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With the desired outcome of social good within the wider robotics ecosystem, trust is identified as the central adhesive of the human–robot interaction (HRI) interface. However, building trust between humans and robots involves more than improving the machine's technical reliability or trustworthiness in function. This paper presents a holistic, community-based approach to trust-building, where trust is understood as a multifaceted and multi-staged looped relation that depends heavily on context and human perceptions. Building on past literature that identifies dispositional and learned stages of trust, our proposed decision to trust model considers more extensively the human and situational factors influencing how trust manifests within social relations. Priority is given to the human user of technology—the initiator of human—robot trust relations—at all stages of decision-making. The envisioned formation of optimal conditions in which trust emerges requires the collective participation of practitioners, policymakers, and members of the community. With trust facilitating the smooth transition of robots into more socially embedded roles, positive receptivity of the best engineering project arises from the presence of harmonious robot-human trust relations in community spaces. [ABSTRACT FROM AUTHOR]

Picking robots have been widely used for citrus harvesting in recent years. However, the rest citrus during continuous picking can be irregularly disturbed by the wind, robot force, and the load weight of bearing branches under the natural environment. The citrus in the disturbed state cannot be rapidly and accurately detected, and then localized online, leading to the low efficiency of automatic robotic picking. In this study, online target detection and rapid localization were proposed using improved YOLOv5s+DeepSORT. The position of citrus at rest was predicted using the motion-tracking trajectory of disturbed citrus within a short period of time. The coordinates of the citrus were then obtained rapidly. Firstly, the CBAM (Convolutional Block Attention Module) attention mechanism was added to the YOLOv5s network, in order to detect the small and occluded targets. The SIoU loss function was used to enhance the direction matching between the prediction and the calibration frame, in order to improve the convergence speed of regression. Secondly, the target re-identification network was improved in the DeepSORT more suitable for the feature extraction of citrus targets. The feature extraction of the network was enhanced to improve the tracking performance on the disturbed citrus; The Count counter was used to accumulate the number of tracking frames in each citrus for an optimal target. Since the disturbance of the rest citrus was progressively propagated over time, the localization prediction and picking were only for targets with optimal tracking trajectories at a time. The real-time updating was realized in real time. Finally, the values of the depth camera were combined within the critical distance range, excluding the influence of background citrus on the detection speed. The number of tracking targets each time was limited to effectively improve the tracking speed of disturbed citrus. The experimental results show that the P (precision) and mAP (average detection accuracy) of improved YOLOv5s were improved by 3.9 and 1.1 percentage points, respectively, with a detection rate of 69.3 frames per second. The MOTA (Multi-Object Tracking Accuracy) and MOTP (Multi-Object Tracking Precision) of the improved DeepSORT were improved by 9.2 and 5.4 percentage points, respectively, whereas, the average number of ID (identity) switching times of targets was reduced by 32 times. Grasping experiments were conducted in the laboratory, in which the citrus was randomly swung along different orientations with an amplitude of about 10 cm. When the predicted localization time was 1, 2, 3, 5, 7, and 10 s, the average precision values of disturbed citrus localization were 21.3%, 53.0%, 81.9%, 83.7%, 86.1%, and 94.9%, respectively. The citrus picking test was conducted with the citrus localization time of 3 s. The average grabbing time for each citrus was 12.8 s, which was 5.6 s shorter than that without the optimization. The efficiency was improved by 30.4%. This finding can provide technical support and references for citrus picking in disturbed states. [ABSTRACT FROM AUTHOR]

Robotic grasping techniques for regular targets with known shapes are now well established. However, unknown shaped objects have complex features such as texture, shape, and appearance, which leads to inaccurate recognition and localization of shaped objects during grasp detection. To improve the generalization ability of the grasping detection network for unfamiliar shaped objects, we propose a lightweight shaped object grasping detection network (LSOGD) based on feature fusion, which solves the problem that the network repeatedly extracts features from images and ultimately improves the accuracy of model detection by combining different features. The effectiveness of LSOGD is confirmed by performance evaluation on the Cornell dataset and Jacquard dataset, where the detection accuracy reaches 97.9% and 96.7% for unknown objects, respectively. In addition, due to the small proportion of shaped objects in the current publicly available dataset, we added a portion of industrial-shaped pieces based on the selection of some shaped objects in the Cornell grasping dataset to build a shaped object dataset named X-Cornell on which the accuracy of our proposed model for grasping and detecting the unknown shaped objects is 94.6%. Finally, an actual robot grasping experiment was conducted using a Realsense d435i camera and a Kinova robotic arm, and the success rate of grasping shaped objects was 94%. [ABSTRACT FROM AUTHOR]

A controller is proposed to guide a multi-articulated robot vehicle (MARV) moving backwards, following a certain path. The ability to avoid fixed and moving obstacles is included, using the null space-based control technique to manage the conflicting tasks of following a path and avoiding obstacles. Additionally, control gains are modulated, thus reducing the risk of jackknifing. These new approaches are the contributions of the article. Results of laboratory-scale experiments with a MARV pushing one and two trailers are presented and discussed, which validate the proposed controller. Simulation results are also presented considering a MARV with three trailers, showing that the proposed controller can be adopted for larger articulated chains and another experiment that shows that it is possible to avoid moving obstacles. • Control of a multi-articulated robotic vehicle to follow a path when moving backwards. • Evasive maneuvers to avoid static or dynamic obstacles in the desired path. • Dynamic compensation to reduce the position errors for the rear of the last trailer. • Null space-based control approach hierarchic execution of complimentary subtasks. • Controller gain modulation as a tool to reduce the possibility of jackknife. [ABSTRACT FROM AUTHOR]

In teleoperation of redundant robotic manipulators, translating an operator's end effector motion command to joint space can be a tool for maintaining feasible and precise robot motion. Through optimizing redundancy resolution, the control system can ensure the end effector maintains maneuverability by avoiding joint limits and kinematic singularities. In autonomous motion planning, this optimization can be done over an entire trajectory to improve performance over local optimization. However, teleoperation involves a human-in-the-loop who determines the trajectory to be executed through a dynamic sequence of motion commands. We present two systems, Predictive Kinematic Control Tree and Predictive Kinematic Control Search, for utilizing a predictive model of operator commands to accomplish this redundancy resolution in a manner that considers future expected motion during teleoperation. Using a probabilistic model of operator commands allows optimization over an expected trajectory of future motion rather than consideration of local motion alone. Evaluation through a user study demonstrates improved control outcomes from this predictive redundancy resolution over minimum joint velocity solutions and inverse kinematics-based motion controllers. [ABSTRACT FROM AUTHOR]

Recent advances in path-planning algorithms have transformed robotics. The Rapidly exploring Random Tree (RRT) algorithm underpins autonomous robot navigation. This paper systematically examines the uses and development of RRT algorithms in single and multiple robots to demonstrate their importance in modern robotics studies. To do this, we have reviewed 70 works on RRT algorithms in single and multiple robot path planning from 2015 to 2023. RRT algorithm evolution, including crucial turning points and innovative techniques, have been examined. A detailed comparison of the RRT Algorithm versions reveals their merits, limitations, and development potential. The review's identification of developing regions and future research initiatives will enable roboticists to use RRT algorithms. This thorough review is essential to the robotics community, inspiring new ideas, helping problem-solving, and expediting single- and multi-robot system development. This highlights the necessity of RRT algorithms for autonomous and collaborative robotics advancement. [ABSTRACT FROM AUTHOR]

The integration of robots into social environments necessitates their ability to interpret human intentions and anticipate potential outcomes accurately. This capability is particularly crucial for social robots designed for human care, as they may encounter situations that pose significant risks to individuals, such as undetected obstacles in their path. These hazards must be identified and mitigated promptly to ensure human safety. This paper delves into the artificial theory of mind (ATM) approach to inferring and interpreting human intentions within human–robot interaction. We propose a novel algorithm that detects potentially hazardous situations for humans and selects appropriate robotic actions to eliminate these dangers in real time. Our methodology employs a simulation-based approach to ATM, incorporating a "like-me" policy to assign intentions and actions to human subjects. This strategy enables the robot to detect risks and act with a high success rate, even under time-constrained circumstances. The algorithm was seamlessly integrated into an existing robotics cognitive architecture, enhancing its social interaction and risk mitigation capabilities. To evaluate the robustness, precision, and real-time responsiveness of our implementation, we conducted a series of three experiments: (i) A fully simulated scenario to assess the algorithm's performance in a controlled environment; (ii) A human-in-the-loop hybrid configuration to test the system's adaptability to real-time human input; and (iii) A real-world scenario to validate the algorithm's effectiveness in practical applications. These experiments provided comprehensive insights into the algorithm's performance across various conditions, demonstrating its potential for improving the safety and efficacy of social robots in human care settings. Our findings contribute to the growing research on social robotics and artificial intelligence, offering a promising approach to enhancing human–robot interaction in potentially hazardous environments. Future work may explore the scalability of this algorithm to more complex scenarios and its integration with other advanced robotic systems. [ABSTRACT FROM AUTHOR]

This paper primarily investigates enhanced object detection techniques for indoor service mobile robots. Robot operating systems (ROS) supply rich sensor data, which boost the models' ability to generalize. However, the model's performance might be hindered by constraints in the processing power, memory capacity, and communication capabilities of robotic devices. To address these issues, this paper proposes an improved you only look once version 5 (YOLOv5)-Lite object detection algorithm based on efficient multi-scale attention and bounding box regression combined with ROS. The algorithm incorporates efficient multi-scale attention (EMA) into the traditional YOLOv5-Lite model and replaces the C3 module with a lightweight C3Ghost module to reduce computation and model size during the convolution process. To enhance bounding box localization accuracy, modified precision-defined intersection over union (MPDIoU) is employed to optimize the model, resulting in the ROS–YOLOv5–FleetEMA model. The results indicated that relative to the conventional YOLOv5-Lite model, the ROS–YOLOv5–FleetEMA model enhanced the mean average precision (mAP) by 2.7% post-training, reduced giga floating-point operations per second (GFLOPS) by 13.2%, and decreased the params by 15.1%. In light of these experimental findings, the model was incorporated into ROS, leading to the development of a ROS-based object detection platform that offers rapid and precise object detection capabilities. [ABSTRACT FROM AUTHOR]

Robots are now widely used in assembly tasks. However, when robots perform the automatic assembly of Multi-Pin Circular Connectors (MPCCs), the small diameter of the pins and the narrow gaps between them present significant challenges. During the assembly process, the robot's end effector can obstruct the view, and the contact between the pins and the corresponding holes is completely blocked, making this task more precise and challenging than the common peg-in-hole assembly. Therefore, this paper proposes a robotic assembly strategy for MPCCs that includes two main aspects: (1) we employ a vision-based Deep Siamese Neural Network (DSNN) model to address the most challenging peg-in-hole alignment problem in MPCC assembly. This method avoids the difficulties of modeling in traditional control strategies, the high training costs, and the low sample efficiency in reinforcement learning. (2) This paper constructs a complete practical assembly system for MPCCs, covering everything from gripping to final screwing. The experimental results consistently demonstrate that the assembly system integrated with the DSNN can effectively accomplish the MPCC assembly task. [ABSTRACT FROM AUTHOR]

We introduce a One-Shot Learning system where a robot effectively learns how to manipulate objects by relying solely on the object's name, a single image, and a visual example of a person picking it up. Once the robot has mastered picking up a new object, an audio command is all that is needed to prompt it to perform the action. Our approach heavily depends on synthetic data generation, which is crucial for training various detection and regression models. Additionally, we introduce a novel combined regression model called Cross-Validation Regression with Z-Score (CVR-ZS), which improves the robot's grasp accuracy. The system also features a classifier that uses a cutting-edge text-encoding technique, allowing for flexible user prompts for object retrieval. The complete system includes a text encoder and classifier, an object detector, and the CVR-ZS regressor. This setup has been validated with a Niryo Ned robot. [ABSTRACT FROM AUTHOR]

The operational environments in which a mobile robot executes its missions often exhibit nonflat terrain characteristics, encompassing outdoor and indoor settings featuring ramps and slopes. In such scenarios, the conventional methodologies employed for localization encounter novel challenges and limitations. This study delineates a localization framework incorporating ground elevation and incline considerations, deviating from traditional two‐dimensional localization paradigms that may falter in such contexts. In our proposed approach, the map encompasses elevation and spatial occupancy information, employing Gridmaps and Octomaps. At the same time, the perception model is designed to accommodate the robot's inclined orientation and the potential presence of ground as an obstacle, besides usual structural and dynamic obstacles. We provide an implementation of our approach fully working with Nav2, ready to replace the baseline Adaptative Monte Carlo Localization (AMCL) approach when the robot is in nonplanar environments. Our methodology was rigorously tested in both simulated environments and through practical application on actual robots, including the Tiago and Summit XL models, across various settings ranging from indoor and outdoor to flat and uneven terrains. Demonstrating exceptional precision, our approach yielded error margins below 10 cm and 0.05 radians in indoor settings and less than 1.0 m in extensive outdoor routes. While our results exhibit a slight improvement over AMCL in indoor environments, the enhancement in performance is significantly more pronounced when compared to three‐dimensional simultaneous localization and mapping algorithms. This underscores the considerable robustness and efficiency of our approach, positioning it as an effective strategy for mobile robots tasked with navigating expansive and intricate indoor/outdoor environments. [ABSTRACT FROM AUTHOR]

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Soft robotics, an emerging field that focuses on the development of robots utilizing soft, flexible, and deformable materials, is revolutionizing traditional robotics (reliant on rigid materials and motors) and broadening its range of applications and potential uses. In addition, by emulating the structure, function, and characteristics of biological systems, bioinspired materials are facilitating significant progress in a diverse array of soft robotic applications. This review offers an overview of bioinspired materials employed in soft robotics, exploring their potential applications, challenges, and future research directions. For an intuitive understanding, soft robots based on the primary abilities required and the habitats (terrestrial, aquatic, aerial) of the animals and plants they mimic are categorized. Furthermore, real‐world applications of developed soft robots in everyday human life are presented. The novel category classification and comprehensive analysis presented in this review provide insights into the development of soft robotic systems with the potential to transform various industries and enhance quality of life. [ABSTRACT FROM AUTHOR]

In engineering materials, surface anisotropy is known in certain textured patterns that appear during the manufacturing process. In biology, there are numerous examples of mechanical systems which combine anisotropic surfaces with the motion, elicited due to some actuation using muscles or stimuli-responsive materials, such as highly ordered cellulose fiber arrays of plant seeds. The systems supplemented by the muscles are rather fast actuators, because of the relatively high speed of muscle contraction, whereas the latter ones are very slow, because they generate actuation depending on the daily changes in the environmental air humidity. If the substrate has ordered surface profile, one can expect certain statistical order of potential trajectories (depending on the order of the spatial distribution of the surface asperities). If not, the expected trajectories can be statistically rather random. The same presumably holds true for the artificial miniature robots that use actuation in combination with frictional anisotropy. In order to prove this hypothesis, we developed numerical model helping us to study abovementioned cases of locomotion in 2D space on an uneven terrain. We show that at extremely long times, these systems tends to behave according to the rules of ballistic diffusion. Physically, it means that their motion tends to be associated with the "channels" of the patterned substrate. Such a motion is more or less the same as it should be in the uniform space. Such asymptotic behavior is specific for the motion in model regular potential and would be impossible on more realistic (and complex) fractal reliefs. However, one can expect that in any kind of the potential with certain symmetry (hexagonal or rhombic, for example), where it is still possible to find the ways, the motion along fixed direction during long (or even almost infinite) time intervals is possible. [ABSTRACT FROM AUTHOR]

Few mobile robot developers already test their software on simulated robots in virtual environments or sceneries. However, the majority still shy away from simulation-based test campaigns because it remains challenging to specify and execute suitable testing scenarios, that is, models of the environment and the robots' tasks. Through developer interviews, we identified that managing the enormous variability of testing scenarios is a major barrier to the application of simulation-based testing in robotics. Furthermore, traditional CAD or 3Dmodelling tools such as SolidWorks, 3ds Max, or Blender are not suitable for specifying sceneries that vary significantly and serve different testing objectives. For some testing campaigns, it is required that the scenery replicates the dynamic (e.g., opening doors) and static features of real-world environments, whereas for others, simplified scenery is sufficient. Similarly, the task and mission specifications used for simulation-based testing range from simple point-to-point navigation tasks to more elaborate tasks that require advanced deliberation and decision-making. We propose the concept of composable and executable scenarios and associated tooling to support developers in specifying, reusing, and executing scenarios for the simulation-based testing of robotic systems. Our approach differs from traditional approaches in that it offers a means of creating scenarios that allow the addition of new semantics (e.g., dynamic elements such as doors or varying task specifications) to existing models without altering them. Thus, we can systematically construct richer scenarios that remain manageable. We evaluated our approach in a small simulation-based testing campaign, with scenarios defined around the navigation stack of a mobile robot. The scenarios gradually increased in complexity, composing new features into the scenery of previous scenarios. Our evaluation demonstrated how our approach can facilitate the reuse of models and revealed the presence of errors in the configuration of the publicly available navigation stack of our SUT, which had gone unnoticed despite its frequent use. [ABSTRACT FROM AUTHOR]

The expression of robot arm morphology is a critical foundation for achieving effective motion planning and collision avoidance in robotic systems. Traditional geometry-based approaches usually suffer from the contradiction between the high demand for computing resources for fine expression and the insufficient detail expression caused by the pursuit of efficiency. The signed distance function addresses these drawbacks due to its ability to handle complex and arbitrary shapes and lower computational requirements. However, conventional robotic morphology methods based on the signed distance function often face challenges when the robot moves dynamically, since robots with different postures are modeled as independent individuals but the postures of robots are infinite. In this paper, we introduce RobotSDF, an implicit morphology modeling approach that can express the robot shape of arbitrary posture precisely. Instead of depicting a whole model of the robot arm, RobotSDF models the robot morphology as integrated implicit joint models driven by joint configurations. In this approach, the dynamic shape change process of the robot is converted into the coordinate transformations of query points within each joint's coordinate system. Experimental results with the Elfin robot demonstrate that RobotSDF can accurately depict robot shapes across different postures up to the millimeter level, which exhibits 38.65 % and 66.24 % improvement over the Neural-JSDF and configuration space distance field algorithms, respectively, in representing robot morphology. We further verified the efficiency of RobotSDF through collision avoidance in both simulation and actual human–robot collaboration experiments. [ABSTRACT FROM AUTHOR]

The role of robotic systems in human assistance is inevitable with the bots that assist with interactive and voice commands. For cooperative and precise assistance, the understandability of these bots needs better input analysis. This article introduces a Comparable Input Assessment Technique (CIAT) to improve the bot system's understandability. This research introduces a novel approach for HRI that uses optimized algorithms for input detection, analysis, and response generation in conjunction with advanced neural classifiers. This approach employs deep learning models to enhance the accuracy of input identification and processing efficiency, in contrast to previous approaches that often depended on conventional detection techniques and basic analytical methods. Regardless of the input type, this technique defines cooperative control for assistance from previous histories. The inputs are cooperatively validated for the instruction responses for human assistance through defined classifications. For this purpose, a neural classifier is used; the maximum possibilities for assistance using self-detected instructions are recommended for the user. The neural classifier is divided into two categories according to its maximum comparable limits: precise instruction and least assessment inputs. For this purpose, the robot system is trained using previous histories and new assistance activities. The learning process performs comparable validations between detected and unrecognizable inputs with a classification that reduces understandability errors. Therefore, the proposed technique was found to reduce response time by 6.81%, improve input detection by 8.73%, and provide assistance by 12.23% under varying inputs. [ABSTRACT FROM AUTHOR]

Regular exercise plays a crucial role in promoting overall well-being in today's lifestyle. However, individuals often find it challenging to properly execute exercises, including maintaining correct postures and appropriate movement speeds. Robotic companions have emerged as potential solutions to assist and motivate users during exercise sessions. This research paper proposes a novel robot companion designed for exercise scenarios using a reconfigurable robot. In contrast to existing non-reconfigurable robotic companions, the use of a reconfigurable robot provides added flexibility in generating emotions. The system incorporates a module that utilizes fuzzy logic to evaluate the correctness of exercise performance based on posture variations and movement speeds. The robot generates emotions and provides feedback to users based on the exercise correctness score. The robot expresses emotions through reconfigurations, motion patterns, and variations in robot speed. This emotion-based feedback could be helpful for creating engaging and interactive exercise experiences. Apart from emotion generation, the robot utilizes vocal cues as feedback. Experimental results validate the effectiveness of the proposed system in evaluating exercise correctness and demonstrating meaningful emotion transitions. The findings of this work contribute to the development of innovative robotic companions for improving exercise adherence and overall well-being. [ABSTRACT FROM AUTHOR]

Domestic service robots (DSRs) that support people in everyday environments have been widely investigated. However, their ability to predict and describe future risks resulting from their own actions remains insufficient. In this study, we focus on the linguistic explainability of DSRs. Most existing methods do not explicitly model the region of possible collisions; thus, they do not properly generate descriptions of these regions. In this paper, we propose the Nearest Neighbor Future Captioning Model that introduces the Nearest Neighbor Language Model for future captioning of possible collisions, which enhances the model output with a nearest neighbors retrieval mechanism. Furthermore, we introduce the Collision Attention Module that attends regions of possible collisions, which enables our model to generate descriptions that adequately reflect the objects associated with possible collisions. To validate our method, we constructed a new dataset containing samples of collisions that can occur when a DSR places an object in a simulation environment. The experimental results demonstrated that our method outperformed baseline methods, based on the standard metrics. In particular, on CIDEr-D, the baseline method obtained 25.09 points, whereas our method obtained 33.08 points. [ABSTRACT FROM AUTHOR]

Magnetic miniature robotic systems have attracted broad research interest because of their precise maneuverability in confined spaces and adaptability to diverse environments, holding significant promise for applications in both industrial infrastructures and biomedical fields. However, the predominant construction methodology involves the preprogramming of magnetic components into the system's structure. While this approach allows for intricate shape transformations, it exhibits limited flexibility in terms of reconfiguration and presents challenges when adapting to diverse materials, combining, and decoupling multiple functionalities. Here, we propose a construction strategy that facilitates the on-demand assembly of magnetic components, integrating ferrofluid droplets with the system's structural body. This approach enables the creation of complex solid-droplet robotic systems across a spectrum of length scales, ranging from 0.8 mm to 1.5 cm. It offers a diverse selection of materials and structural configurations, akin to assembling components like building blocks, thus allowing for the seamless integration of various functionalities. Moreover, it incorporates decoupling mechanisms to enable selective control over multiple functions, leveraging the fluidity, fission/fusion, and magneto-responsiveness properties inherent in the ferrofluid. Various solid-droplet systems have validated the feasibility of this strategy. This study advances the complexity and functionality achievable in small-scale magnetic robots, augmenting their potential for future biomedical and other applications. [ABSTRACT FROM AUTHOR]

Background: Preclinical technical feasibility study of robot-assisted microinvasive glaucoma surgery using a novel ophthalmic robot-assisted surgery system. Methods: Feasibility was assessed in synthetic eye models in two stages: Stage I, nonimplantable robot-assisted goniotomy; and Stage II, robot-assisted stent implantation using a trabecular bypass stent. Robot-assisted interventions were subsequently compared to the manual approach. Results: Stage I: Two surgeons completed 10 trials each of ab-interno sectoral goniotomy with and without robotic assistance for at least 3 clock hours using a standard goniotomy knife and more than 10 clock hours of extended goniotomy using a flexible, guided goniotomy instrument. Stage II: Trabecular bypass stent deployment was successfully achieved in 100% of the attempts with and without robotic assistance. Surgical time was recorded and compared between the robotic-assisted and the manual approach. Conclusions: A system for robot-assisted microinvasive glaucoma surgery can successfully achieve implantable and nonimplantable interventions in the anterior segment. This is the first known demonstration of the feasibility of robot-assisted glaucoma surgery. [ABSTRACT FROM AUTHOR]

The research work presents a novel voxel-type soft amphibious robot based on an assembly of origami flexiballs. The geometric and elastic constitutive models of the origami flexiball are theoretically established to elucidate its intricate deformation mechanism. Especially, the zero-energy storage phenomenon and the quasi-zero-stiffness characteristic are revealed to prove that the origami flexiball is suitable for serving as soft robotic components. As a proof of concept, fourteen origami flexiballs are interconnected to form a quadruped robot capable of walking or crawling in both underwater and terrestrial environments, including flat surfaces and sandy terrain. Its adaptability across multiple environments is enhanced by the origami polyhedra-inspired hollow structure, which naturally adjusts to underwater conditions such as hydrostatic pressure and currents, improving stability and performance. Other advantages of the voxel-type soft amphibious quadruped robot include its ease of manufacture using 3D printing with accessible soft elastic materials, ensuring rapid and cost-effective fabrication. We anticipate its potentially versatile applications, including underwater pipeline inspections, offshore maintenance, seabed exploration, ecological monitoring, and marine sample collection. By leveraging metamaterial features embodied in the origami polyhedra, the presented voxel-type soft robot exemplifies an innovative approach to achieving complex functionalities in soft robotics. [ABSTRACT FROM AUTHOR]

Incremental advancements in technology present researchers with opportunities to examine and predict human behavior before the integration of technology into daily life. Previous studies have identified trends in both the design and reception of current social robotic technologies, including gender biases and social "othering", which may affect how humans interact with more advanced robotic technologies in the future. The aim of the current study was to explore whether preconceived beliefs about gender inequality, interest in casual sex, and social hierarchies would relate individuals' interest in engaging in platonic friendships ("robofriendship") or sexual relationships ("robosexuality") with hypothetical human-like robots. Two-hundred and twelve participants completed an online survey measuring gender, ambivalent sexism, social dominance orientation, and sociosexual orientation in relation to individuals' interest in both robofriendship and robosexuality. It was found that hostile sexism positively predicted interest in robosexuality, particularly for men (β =.16, b =.27, 95% CI [.03,.30], t (209) = 2.364, p =.019). Conversely, hostile sexism negatively predicted robofriendship, and significant interactions effects were found in that at lower levels of SDO, women maintained greater interest in robofriendship than men (β =.26, b =.54, 95% CI [.09,.99], t(208) = −2.235, p =.02). The current study provides preliminary evidence to suggest that preconceived beliefs about social hierarchy and gender inequality may impact romantic and platonic interactions between humans and robots. Limitations and future directions are also discussed. [ABSTRACT FROM AUTHOR]

Collaborative robots are increasingly recognized as potential assistants to relieve workers from repetitive and physically demanding tasks on construction jobsites. Despite the great potential, most efforts have focused on developing various artificial intelligence (AI) and robotic technologies to achieve specific human–robot collaboration (HRC) functions. However, there is a significant lack of research regarding the impacts of such collaboration on construction work performance and workers' perception and acceptance of collaborative robots, which could be a critical influence factor on the feasibility and effectiveness of HRC on construction jobsites. To this end, this study aims to evaluate the multidimensional impacts of collaborative robots on work efficiency, quality, and workload as well as workers' perception and acceptance. HRC experiments on sample construction tasks (i.e., wood assembly) were conducted in conjunction with quantitative measurements and subject surveys. Through comparison between HRC experiments and human–human collaboration (HHC) experiments based on this case study, it was found that HRC could improve up to 29.3% and 88.6% in work efficiency and assembly accuracy, respectively, and reduce workers' workload by up to 20.3%. Furthermore, workers' perception of HRC is found to be positive overall with higher acceptance after HRC experience, characterized by questionnaires designed based on the technology acceptance model. Through physical experiments, this research is expected to produce more reliable results compared with conventional approaches where participants are simply provided with imaginary scenarios. The findings will also guide the development of robotic technologies to enhance the practical application of HRC in construction. [ABSTRACT FROM AUTHOR]

The state reconstruction problem for a remote robotic system with communication delay is investigated in this paper. A symmetric estimation scheme is proposed based on both input and output observers. First, a sliding-mode disturbance observer is designed to estimate the input torque of the robot on the remote side. Then, by using the received output and torque estimation subject to time delay, a novel predictive observer is proposed on the local side to reconstruct the real-time joint angles and velocities. Based on a Lyapunov approach, sufficient conditions are obtained to make sure that the estimation errors can converge exponentially to bounded regions through selecting proper observer gains. Simulation studies are conducted to verify the effectiveness of the proposed strategy. The estimation error of state reconstruction is decreased by 14% in terms of the integral square error when compared to the standard high-gain predictor, and the simulation results demonstrate the effectiveness of force estimation under disturbances and model uncertainties. [ABSTRACT FROM AUTHOR]

This study presents a robot-assisted solution for the automated identification of bridge frequencies and high-spatial-resolution mode shapes using a minimal number of sensors. The proposed approach employs programmable wheeled robots, whose movement can be remotely controlled, as the mobile platform carrying accelerometers. The output-only frequency domain decomposition (FDD) algorithm is adopted for use with the proposed stop-and-go mobile sensing scheme, resulting in the identification of frequencies and high-resolution mode shapes. The solution was verified via two numerical case studies and was validated on a full-scale test of a footbridge. The results reveal that the frequencies and high-resolution shapes of the excited structural modes are identified successfully using only two accelerometers, confirming the satisfactory practicality and efficiency of the proposed solution. [ABSTRACT FROM AUTHOR]

People meeting a robot for the first time do not know what it is capable of and therefore how to interact with it—what actions to produce, and how to produce them. Despite social robotics' long-standing interest in the effects of robots' appearance and conduct on users, and efforts to identify factors likely to improve human–robot interaction, little attention has been paid to how participants evaluate their robotic partner in the unfolding of actual interactions. This paper draws from qualitative analyses of video-recorded interactions between a robot and groups of participants, in the framework of ethnomethodology and conversation analysis. We analyse the particular ways in which participants shape their embodied actions, how they can reproduce a prior action that failed to obtain a response from the robot; and how they explore the robot's embodied nature. We find a set of recurrent methods or practices, showing that robot-recipient design displays not only participants' initial assumptions about the robot's competences, but also more importantly perhaps their continuous assessment of the robot's behaviour, and their attempts to adapt to it. Participants locally produce and constantly revise their understanding of the robot as a more or less competent co-participant, drawing from its past, current, and projected conduct and responsiveness. We discuss the implications of these findings for research in robotics and human–robot interactions, and the value of the approach to shed new light on old questions by paying attention to the quality of gesture and the sequential organisation of interaction. [ABSTRACT FROM AUTHOR]

This paper introduces a novel resource-efficient control structure for remote path-following control of autonomous vehicles based on a comprehensive combination of Kalman filtering, non-uniform dual-rate sampling, periodic event-triggered communication, and prediction-based and packet-based control techniques. An essential component of the control solution is a non-uniform dual-rate extended Kalman filter (NUDREKF), which includes an h-step ahead prediction stage. The prediction error of the NUDREKF is ensured to be exponentially mean-square bounded. The algorithmic implementation of the filter is straightforward and triggered by periodic event conditions. The main goal of the approach is to achieve efficient usage of resources in a wireless networked control system (WNCS), while maintaining satisfactory path-following behavior for the vehicle (a holonomic Mecanum-wheeled robot). The proposal is additionally capable of coping with typical drawbacks of WNCS such as time-varying delays, and packet dropouts and disorder. A Simscape Multibody simulation application reveals reductions of up to 93% in resource usage compared to a nominal time-triggered control solution. The simulation results are experimentally validated in the holonomic Mecanum-wheeled robotic platform. • A novel, comprehensive periodic event-triggered networked control solution is proposed. • High reduction of resource usage (of up to 93%) can be reached while maintaining acceptable control performance. • A non-uniform dual-rate extended Kalman filter (NUDREKF) is essential in the control structure. • Some conditions are provided such that the prediction error of the NUDREKF is exponentially mean square bounded. • Real path-following control tests for a holonomic Mecanum-wheeled robot validate the control solution. [ABSTRACT FROM AUTHOR]

For nearly three decades, researchers have explored the use of augmented reality for facilitating collaboration between humans and robots. In this survey paper, we review the prominent, relevant literature published since 2008, the last date that a similar review article was published. We begin with a look at the various forms of the augmented reality (AR) technology itself, as utilized for human–robot collaboration (HRC). We then highlight specific application areas of AR for HRC, as well as the main technological contributions of the literature. Next, we present commonly used methods of evaluation with suggestions for implementation. We end with a look towards future research directions for this burgeoning field. This review serves as a primer and comprehensive reference for those whose work involves the combination of augmented reality with any kind of human–robot collaboration. [ABSTRACT FROM AUTHOR]

The problem of the insufficient accuracy performance of industrial robots in high-precision manufacturing is addressed in this paper. Firstly, a kinematic error model based on an M-DH model was presented. Secondly, a hybrid observability index O6 was proposed to select the optimal poses for parameter identification. O6 is the combination of O1 and O3. The optimal poses were obtained by using the IOOPS algorithm. Thirdly, the fitness function for parameter identification was established, and the Levenberg–Marquardt (LM) algorithm was applied for the accurate identification of kinematic parameter errors. Finally, several experiments were conducted to evaluate the performance of the proposed hybrid observability index O6. The average position error and average attitude error of Staubli TX60 robot were reduced by 89% and 49%. The results show that the proposed hybrid observability index O6 has great stability and effectiveness for robot calibration. [ABSTRACT FROM AUTHOR]

Autonomous navigation in dynamic environments is a significant challenge in robotics. The primary goals are to ensure smooth and safe movement. This study introduces a control strategy based on an Adaptive Neuro-Fuzzy Inference System (ANFIS). It enhances autonomous robot navigation in dynamic environments with a focus on collision-free path planning. The strategy uses a path-planning technique to develop a trajectory that allows the robot to navigate smoothly while avoiding both static and dynamic obstacles. The developed control system incorporates four ANFIS controllers: two are tasked with guiding the robot toward its end point, and the other two are activated for obstacle avoidance. The experimental setup conducted in CoppeliaSim involves a mobile robot equipped with ultrasonic sensors navigating in an environment with static and dynamic obstacles. Simulation experiments are conducted to demonstrate the model's capability in ensuring collision-free navigation, employing a path-planning algorithm to ascertain the shortest route to the target destination. The simulation results highlight the superiority of the ANFIS-based approach over conventional methods, particularly in terms of computational efficiency and navigational smoothness. [ABSTRACT FROM AUTHOR]

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This study investigates the potential of a voice-controlled collaborative robot to promote computational thinking (CT) skills in preschoolers aged 4 to 6 in Southern Chile. During a week-long workshop, 34 children engaged in activities designed to develop several CT components while interacting with a robotic arm. Pre- and post-workshop assessments utilizing the TechCheck-K tool revealed significant improvements in CT skills. The findings of this preliminary study demonstrate the effectiveness of voice-controlled robots as innovative tools in early education, significantly enhancing young learners' understanding of CT concepts, practices, and perspectives. This research advocates for further exploration of robotic integration in educational settings, highlighting their potential to improve or extend preschool learning environments and foster CT skill development. [ABSTRACT FROM AUTHOR]

The rising of e-commerce demands an increase in the performance of warehousing systems, which are being redesigned to deal with a mass volume of demands to be fulfilled as quickly as possible. The manual and robotic mobile fulfilment systems (RMFS) are the most commonly used for these activities. The former is a human-centred system that handles complex operations that current robots cannot perform. However, newer generations of autonomous robots are leading to a gradual replacement by the latter to increase productivity. Regardless of the system used, several interdependent problems must be solved for efficient storage and picking processes. Storage problems concern decisions on where to store products within the warehouse. Batching and picking problems include batching orders to be fulfilled, and the routes the pickers and robots should follow to retrieve the products demanded. This paper identifies the most relevant problems and the state-of-the-art research on these topics. We study the interactions among many of these interrelated problems and provide a critical analysis of the literature. Finally, eight research directions are identified and discussed. [ABSTRACT FROM AUTHOR]

The study presents an integration of cob with robotic processes. By challenging conventional monolithic earth-building methods, the study proposes the use of spatial nonplanar formations that are robotically fabricated, presenting an alternative geometric language for earth construction. The research methodology is derived from existing factors within the robotic lab, encompassing both constant and variable parameters. Through an experimental approach, the variables are systematically manipulated while observing the outcomes to identify patterns and relationships. Incremental refinements to the research conditions result in an optimal equilibrium state within the defined lab parameters. An empirical investigation approach serves as the foundation for controlling the printing process; wherein an iterative adjustment of the robot extrusion parameters is based on the behaviour of the deposited material. The outcome is several robotically printed cob nonplanar prototypes. Depending on their geometric formations and complexity, the printing process combined three variations: continuous, intervals, and modular. The latter enabled the production of a cob arch, serving as proof of feasibility for the creation of modular cob structures through a segmented assembly process. The study contributes to expanding the possibilities of cob construction by leveraging robotic technologies and paving the way for innovative applications of cob in contemporary architecture practices. [ABSTRACT FROM AUTHOR]

The availability of inspection robots in the construction and operation phases of buildings has led to expanding the scope of applications and increasing technological challenges. Furthermore, the building information modeling (BIM)-based approach for robotic inspection is expected to improve the inspection process as the BIM models contain accurate geometry and relevant information at different phases of the lifecycle of a building. Several studies have used BIM for navigation purposes. Also, some studies focused on developing a knowledge-based ontology to perform activities in a robotic environment (e.g., CRAM). However, the research in this area is still limited and fragmented, and there is a need to develop an integrated ontology to be used as a first step towards logic-based inspection. This paper aims to develop an ontology for BIM-based robotic navigation and inspection tasks (OBRNIT). This ontology can help system engineers involved in developing robotic inspection systems by identifying the different concepts and relationships between robotic inspection and navigation tasks based on BIM information. The developed ontology covers four main types of concepts: (1) robot concepts, (2) building concepts, (3) navigation task concepts, and (4) inspection task concepts. The ontology is developed using Protégé. The following steps are taken to reach the objectives: (1) the available literature is reviewed to identify the concepts, (2) the steps for developing OBRNIT are identified, (3) the basic components of the ontology are developed, and (4) the evaluation process is performed for the developed ontology. The semantic representation of OBRNIT was evaluated through a case study and a survey. The evaluation confirms that OBRNIT covers the domain's concepts and relationships, and can be applied to develop robotic inspection systems. In a case study conducted in a building at Concordia University, OBRNIT was used to support an inspection robot in navigating to identify a ceiling leakage. Survey results from 33 experts indicate that 28.13% strongly agreed and 65.63% agreed on the usage of OBRNIT for the development of robotic navigation and inspection systems. This highlights its potential in enhancing inspection reliability and repeatability, addressing the complexity of interactions within the inspection environment, and supporting the development of more autonomous and efficient robotic inspection systems. [ABSTRACT FROM AUTHOR]

In robotic manipulation, achieving efficient and reliable grasping in cluttered environments remains a significant challenge. This study presents a novel approach that integrates pushing and grasping actions using deep reinforcement learning. The proposed model employs two fully convolutional neural networks—Push-Net and Grasp-Net—that predict pixel-wise Q-values for potential pushing and grasping actions from heightmap images of the scene. The training process utilizes deep Q-learning with a reward structure that incentivizes both successful pushes and grasps, encouraging the robot to create favorable conditions for grasping through strategic pushing actions. Simulation results demonstrate that the proposed model significantly outperforms traditional grasp-only policies, achieving an 87% grasp success rate in cluttered environments, compared to 60% for grasp-only approaches. The model shows robust performance in various challenging scenarios, including well-ordered configurations and novel objects, with completion rates of up to 100% and grasp success rates as high as 95.8%. These findings highlight the model's ability to generalize to unseen objects and configurations, making it a practical solution for real-world robotic manipulation tasks. [ABSTRACT FROM AUTHOR]

With the development of robotics and vision measurement technology, the use of robots with line laser scanners for 3D scanning and measurement of parts has become a mainstream trend in the field of industrial inspection. Traditional scanning and measuring robots mainly use the teach-in scanning method, which has unstable scanning quality and low scanning efficiency. In this paper, the adaptive sampling method for a free-form surface, which can realize the adaptive distribution of surface measurement points according to the curvature features of free-form surfaces, is proposed first. Then, integrated with the proposed adaptive sampling method, the automatic path planning method is proposed. This method consists of adaptive sampling, scanning attitude calculation based on a quaternion, scanning viewpoint planning based on viewable cones, and scan path generation based on bi-directional scanning. Based on the proposed automatic path planning method, the scanning and measuring robot can obtain complete 3D information of the surface to be measured with high measurement accuracy and efficiency. The performance index of the laser scanner can be fully reached. [ABSTRACT FROM AUTHOR]

Segmenting unseen object instances in unstructured environments is an important skill for robots to perform grasping-related tasks, where the trade-off between efficiency and accuracy is an urgent challenge to be solved. In this work, we propose a fast unseen object instance segmentation (Fast UOIS) method that utilizes predicted center offsets of objects to compute the positions of local maxima and minima, which are then used for selecting initial seed points required by the mean-shift clustering algorithm. This clustering algorithm that adaptively generates seed points can quickly and accurately obtain instance masks of unseen objects. Accordingly, Fast UOIS first generates pixel-wise predictions of object classes and center offsets from synthetic depth images. Then, these predictions are used by the clustering algorithm to calculate initial seed points and to find possible object instances. Finally, the depth information corresponding to the filtered instance masks is fed into the grasp generation network to generate grasp poses. Benchmark experiments show that our method can be well transferred to the real world and can quickly generate sharp and accurate instance masks. Furthermore, we demonstrate that our method is capable of segmenting instance masks of unseen objects for robotic grasping. [ABSTRACT FROM AUTHOR]

As robot numbers in the home increase, creating a market for second-hand robotic systems is essential to reduce the waste impact of the industry. Via a survey, consumer attitudes of United Kingdom participants towards secondhand robots were investigated; finding that second-hand robots with guarantees have an equal purchasing interest compared to new systems, highlighting the opportunity for manufacturers and retailers to develop certification standards for second-hand robots to move towards a circular economy. Consumer demographics also demonstrated that those most open to the purchase of both new and second-hand systems were women, those aged 18-25 years old, and those who have previously owned a robot for the home. Participants' prior ownership of second-hand electronic devices (such as phones and laptops) did not affect rates of interest for second-hand robotic systems suggesting that the technology is still too new for people to be able to project their experience of current second-hand electronics to that of a robot. Additionally, this research found the robotics industry can consider the potential market for second-hand robots to be more similar to the second-hand smartphone market than to the household electronics market, and lessons learnt from the concerns raised by consumers for other internet-enabled electronic devices are similar to those concerns for second-hand robots. This provides an opportunity for the industry to break down the barriers for a circular economy earlier in the technology maturity process than has been seen for other electronics. [ABSTRACT FROM AUTHOR]

The growing market for lightweight robots inspires new use-cases, such as collaborative manipulators for human-centered automation. However, widespread adoption faces obstacles due to high R&D costs and longer design cycles, although rapid advances in mechatronic engineering have effectively narrowed the design space to affordable robot components, turning the development of lightweight robots into a component selection and integration challenge. Recognizing this transformation, we demonstrate a practical framework for designing lightweight industrial manipulators using a case-study of indigenously developed 5 Degrees-of-Freedom (DOF) cobot prototype. Our framework incorporates off-the-shelf sensors, actuators, gears, and links for Design for Manufacturing and Assembly (DFMA), along with complete virtual prototyping. The design cycle time is reduced by approximately 40% at the cost of cobot real-time performance deviating within 2.5% of the target metric. Our physical prototype, having repeatability of 0.05mm calculated as per the procedure defined in ISO 9283:1998, validates the cost-effective nature of the framework for creating lightweight manipulators, benefiting robotic startups, R&D organizations, and educational institutes without access to expensive in-house fabrication setups. [ABSTRACT FROM AUTHOR]

In recent years, the integration of polarimetric imaging into robotic perception systems has increased significantly, driven by the accessibility of affordable polarimetric sensors. This technology complements traditional color imaging by capturing and analyzing the polarization characteristics of light. This additional information provides robots with valuable insights into object shape, material composition, and other properties, ultimately enabling more robust manipulation tasks. This review aims to provide a comprehensive analysis of the principles behind polarimetric imaging and its diverse applications within the field of robotic perception. By exploiting the polarization state of light, polarimetric imaging offers promising solutions to three key challenges in robot vision: Surface segmentation; depth estimation through polarization patterns; and 3D reconstruction using polarimetric data. This review emphasizes the practical value of polarimetric imaging in robotics by demonstrating its effectiveness in addressing real-world challenges. We then explore potential applications of this technology not only within the core robotics field but also in related areas. Through a comparative analysis, our goal is to elucidate the strengths and limitations of polarimetric imaging techniques. This analysis will contribute to a deeper understanding of its broad applicability across various domains within and beyond robotics. [ABSTRACT FROM AUTHOR]

Soft robots have recently attracted increasing interest due to their advantages in durability, flexibility, and deformability, which enable them to adapt to unstructured environments and perform various complex tasks. Perception is crucial for soft robots. To better mimic biological systems, sensors need to be integrated into soft robotic systems to obtain both proprioceptive and external perception for effective usage. This review summarizes the latest advancements in flexible sensing feedback technologies for soft robotic applications. It begins with an introduction to the development of various flexible sensors for soft robots, followed by an in‐depth exploration of smart materials and advanced manufacturing methods. A detailed description of flexible sensing modalities and methodologies is also included in the review to illustrate the continuous breakthrough of the technology. In addition, the applications of soft robots based on these advanced sensing technologies are concluded as well. The challenges of flexible sensing technologies for soft robots and promising solutions are finally discussed and analyzed to provide a prospect for future development. By examining the recent advances in intelligent flexible sensing technologies, this review is dedicated to highlighting the potential of soft robotics and motivating innovation within the field. [ABSTRACT FROM AUTHOR]

Collaborative robotics is a major topic in current robotics research, posing new challenges, especially in human–robot interaction. The main aspect in this area of research focuses on understanding the behavior of robots when engaging with humans, where reinforcement learning is a key discipline that allows us to explore sophisticated emerging reactions. This review aims to delve into the relevance of different sensors and techniques, with special attention to EEG (electroencephalography data on brain activity) and its influence on the behavior of robots interacting with humans. In addition, mechanisms available to mitigate potential risks during the experimentation process such as virtual reality are also be addressed. In the final part of the paper, future lines of research combining the areas of collaborative robotics, reinforcement learning, virtual reality, and human factors are explored, as this last aspect is vital to ensuring safe and effective human–robot interactions. [ABSTRACT FROM AUTHOR]

This paper investigates varying the operating conditions of a neural network in a robotic system using a low-cost webcam to achieve optimal settings in order to detect crossed-recess screws on laptops, a necessary step in the realization of automated disassembly systems. A study was performed that varied the lighting conditions, velocity, and number of passes the robot made over the laptop, as well as the network size of a YOLO-v5 neural network. The analysis reveals that specific combinations of operating parameters and neural network configurations can significantly improve detection accuracy. Specifically, the best results for the majority of laptops were obtained when the system ran at medium velocity (10 and 15 mm/s), with a light, and the neural network was run with an extra large network. Additionally, the results show that screw characteristics like the screw hole depth, the presence of a taper in the screw hole, screw hole location, and the color difference between the laptop cover and the screw color impact the system's overall detection rate, with the most important factor being the depth of the screw. [ABSTRACT FROM AUTHOR]

AbstractAs robotic technology advances, robots are increasingly being designed and utilized for applications involving children across various fields. Since the acceptance of robots is profoundly affected by their appearance, understanding children’s preferences for the looks of robots becomes imperative. The uncanny valley effect (UVE) posits that the perceived likeability of robots increases as their appearance becomes more humanlike but then sharply drops when they approach near-perfect human similarity before rising once an extremely realistic likeness is achieved. Due to inconsistent results in the literature regarding when humans develop a sense of uncanniness toward humanlike robots, the present study aims to examine the emergence of the UVE in children. 67 1st-graders (aged six to seven years) and 95 4th-graders (aged nine to ten years) evaluated 12 robot faces on the scales of likeability, disgust, humanness, and intention to play with the robots (i.e., acceptance). The results demonstrated that the UVE was not observed in 1st-graders or 4th-grade boys. Nevertheless, a UVE-like pattern emerged among 4th-grade girls, particularly in their ratings of likeability and acceptance as a function of robots’ degree of humanness. A positive correlation between likeability and acceptance was consistently found across all age and gender groups. Additionally, negative correlations were found between likeability and disgust, as well as disgust and acceptance, specifically in the fourth graders but not in the first graders. These results suggest that the UVE may not be fully developed in 10-year-old children, with girls potentially exhibiting earlier development than boys. We conclude that age and gender differences should be considered when evaluating children’s preferences for robot appearance and their willingness to interact with robots. [ABSTRACT FROM AUTHOR]

Introduction: Robotics uptake in the aerospace industry is low, mainly due to the low-volume/high-accuracy production that aerospace manufacturers require. Furthermore, aerospace manufacturing and assembly sites are often unstructured environments not specifically suitable for robots to operate in. Methods: This paper introduces a robotic visual inspection system using off-the-shelf components able to inspect the mounting holes for wing slat actuators without the need for fixed-coordinate programming; the part just needs to be left within reach of the robot. Our system sets one of the opposed pairs of mounting holes as a reference (the "datum") and then compares the tilt of all other pairs of mounting holes with respect to it. Under the assumption that any deviation in the mounting hole tilt is not systematic but due to normal manufacturing tolerances, our system will either guarantee the correct alignment of all mounting holes or highlight the existence of misaligned holes. Results and Discussion: Computer-vision tilt measurements are performed with an error of below 0.03° using custom optimization for the sub-pixel determination of the center and radius of the mounting holes. The error introduced by the robot's motion from the datum to each of the remaining hole pairs is compensated by moving back to the datum and fixing the orientation again before moving to inspect the next hole pair. This error is estimated to be approximately 0.05°, taking the total tilt error estimation for any mounting hole pair to be 0.08° with respect to the datum. This is confirmed by manually measuring the tilt of the hole pairs using a clock gauge on a calibrated table (not used during normal operation). [ABSTRACT FROM AUTHOR]

Digital representation of robots as Avatars, called "RoboAvatars," is a premise for value-added construction applications such as simulation, layout design, and task planning. Existing RoboAvatars are described in data schemas predominantly from the robotics community, which prevents their smooth applications in the built environment. To fully unleash the power of robotics, this research aims to develop a Building RoboAvatar by adopting the industry foundation classes (IFC) as the de facto standard in the building industry. First, the Building RoboAvatar is defined from a built environment perspective, and then substantiated with IFC. A translator called RoboIFCTrans is developed to facilitate the exploitation of the numerous readily available RoboAvatars represented by the Unified Robot Description Format. Experiments demonstrated the effectiveness of Building RoboAvatar in representing robot information needed for the built environment, which encompasses the "whole-part" robot structure and properties in terms of productivity, capability, etc. The RoboIFCTrans can accurately generate IFC representations of diverse robots (TurtleBot, UR-5, Diablo) within 41.9 s. Practical implications of the IFC-based Building RoboAvatars were illustrated by two use cases. The research contributes to building a "Tower of Babel" between the construction and robotics communities. The source code is made publicly open, in the hope of encouraging future research to explore more exciting opportunities (e.g., robot-oriented design, digital twin) enabled by the Building RoboAvatar. [ABSTRACT FROM AUTHOR]