Robotics Research Research Articles

BEATRIX: An open source humanoid head platform for robotics teaching and research

This paper introduces BEATRIX, a novel robotic head designed to bridge the gap between theoretical knowledge and practical experience in the field of robotics at universities. The BEATRIX robot comprises a head actuated by a neck-like mechanism with three stepper motors, two cameras and two microphones for acquisition of visual and audio information from the environment. The robot can be connected to any external computer for the design and implementation of algorithms for applications in human–robot interaction. The proposed robotic platform has been used successfully with undergraduate and master students implementing tasks such as face detection and tracking, sound detection and tracking, robot control and graphical user interfaces. This paper includes lists of all the robot components, assembly instructions, and links to all CAD and software files, facilitating replication and further exploration. The robot design and integration of visual and audio sensors enables the development of engaging educational tutorials and robot experiments, enhancing the teaching and learning experience in robotics.

Soft robots and soft bodies: biological insights into the structure and function of fluidic soft robots

Over the last two decades, robotics engineering has witnessed rapid growth in the exploration and development of soft robots. Soft robots are made of deformable materials with mechanical properties or other features that resemble biological structures. These robots are often inspired by living organisms or mimic their locomotion, such as crawling and swimming. This paper aims to assist researchers in robotics and engineering to design soft robots incorporating or inspired by biological systems with a more informed perspective on biological models and functions. We address the characteristics of fluidic soft robots inspired by or mimicking biological examples, establish a method to categorize soft robots from a functional biological perspective, and provide a wider range of organisms to inspire the development of soft robotics. The actuation mechanisms in bioinspired and biomimetic soft robotics would benefit from a clearer understanding of the underlying principles, organization, and function of biological structures.

A comparative study to elucidate factors explaining willingness to use home-care robots in Japan, Ireland, and Finland.

The implementation of home-care robots is sometimes unsuccessful. This study aimed to explore factors explaining people's willingness to use home-care robots, particularly among care recipients and caregivers. Surveys were conducted in Japan, Ireland, and Finland. The survey questionnaire comprised four categories (familiarity with robots, important points about home-care robots, functions expected from home-care robots, and ethically acceptable uses), with 48 items assessing users' willingness to use home-care robots. The responses from 525 Japanese, 163 Irish, and 170 Finnish respondents were analyzed to identify common and distinct factors influencing their willingness to use these robots. Common factors across the countries included "willingness to participate in research and development," "interest in robot-related news,"and "having a positive impression of robots". The distinct factors for each country were: "convenience" in Japan; "notifying family members and support personnel when an unexpected change occurs in an older person" in Ireland; and "design" in Finland. Therefore, developers should determine potential users' willingness to participate in the research and development of home-care robots and consider a system that involves them in the development process.

Scene Representations for Robotic Spatial Perception

The ability of a robot to build a persistent, accurate, and actionable model of its surroundings through sensor data in a timely manner is crucial for autonomous operation. While representing the world as a point cloud might be sufficient for localization, denser scene representations are required for obstacle avoidance. On the other hand, higher-level semantic information is often crucial for breaking down the necessary steps to autonomously complete a complex task, such as cooking. So the looming question is, What is a suitable scene representation for the robotic task at hand? This survey provides a comprehensive review of key approaches and frameworks driving progress in the field of robotic spatial perception, with a particular focus on the historical evolution and current trends in representation. By categorizing scene modeling techniques into three main types—metric, metric–semantic, and metric–semantic–topological—we discuss how spatial perception frameworks are transitioning from building purely geometric models of the world to more advanced data structures incorporating higher-level concepts, such as the notion of object instances and places. Special emphasis is placed on approaches for real-time simultaneous localization and mapping, their integration with deep learning for enhanced robustness and scene understanding, and their ability to handle scene dynamicity as some of the hottest topics of interest driving robotics research today. We conclude with a discussion of ongoing challenges and future research directions in the quest to develop robust and scalable spatial perception systems suitable for long-term autonomy.

Preferences and Expectations for Home Robot Tasks: Comparison According to Age and Household Type in Republic of Korea

Most studies of consumer preferences and expectations for home robots focus on either older adults or single-person households (SPHs). However, with the rise in voluntary SPHs among young adults and seniors, it is critical to compare both age and household types in robot research. This study explored perceptions of home robots and willingness to use their features based on age and household type, in the context of the expanding home robot market in Republic of Korea. An online survey of 400 individuals was conducted, targeting young SPHs and multi-person households (MPHs) in their 20s and 30s as well as older SPHs and MPHs in their 50s and 60s. The survey covered four robot task categories, with 40 items derived from previous research: household chores (20 items), personal care (seven items), leisure/companion (nine items), and health (four items). The results helped predict the main target groups for each in-home robot task by identifying items that showed differences in responses between groups and interpreting these based on age, household type, and their combination. The study provides valuable data on consumer expectations, highlighting differences in responses according to both age and household type, offering insights for the robotics industry to effectively target their products.

Attention-based hand pose estimation with voting and dual modalities

Hand pose estimation has recently emerged as a compelling topic in the robotic research community, because of its usefulness in learning from human demonstration or safe human–robot interaction. Although deep learning-based methods have been introduced for this task and have shown promise, it remains a challenging problem. To address this, we propose a novel end-to-end architecture for hand pose estimation using red-green-blue (RGB) and depth (D) data (RGB-D). Our approach processes the two data sources separately and utilizes a dense fusion network with an attention module to extract discriminative features. The features extracted include both spatial information and geometric constraints, which are fused to vote for the hand pose. We demonstrate that our voting mechanism in conjunction with the attention mechanism is particularly useful for solving the problem, especially when hands are heavily occluded by objects or are self-occluded. Our experimental results on benchmark datasets demonstrate that our approach outperforms state-of-the-art methods by a significant margin.

Application of ant colony optimization algorithm based on farthest point optimization and multi-objective strategy in robot path planning

With the continuous development of high technology and the continuous progress of intelligent industry, mobile robots are gradually widely used in various fields. In the field of mobile robot research, path planning is crucial. However, the current ant colony optimization algorithm applied to mobile robot path planning still has some limitations, such as early blind search, slower convergence speed, and lower path smoothness. To overcome these problems, this paper proposes an ant colony optimization algorithm based on farthest point optimization and multi-objective strategy. The algorithm introduces new heuristic information such as the normal distribution model, triangle inequality principle, smoothness function, safety value function, etc. It adopts multi-objective comprehensive evaluation indexes to judge the quality of paths. For the high-quality and poor-quality paths, the algorithm takes additional pheromone increments and decrements in pheromone concentration to speed up the algorithm’s convergence. Besides, the farthest point optimization strategy is used to improve the quality of the paths further. Finally, to verify the algorithm’s effectiveness, the algorithm is compared with 20 existing methods for solving the robot path planning problem, and the experimental results show that the algorithm exhibits better results in terms of convergence, optimal path length, and smoothness. Specifically, the algorithm can produce the shortest path in four different environments while realizing the least number of turns with faster convergence, further proving the effectiveness of the improved algorithm in this paper.

Design and Control Co-Optimization for Dynamic Loco-Manipulation With a Robotic Arm on a Quadruped Robot

Abstract Research in quadrupedal robotics is transitioning to studies into loco-manipulation, featuring fully articulated robotic arms mounted atop these robots. Integrating such arms enhances the practical utility of legged robots, paving the way for expanded applications like industrial inspection and search and rescue. Existing literature commonly employs a six-degree-of-freedom (six-DoF) arm directly mounted to the robot, which inherently adds significant weight and reduces the available payload for manipulation tasks. Our study explores an optimized combination of arm configuration and control framework by strategically reducing the DoFs and leveraging the quadruped robot’s inherent agile mobility. We demonstrate that by minimizing the DoFs to just one, a range of canonical loco-manipulation tasks can still be accomplished. Some tasks even show improved performance with fewer robotic arm DoFs due to the higher torque motor used in the design, allowing more of the robot’s payload to be used for manipulation. We designed our optimized one-DoF robotic arm and the control framework and tested it on top of a Unitree Aliengo. Our design outperforms conventional six-DoF counterparts in lifting capacity, achieving an impressive 8 kg payload compared to the 2 kg maximum payload of industry-standard six-DoF robotic arms on the same quadruped platform.

Research Progress on Pole-Climbing Robots: A Review

Background: Pole-climbing robots are a type of application robot commonly used for quality inspection, cleaning, and maintenance of utility poles, high voltage lines, bridge cables, transmission pipelines, and other important facilities with a certain height. The working conditions are often harsh and dangerous, making manual participation unsafe. The pole-climbing robot can provide a platform for maintenance and cleaning work. Remote control operation of the pole-climbing robot is achieved through communication and control technology. It has been created to improve efficiency and reduce personnel accidents greatly. At the same time, the continuous development of science and technology has led to the expansion of robot design concepts. It has also brought about great changes and growth in the development of pole-climbing robots. Objective: The purpose of this paper is to report the latest progress in the research of pole-climbing robots from bionic and non-bionic perspectives and to provide a research reference for researchers in this field. Method: By analyzing academic theses and published patents, this paper presents a new classification of pole-climbing robots from the perspective of bionic and non-bionic. By summarizing the literature, the structural characteristics of various pole-climbing robots and their differences and applications are summarized. Results: The performance of the pole-climbing robots is analyzed from the viewpoint of structural characteristics and action execution methods. In turn, the characteristics of various types of pole-climbing robots are summarized. Finally, based on the above discussion, the future problems and development directions of pole-climbing robots are predicted. Conclusion: The pole-climbing robots can be divided into two categories, bionic and non-bionic, based on the analysis of design features. Both bionic and non-bionic pole-climbing robots have good development and applications in their respective directions. Also, bionic and non-bionic pole-climbing robots have different detailed classifications based on material, structure, and other perspectives. They have different advantages and disadvantages in terms of the performance of pole-climbing action execution. It provides a research reference for future researchers in this field.

Understanding the sense of self through robotics.

Robotics can play a useful role in the scientific understanding of the sense of self, both through the construction of embodied models of the self and through the use of robots as experimental probes to explore the human self. In both cases, the embodiment of the robot allows us to devise and test hypotheses about the nature of the self, with regard to its development, its manifestation in behavior, and the diversity of selves in humans, animals, and, potentially, machines. This paper reviews robotics research that addresses the topic of the self-the minimal self, the extended self, and disorders of the self-and highlights future directions and open challenges in understanding the self through constructing its components in artificial systems. An emerging view is that key phenomena of the self can be generated in robots with suitably configured sensor and actuator systems and a layered cognitive architecture involving networks of predictive models.

Rapid Prototyping of Multiscale Flexible Printed Circuit Boards with NeXus, a Robotic Additive Manufacturing System

Abstract This paper demonstrates the NeXus system, a multi-scale robotic additive manufacturing platform developed at the Louisville Automation and Robotics Research Institute (LARRI), as a rapid prototyping tool through additively manufacturing a multi-layer flexible printed circuit board (FPC) with a printed strain sensor and soldered Surface Mounted Devices (SMD). Manufacturing of the demonstrator requires the application and curing of multiple materials with specialized properties, tools for automated assembly, and software advances to streamline the process enabling the use of industry-standardized programs to command the NeXus system. Additive manufacturing processes supported by the NeXus include Aerosol Jet printing (AJP) for fine feature silver conducting lines, direct write ink-jet printing for insulating materials, and Intense Pulsed Light (IPL) for curing materials between depositions. The NeXus system transports and manipulates parts using a 5-DOF high-precision positioner. Solder paste deposition and pick-and-place (PnP) procedures are performed by a 4-DOF Selective Compliance Articulated Robot Arm (SCARA). Conversion methods between traditional PCB design software and production-ready command scripts were developed to translate basic drawings into command scripts. Multilayer structures with AJP 50-micron wide lines, an insulating bridge with a thickness of around 100 microns, and SMDs soldered to silver AJP pads, were integrated within the demonstrator. An operational amplifier, and other SMDs, reduce the complexity of the accompanying control circuit and amplifies the sensor?s response by 1,830 times. The successful fabrication of the demonstrator FPC highlights the rapid prototyping ability of the NeXus System.

A scoping review of robotic technology in hospitality and tourism

Purpose With the disruptive evolution of artificial intelligence (AI), the roles of robotics in hospitality and tourism are shifting rapidly from automatic to emotional works. Although academics have focused on examining robotic applications in hospitality and tourism, concrete images of the different roles of robots are unclear. Thus, this study aims to systematically aggregate and evaluate existing robotic technology-related studies published in Social Science Citation Index-listed hospitality and tourism journals to link the fragmented knowledge and provide an up-to-date overview of robotic technology in hospitality and tourism. Design/methodology/approach This study retrieved 134 robotic technology-related articles and used descriptive and content analyses to analyze the retrieved papers thoroughly. Findings The top keyword identified was service robot. Robotic technologies are categorized into AI-supplemented, AI-generated, robotic technology anthropomorphism (RTA)-facilitated and RTA-mediated. Research limitations/implications Future studies can consider exploring service robots further from the perspectives of suppliers in tourism. A more comprehensive categorization of robotic technologies is also recommended. Originality/value This study contributes to the robotic research realm by providing a holistic view of robotic applications in hospitality and tourism research. This study also attempts to pin down the potential research directions to guide researchers in expanding future studies.

Behavioral Cloning Strategies in Steering Angle Prediction: Applications in Mobile Robotics and Autonomous Driving

Artificial neural networks (ANNs) are artificial intelligence techniques that have made autonomous driving more efficient and accurate; however, autonomous driving faces ongoing challenges in the accuracy of decision making based on the analysis of the vehicle environment. A critical task of ANNs is steering angle prediction, which is essential for safe and effective navigation of mobile robots and autonomous vehicles. In this study, to optimize steering angle prediction, NVIDIA’s architecture was adapted and modified along with the implementation of the Swish activation function to train convolutional neural networks (CNNs) by behavioral cloning. The CNN used human driving data obtained from the UDACITY beta simulator and tests in real scenarios, achieving a significant improvement in the loss function during training, indicating a higher efficiency in replicating human driving behavior. The proposed neural network was validated through implementation on a differential drive mobile robot prototype, by means of a comparative analysis of trajectories in autonomous and manual driving modes. This work not only advances the accuracy of steering angle predictions but also provides valuable information for future research and applications in mobile robotics and autonomous driving. The performance results of the model trained with the proposed CNN show improved accuracy in various operational contexts.

Integral resonant negative derivative feedback suppression control strategy for nonlinear dynamic vibration behavior model

One of the major problems in robotics research has been developing an actuator system for extremely dynamic-legged robots. High torque density and the capacity to control dynamic physical interactions are two design requirements for high-speed locomotion that are challenging for conventional actuators used in manufacturing applications to meet. To address this system and apply the desired control to reach the best stability position, the robot's foot was simulated with the Van der Pol equations, applied the required control, and studied that application. This work describes the actions of a new novel control mechanism known as the Integral Resonant Negative Derivative Feedback (IRNDF) controller, which reduces the vibration response of a double Van der Pol oscillator subjected to external excitations. This unique controller combines integral resonant control (IRC) and negative derivative feedback (NDF) controllers to provide a new controller effect for double Van der Pol oscillators. The multiple scale perturbation technique (MSPT) has been applied to solve the controlled system analytically. The MATLAB and MAPLE programs have been used to complete and clarify all of the numerical talks. The frequency response curves have been used to study the impact that altering the parameter values had on the amplitude. The controlled system vibration amplitude is governed by frequency-response equations (FREs), which have been constructed. In the vibration system, the IRC, NDF, and IRNDF controllers were compared to see which one was the best. Numerical results show that the unique IRNDF controller is the best at reducing oscillations and decreasing amplitude values. The effects of the effective parameters on the controlled system have been identified. The frequency-response equation that was derived has been used to plot the various response curves for the framework that show the stable and unstable zones when the controller is off and on. Lastly, excellent agreement between the derived numerical findings and the analytical ones was observed. Lastly, utilizing time histories and response curves to compare analytical and numerical solutions was fascinating and significant.

Transforming science labs into automated factories of discovery.

Laboratories in chemistry, biochemistry, and materials science are at the leading edge of technology, discovering molecules and materials to unlock capabilities in energy, catalysis, biotechnology, sustainability, electronics, and more. Yet, most modern laboratories resemble factories from generations past, with a large reliance on humans manually performing synthesis and characterization tasks. Robotics and automation can enable scientific experiments to be conducted faster, more safely, more accurately, and with greater reproducibility, allowing scientists to tackle large societal problems in domains such as health and energy on a shorter timescale. We define five levels of laboratory automation, from laboratory assistance to full automation. We also introduce robotics research challenges that arise when increasing levels of automation and when increasing the generality of tasks within the laboratory. Robots are poised to transform science labs into automated factories of discovery that accelerate scientific progress.

State-of-the-Art Flocking Strategies for the Collective Motion of Multi-Robots

The technological revolution has transformed the area of labor with reference to automation and robotization in various domains. The employment of robots automates these disciplines, rendering beneficial impacts as robots are cost-effective, reliable, accurate, productive, flexible, and safe. Usually, single robots are deployed to accomplish specific tasks. The purpose of this study is to focus on the next step in robot research, collaborative multi-robot systems, through flocking control in particular, improving their self-adaptive and self-learning abilities. This review is conducted to gain extensive knowledge related to swarming, or cluster flocking. The evolution of flocking laws from inception is delineated, swarming/cluster flocking is conceptualized, and the flocking phenomenon in multi-robots is evaluated. The taxonomy of flocking control based on different schemes, structures, and strategies is presented. Flocking control based on traditional and trending approaches, as well as hybrid control paradigms, is observed to elevate the robustness and performance of multi-robot systems for collective motion. Opportunities for deploying robots with flocking control in various domains are also discussed. Some challenges are also explored, requiring future considerations. Finally, the flocking problem is defined and an abstraction of flocking control-based multiple UAVs is presented by leveraging the potentials of various methods. The significance of this review is to inspire academics and practitioners to adopt multi-robot systems with flocking control for swiftly performing tasks and saving energy.

Monitoring system for physical activity and fitness based on service robots and biomechanics

Exercise is one of the important ways for people to exercise, with characteristics such as sociality and strong participation. Especially with the improvement of the level of economic development and the improvement of the quality of life of the people, more and more people begin to attach importance to the maintenance of their own health. Physical fitness monitoring, as an effective means, is widely used in daily life, especially among the elderly. However, most of the existing monitoring methods are relatively simple, lacking pertinence, and the data collection process is relatively cumbersome and unstable, which cannot meet current needs. Therefore, it is very necessary to explore a new type of equipment that can more comprehensively and accurately monitor various physiological parameters of the human body to replace existing traditional detection technologies. Service Robots are currently the most promising intelligent hardware products. They mainly provide personalized services centered on users, sensing user behavior and implementing intelligent decision-making based on their characteristics, thereby better meeting the needs of different groups of people. This article focused on the research and development of Service Robots, and designed a comprehensive solution for Service Robots based on theories such as the Internet of Things, cloud computing, big data technology, and artificial intelligence. This article compared existing intelligent monitoring systems with fitness monitoring systems based on Service Robots, and proved that the user experience of fitness monitoring with robot participation has improved by about 4.68%. Its application scenarios were richer and its effects were more significant, enabling it to better complete tasks such as analysis and prediction of physical fitness status, real-time warning, etc., reducing the risk of people suffering from diseases, and enhancing individual protection awareness.

Utilization of the Resonance Behavior of a Tendon-Driven Continuum Joint for Periodic Natural Motions in Soft Robotics

Continuum joints use structural elastic deformations to enable joint motion, and their intrinsic compliance and inherent mechanical robustness are envisioned for applications in which the robot, the human, and the environment need to be safe during interaction. In particular, the intrinsic compliance makes continuum joints a competitor to soft articulated joints, which require additional integrated spring elements. For soft articulated joints incorporating rigid and soft parts, natural motions have been investigated in robotics research to exploit this energy-efficient motion property for cyclic motions, e.g., locomotion. To the best of the author’s knowledge, there is no robotic system to date that utilizes the natural motion of a continuum joint under periodic excitation. In this paper, the resonant behavior of a tendon-driven continuum joint under periodic excitation of the torsional axis is experimentally investigated in a functional sense. In the experiments, periodic inputs are introduced on the joint side of a tendon driven continuum joint with four tendons. By modulating the pretension of the tendons, both the resonant frequency and the gain can be shifted, from 3 to 4.3 Hz and 2.8 to 1.4, respectively, in the present experimental setup. An application would be the rotation of a humanoid torso, where gait frequencies are synchronized with the resonant frequency of the continuum joint.

Adaptive Deep Ant Colony Optimization–Asymmetric Strategy Network Twin Delayed Deep Deterministic Policy Gradient Algorithm: Path Planning for Mobile Robots in Dynamic Environments

Path planning is one of the main focal points and challenges in mobile robotics research. Traditional ant colony optimization (ACO) algorithms encounter issues such as low efficiency, slow convergence, and a tendency to become stuck in local optima and search stagnation when applied to complex dynamic environments. Addressing these challenges, this study introduces an adaptive deep ant colony optimization (ADACO) algorithm, which significantly improves efficiency and convergence speed through enhanced pheromone diffusion mechanisms and updating strategies, applied to global path planning. To adapt to dynamically changing environments and achieve more precise local path planning, an asymmetric strategy network TD3 algorithm (ATD3) is further proposed, which utilizes global path planning information within the strategy network only, creating a new hierarchical path planning algorithm—ADACO-ATD3. Simulation experiments demonstrate that the proposed algorithm significantly outperforms in terms of path length and number of iterations, effectively enhancing the mobile robot’s path planning performance in complex dynamic environments.

Bionic Multi-Legged Robots with Flexible Bodies: Design, Motion, and Control.

Bionic multi-legged robots with flexible bodies embody human ingenuity in imitating, learning, and exploring the natural world. In contrast to rigid-body robots, these robots with flexible bodies exhibit superior locomotive capabilities. The flexible body of the robot not only boosts the moving speed and walking stability but also enhances adaptability across complex terrains. This article focuses on the innovative design of flexible bodies. Firstly, the structural designs, including artificial spines and single/multi-axis articulation mechanisms, are outlined systematically. Secondly, the enhancement of robotic motion by flexible bodies is reviewed, examining the impact that body degrees of freedom, stiffness, and coordinated control between the body and limbs have on robotic motion. Thirdly, existing robotic control methods, organized by control architectures, are comprehensively overviewed in this article. Finally, the application prospects of bionic multi-legged robots with flexible bodies are offered, and the challenges that may arise in their future development are listed. This article aims to serve as a reference for bionic robot research.