achieve both locomotion and object transportation, although they are limited to specific object types/sizes (10%-65% of leg length) on flat terrain. Accordingly, we proposed a novel integrated neural control approach that, like dung beetles, pushes state-of-the-art insect-like robots beyond their current limits toward versatile locomotion and object transportation with different object types/sizes and terrains (flat and uneven). The control method is synthesized based on modular neural mechanisms, integrating central pattern generator (CPG)-based control, adaptive local leg control, descending modulation control, and object manipulation control. We also introduced an object transportation strategy combining walking and periodic hind leg lifting for soft object transportation. We validated our method on a dung beetle-like robot. Our results show that the robot can perform versatile locomotion and use its legs to transport hard and soft objects of various sizes (60%-70% of leg length) and weights (approximately 3%-115% of robot weight) on flat and uneven terrains. The study also suggests possible neural control mechanisms underlying the dung beetle Scarabaeus galenus' versatile locomotion and small dung pallet transportation.

Role of arm reaching movement kinematics in friction perception at initial contact with smooth surfaces.

When manipulating objects, humans begin adjusting their grip force to friction within 100ms of contact. During motor adaptation, subjects become aware of the slipperiness of touched surfaces. Previously, we have demonstrated that humans cannot perceive frictional differences when surfaces are brought in contact with an immobilised finger, but can do so when there is submillimeter lateral displacement or subjects actively make the contact movement. Similarly, in, we investigated how humans perceive friction in the absence of intentional exploratory sliding or rubbing movements, to mimic object manipulation interactions. We used a two-alternative forced-choice paradigm in which subjects had to reach and touch one surface followed by another, and then indicate which felt more slippery. Subjects correctly identified the more slippery surface in 87±8% of cases (mean±SD; n=12). Biomechanical analysis of finger pad skin displacement patterns revealed the presence of tiny (<1mm) localised slips, known to be sufficient to perceive frictional differences. We tested whether these skin movements arise as a result of natural hand reaching kinematics. The task was repeated with the introduction of a hand support, eliminating the hand reaching movement and minimising fingertip movement deviations from a straight path. As a result, our subjects' performance significantly declined (66±12% correct, mean±SD; n=12), suggesting that unrestricted reaching movement kinematics and factors such as physiological tremor, play a crucial role in enhancing or enabling friction perception upon initial contact. KEY POINTS: More slippery objects require a stronger grip to prevent them from slipping out of hands. Grip force adjustments to friction driven by tactile sensory signals are largely automatic and do not necessitate cognitive involvement; nevertheless, some associated awareness of grip surface slipperiness under such sensory conditions is present and helps to select a safe and appropriate movement plan. When gripping an object, tactile receptors provide frictional information without intentional rubbing or sliding fingers over the surface. However, we have discovered that submillimeter range lateral displacement might be required to enhance or enable friction sensing. The present study provides evidence that such small lateral movements causing localised partial slips arise and are an inherent part of natural reaching movement kinematics.

A Scoping Review on Mobile Health Technology for Assessment and Intervention of Upper Limb Motor Function in Children with Motor Impairments.

Upper limb (UL) motor dysfunctions impact residual movement in hands/shoulders and limit participation in play, sports, and leisure activities. Clinical and laboratory assessments of UL movement can be time-intensive, subjective, and/or require specialized equipment and may not optimally capture a child's motor abilities. The restrictions to in-person research experienced during the COVID-19 pandemic have inspired investigators to design inclusive at-home studies with child participants and their families. Relying on the ubiquity of mobile devices, mobile health (mHealth) applications offer solutions for various clinical and research problems. This scoping review article aimed to aggregate and synthesize existing research that used health technology and mHealth approaches to evaluate and assess the hand function and UL movement in children with UL motor impairment. A scoping review based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) model was conducted in March 2023 yielding 25 articles (0.32% of 7891 studies). Assessment characteristics included game or task-based tests (13/25, 52%), primarily for neurological disorders (e.g., autism spectrum disorder [ASD], dystonia, dysgraphia) or children with cerebral palsy (CP). Although several mHealth studies were conducted in the clinical environment (10/25, 40%), studies conducted at home or in nonclinical settings (15/25, 60%) reported acceptable and highly satisfactory to the patients as minimizing the potential risks in participation. Moreover, the remaining barriers to clinical translation included object manipulation on a touch screen, offline data analysis, real-world usability, and age-appropriate application design for the wider population. However, the results emphasize the exploration of mHealth over traditional approaches, enabling user-centered study design, family-oriented methods, and large-scale sampling in future research.

Context-I2W: Mapping Images to Context-Dependent Words for Accurate Zero-Shot Composed Image Retrieval

Different from the Composed Image Retrieval task that requires expensive labels for training task-specific models, Zero-Shot Composed Image Retrieval (ZS-CIR) involves diverse tasks with a broad range of visual content manipulation intent that could be related to domain, scene, object, and attribute. The key challenge for ZS-CIR tasks is to learn a more accurate image representation that has adaptive attention to the reference image for various manipulation descriptions. In this paper, we propose a novel context-dependent mapping network, named Context-I2W, for adaptively converting description-relevant Image information into a pseudo-word token composed of the description for accurate ZS-CIR. Specifically, an Intent View Selector first dynamically learns a rotation rule to map the identical image to a task-specific manipulation view. Then a Visual Target Extractor further captures local information covering the main targets in ZS-CIR tasks under the guidance of multiple learnable queries. The two complementary modules work together to map an image to a context-dependent pseudo-word token without extra supervision. Our model shows strong generalization ability on four ZS-CIR tasks, including domain conversion, object composition, object manipulation, and attribute manipulation. It obtains consistent and significant performance boosts ranging from 1.88% to 3.60% over the best methods and achieves new state-of-the-art results on ZS-CIR. Our code is available at https://anonymous.4open.science/r/Context-I2W-4224/.

Forecasting Bimanual Object Manipulation Sequences from Unimanual Observations

Learning to forecast bimanual object manipulation sequences from unimanual observations has broad applications in assistive robots and augmented reality. This challenging task requires us to first infer motion from the missing arm and the object it would have been manipulating were the person bimanual, then forecast the human and object motion while maintaining hand-object contact during manipulation. Previous attempts model the hand-object interactions only implicitly, and thus tend to produce unrealistic motion where the objects float in air. We address this with a novel neural network that (i) identifies and forecasts the pose for only the objects undergoing motion through an object motion module and (ii) refines human pose predictions by encouraging hand-object contact during manipulation through an ensemble of human pose predictors. The components are also designed to be generic enough for use in both unimanual and bimanual contexts. Our approach outperforms the state-of-the-art pose forecasting methods on bimanual manipulation datasets.

Hand-Centric Motion Refinement for 3D Hand-Object Interaction via Hierarchical Spatial-Temporal Modeling

Hands are the main medium when people interact with the world. Generating proper 3D motion for hand-object interaction is vital for applications such as virtual reality and robotics. Although grasp tracking or object manipulation synthesis can produce coarse hand motion, this kind of motion is inevitably noisy and full of jitter. To address this problem, we propose a data-driven method for coarse motion refinement. First, we design a hand-centric representation to describe the dynamic spatial-temporal relation between hands and objects. Compared to the object-centric representation, our hand-centric representation is straightforward and does not require an ambiguous projection process that converts object-based prediction into hand motion. Second, to capture the dynamic clues of hand-object interaction, we propose a new architecture that models the spatial and temporal structure in a hierarchical manner. Extensive experiments demonstrate that our method outperforms previous methods by a noticeable margin.

The influence of depth on object selection and manipulation in visual working memory within a 3D context.

Recent studies have examined whether the internal selection mechanism functions similarly for perception and visual working memory (VWM). However, the process of how we access and manipulate object representations distributed in a 3D space remains unclear. In this study, we utilized a memory search task to investigate the effect of depth on object selection and manipulation within VWM. The memory display consisted of colored items half positioned at the near depth plane and the other half at the far plane. During memory maintenance, the participants were instructed to search for a target representation and update its color. The results showed that under object-based attention (Experiments 1, 3, and 5), the update time was faster for targets at the near plane than for those at the far plane. This effect was absent in VWM when deploying spatial attention (Experiment 2) and in visual search regardless of the type of attention deployed (Experiment 4). The differential effects of depth on spatial and object-based attention in VWM suggest that spatial attention primarily relied on 2D location information irrespective of depth, whereas object-based attention seemed to prioritize memory representations at the front plane before shifting to the back. Our findings shed light on the interaction between depth perception and the selection mechanisms within VWM in a 3D context, emphasizing the importance of ordinal, rather than metric, spatial information in guiding object-based attention in VWM.

Estimation of Object Handover Position Using Human-Robot Proxemics and Unsupervised Pattern Recognition

The increasing elderly population presents significant challenges in terms of the meeting of their daily care needs. Cognitive decline and reduced arm reflexes following balance loss impede the elderly’s execution of activities of daily living. To address these challenges, robots have emerged as valuable assistants for elderly individuals in their daily activities, including object manipulation, and have the potential to significantly improve the quality of life for the aging population. However, no research has been undertaken to enhance the selection of object handover locations in human-robot interactions by merging topology mapping with both parties’ range of motion, based on personal space. Based on the idea of personal space within human-robot proxemics, this research presents an alternative approach that makes use of topological mapping while taking into account the range of motion of both humans and robots. This research aims to minimize the expenses related to human-robot proximity and to determine the best locations for item handovers in order to discover which locations are optimal. In order to improve object handover locations, this work is a groundbreaking attempt to combine growing neural gas and human proxemics inside a robotic framework. Furthermore, it implies the creation of robot behaviors that resemble human proximity by estimating personal distances and incorporating rule-based requirements for item handover locations by taking into account the mobility ranges of both humans and robots. The simulation findings reported in this work show the ability of the suggested methodology and offer interesting information and prospects for further developments in the area of object handovers by robots.