Robotics Research Research Articles

Design, modeling, and experiment of a multi-degree-of-freedom pneumatic soft bionic actuator

Drawing on the driving mechanism of biological muscles and combining the nonlinear hyperelastic characteristics of silicone rubber, a multi-degree-of-freedom pneumatic soft bionic actuator is designed, which can be used as the executing mechanism for soft robots and robotic arms. Using response surface analysis and numerical simulation algorithms, the optimal combination of structural dimensions parameters is determined with the maximum bending and torsion angles output by the actuator as the optimization objectives. Based on the idea of flexible mechanism Piecewise Constant Curvature (PCC) modeling, the kinematics equation of the actuator is derived. Analyze the equivalent motion structure of the actuator and establish a dynamic model of the actuator based on the Lagrange method. The physical model of the actuator is manufactured using rapid prototyping technology. Finally, experimental testing and analysis are conducted on the physical model to obtain the motion and dynamic characteristics curves and empirical formulas of the actuator, which are compared with theoretical and simulation results to verify that the pneumatic soft bionic actuator is feasible and effective. The above research methods, processes, and results can provide reference and inspiration for the research and implementation of pneumatic and hydraulic soft robots and gripping actuators for soft robotic arms.

Research Review and Future Directions of Key Technologies for Welding Robots in the Construction Industry

The rapid development of the construction industry has highlighted the urgent need for enhanced construction efficiency and safety, propelling the development of construction robots to ensure sustainable and intelligent industry advancement. Welding robots, in particular, hold significant promise for application in steel structure construction. However, harsh construction environments, variable construction structures, and complex construction conditions present critical technical challenges for weld position, path, and quality for welding robots. This paper aims to provide a focused review of the key technical difficulties faced by welding robots in the construction industry, starting from the progress in research and applications. The review identifies the current state of welding robot technology, the technical bottlenecks encountered, and the potential solutions to these challenges, offering valuable insights for the research and development of construction robots.

Ethics and responsibility in biohybrid robotics research

The industrial revolution of the 19th century marked the onset of an era of machines and robots that transformed societies. Since the beginning of the 21st century, a new generation of robots envisions similar societal transformation. These robots are biohybrid: part living and part engineered. They may self-assemble and emerge from complex interactions between living cells. While this new era of living robots presents unprecedented opportunities for positive societal impact, it also poses a host of ethical challenges. A systematic, nuanced examination of these ethical issues is of paramount importance to guide the evolution of this nascent field. Multidisciplinary fields face the challenge that inertia around collective action to address ethical boundaries may result in unexpected consequences for researchers and societies alike. In this Perspective, we i) clarify the ethical challenges associated with biohybrid robotics, ii) discuss the need for and elements of a potential governance framework tailored to this technology; and iii) propose tangible steps toward ethical compliance and policy formation in the field of biohybrid robotics.

Southern creative robotics – A collaborative framework to implement computational design, fabrication and robotics

Scientific knowledge, ideally neutral and impartial, is inevitably shaped by geographical, economic, and cultural contexts. This research contends that overcoming the constraints of human and economic scarcity, inertia, and limited funding access demands development of collaborative research networks. To this end, four university laboratories from Brazil and Chile - UFRJ, UBB, UTFSM and UAI - have united to advance robotics applied to architecture. The methodology begins with an analysis of Industry 4.0, Fab Lab implementations, and robotics in architecture in the region. They identify key research aspects by mapping each laboratory’s activities and technologies to pinpoint expertise and potential collaborative areas. The authors propose a summary table comparing the labs and a chronological overview to track regional robotics advancement. To raise awareness among peers, the initiative involves joint actions such as courses, workshops and technical visits. 1 Recognising the scarcity of robotic units and the inapplicability of procedures from wealthier contexts, the authors draw on their lab experience to propose guidelines for implementing robotics research units in academia within the region. This includes technological alternatives, installation considerations, and detailed configuration reviews. The deployment of additional robotic units is a means to foster collaboration and bolster the network. Anticipated outcomes encompass increased critical mass, collaborative research initiatives, faculty and student exchange across institutions, higher publication rates, knowledge acquisition, and improved access to global funding agencies. In summary, the Southern Creative Robotics network initiative aims to catalyse the creative integration of digital manufacturing and robotics technology within design in the Ibero-American context.

Enhancing Robot Behavior with EEG, Reinforcement Learning and Beyond: A Review of Techniques in Collaborative Robotics

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.

The need for reproducible research in soft robotics

The need for reproducible research in soft robotics

Automatic Parkinson’s Disease Diagnosis with Wearable Sensor Technology for Medical Robot

The clinical diagnosis of Parkinson’s disease (PD) has been the subject of medical robotics research. Currently, a hot research topic is how to accurately assess the severity of Parkinson’s disease patients and enable medical robots to better assist patients in the rehabilitation process. The walking task on the Unified Parkinson’s Disease Rating Scale (UPDRS) is a well-established diagnostic criterion for PD patients. However, the clinical diagnosis of PD is determined based on the clinical experience of neurologists, which is subjective and inaccurate. Therefore, in this study, an automated diagnostic method for PD based on an improved multiclass support vector machine (MCSVM) is proposed in which wearable sensors are used. Kinematic analysis was performed to extract gait features, both spatiotemporal and kinematic, from the installed IMU and pressure sensors. Comparison experiments of three different kernel functions and linear trajectory experiments were designed. The experimental results show that the accuracies of the three kernel functions of the proposed improved MCSVM are 92.43%, 93.45%, and 95.35%. The simulation trajectories of the MCSVM are the closest to the real trajectories, which shows that the technique performs better in the clinical diagnosis of PD.

A-star algorithm vs. Q-learning algorithm for path planning: A comparative analysis

In robotics research, the core focus is on path planning, as it's essential for robots to navigate according to human objectives to be valuable. This paper provides an in-depth analysis of the commonly used A-Star algorithm and Q-learning algorithm in mobile robot path planning. It specifically examines the advantages and disadvantages of both the A-Star algorithm and the Q-learning algorithm, highlighting their roles in the advancement of robotic intelligence. The A-star algorithm is an algorithm with a heuristic function to guide path selection direction. Also, the A-star algorithm is guaranteed to identify the most efficient path in terms of traffic cost when facing static obstacles. However, its performance degrades when dealing with trap nodes. Specifically, the algorithm occupies a large amount of memory and takes a long computational time. In contrast, the Q-learning algorithm is effective for learning continuous actions with the same network data structure for local path planning tasks.

Real-time deep learning-based model predictive control of a 3-DOF biped robot leg

Our research utilized deep learning to enhance the control of a 3 Degrees of Freedom biped robot leg. We created a dynamic model based on a detailed joint angles and actuator torques dataset. This model was then integrated into a Model Predictive Control (MPC) framework, allowing for precise trajectory tracking without the need for traditional analytical dynamic models. By incorporating specific constraints within the MPC, we met operational and safety standards. The experimental results demonstrate the effectiveness of deep learning models in improving robotic control, leading to precise trajectory tracking and suggesting potential for further integration of deep learning into robotic system control. This approach not only outperforms traditional control methods in accuracy and efficiency but also opens the way for new research in robotics, highlighting the potential of utilizing deep learning models in predictive control techniques.

Robotic Surgery in Gynecologic Oncology—A Bibliometric Study

Study ObjectiveTo characterize robotic surgery publications in gynecologic oncology, and to identify factors associated with high citation metrics. DesignA cross-sectional study SettingOriginal articles on robotic surgery in gynecologic oncology. PatientsNo patients involved. InterventionsRobotic surgeries in gynecologic oncology. Measurements and Main ResultsWe performed PubMed Medical Subject Headings search for original articles on robotic surgery in gynecologic oncology. We analyzed citation scores and income level of country of publication, as well as factors associated with high citation metrics. Overall, 566 studies during 2005 to 2023 were included. Of those 292, 51.6% were from North America, and 182 32.2% from Europe. The leading tumor site studied was endometrial cancer (57.4%). The majority (87.6%) of studies were retrospective and 13 (2.3%) were randomized controlled trials. Most studies (94.2%) originated in high-income countries. Articles from middle-income countries had lower citations per year as compared to high-income countries (median 1.6 vs 2.5, p =.002) and were published in lower-impact factor journals (median 2.6 vs 4.3, p < .001) when compared with high-income countries. Cervical cancer studies had higher representation in middle-income countries than in high-income countries (48.5% vs 18.4%, p < .001). In a multivariable regression analysis, journal's impact factor [aOR 95% CI 1.26 (1.12–1.40)], cervical cancer topic [aOR 95% CI 3.0 (1.58–5.91)], and North American publications [aOR 95% CI 2.07 (1.08–3.97)] were independently associated with higher number of citations per year. ConclusionThe majority of robotic surgery research in gynecologic oncology is retrospective and from high-income countries. Middle-income countries are not as frequently cited and are predominantly in lower-impact factor journals.

Position control of a soft pneumatic actuator based on the pressure parameter feedback model (PPFM)

Abstract. Soft pneumatic actuators have been one of the cores of soft robotics research and play a key role in driving the development of soft robots. Due to its high degree of internal nonlinearity and unpredictable deformation caused by environmental influences, the control model established for soft robots is still a difficult problem in terms of improving accuracy. This paper proposes a new positional control method for soft pneumatic actuators that are suitable for independent 3D deformation at any position and are the core units of continuous robots. The pressure parameter feedback model (PPFM) of the airbag is obtained by adjusting the pressure input through a proportional valve, collecting the air pressure inside the airbag and obtaining the airbag expansion height. The pressure input signal is changed according to the PPFM of the airbag to control the position of the soft pneumatic actuator. A modular experimental platform is built to validate the PPFM-based control strategy, which is able to adjust the position of the end center point of the soft pneumatic actuator in space with the discussed characteristics. It is demonstrated that the theoretical model can significantly improve the stability and accuracy of the soft pneumatic actuator motion.

Enhancing Robotic Grasping Performance through Data-Driven Analysis

In automation, reliability in robotic grasping in dynamic environment is still a problem encountered. Further, there is the need to consider deep learning methods, as traditional approaches are not easily flexible in dealing with different objects and situations. In this work, we aim to analyze how well deep neural network models perform in predicting grasp strength based on data collected from the Smart Grasping Sandbox simulation trials. Hence, the proposed approach for analyzing the joint positions, velocities and efforts led to the design of a deep neural network for improving robot grasp performance. The question here could is if deep learning could help better predict grasp stability. To implement the method, we underwent rigorous data pre-processing such as outlier detection, and feature normalization and employed a structured neural network model for training. Our model got a training accuracy of nearly 99% and the test accuracy of nearly 96% demonstrating significant promise. These results were also better than CNN and LSTM where their accuracy rate was 94.12% and 91.81%, respectively. High deep learning performance was proven in robotic grasping, which can contribute to the creation of more flexible and sophisticated robotic platforms. This work also provides the foundation for subsequent research in robotics and automation, with emphasis on the role of data-driven methods in robotic grasping tasks.

Dynamic Modeling and Analysis of Flexible-Joint Robots with Clearance.

The coupling effects of flexible joints and clearance on the dynamics of a robotic system were investigated. A numerical analysis was undertaken to reveal the coupling effects between flexible joints and clearance. The nonlinear spring-damping model and Coulomb model were applied to characterize the contact characteristics of the clearance, and a model for the flexible joint was formulated using the equivalent spring theory. An accurate robot model was established based on the clearance and joint flexibility characterization. The dynamic equation of a robot was obtained according to the Newton-Euler method. A comparative analysis was performed to assess the impacts of both the joint action of clearance and flexible joints and varying joint clearance values on the performance of the robot. The results showed that the coupling effects of flexible joints and clearance had a negative impact on the system dynamic performance. The amplitudes of the dynamic responses caused by the clearance are weakened by the flexible joint, but it leads to the lag of the system response. This study served as the theoretical foundation for exploring precise control techniques in robotics research.

Development of a Premium Tea-Picking Robot Incorporating Deep Learning and Computer Vision for Leaf Detection

Premium tea holds a significant place in Chinese tea culture, enjoying immense popularity among domestic consumers and an esteemed reputation in the international market, thereby significantly impacting the Chinese economy. To tackle challenges associated with the labor-intensive and inefficient manual picking process of premium tea, and to elevate the competitiveness of the premium tea sector, our research team has developed and rigorously tested a premium tea-picking robot that harnesses deep learning and computer vision for precise leaf recognition. This innovative technology has been patented by the China National Intellectual Property Administration (ZL202111236676.7). In our study, we constructed a deep-learning model that, through comprehensive data training, enabled the robot to accurately recognize tea buds. By integrating computer vision techniques, we achieved exact positioning of the tea buds. From a hardware perspective, we employed a high-performance robotic arm to ensure stable and efficient picking operations even in complex environments. During the experimental phase, we conducted detailed validations on the practical application of the YOLOv8 algorithm in tea bud identification. When compared to the YOLOv5 algorithm, YOLOv8 exhibited superior accuracy and reliability. Furthermore, we performed comprehensive testing on the path planning for the picking robotic arm, evaluating various algorithms to determine the most effective path planning approach for the picking process. Ultimately, we conducted field tests to assess the robot’s performance. The results indicated a 62.02% success rate for the entire picking process of the premium tea-picking robot, with an average picking time of approximately 1.86 s per qualified tea bud. This study provides a solid foundation for further research, development, and deployment of premium tea-picking robots, serving as a valuable reference for the design of other crop-picking robots as well.

When Trustworthiness Meets Face: Facial Design for Social Robots.

As a technical application in artificial intelligence, a social robot is one of the branches of robotic studies that emphasizes socially communicating and interacting with human beings. Although both robot and behavior research have realized the significance of social robot design for its market success and related emotional benefit to users, the specific design of the eye and mouth shape of a social robot in eliciting trustworthiness has received only limited attention. In order to address this research gap, our study conducted a 2 (eye shape) × 3 (mouth shape) full factorial between-subject experiment. A total of 211 participants were recruited and randomly assigned to the six scenarios in the study. After exposure to the stimuli, perceived trustworthiness and robot attitude were measured accordingly. The results showed that round eyes (vs. narrow eyes) and an upturned-shape mouth or neutral mouth (vs. downturned-shape mouth) for social robots could significantly improve people's trustworthiness and attitude towards social robots. The effect of eye and mouth shape on robot attitude are all mediated by the perceived trustworthiness. Trustworthy human facial features could be applied to the robot's face, eliciting a similar trustworthiness perception and attitude. In addition to empirical contributions to HRI, this finding could shed light on the design practice for a trustworthy-looking social robot.

Cooperative path planning study of distributed multi-mobile robots based on optimised ACO algorithm

The rapid development of robotics technology has driven the growth of robot types and the development of related technologies. As an important aspect of robot research, path planning technology plays an irreplaceable role in practical production and application. Ant colony algorithm has a wide range of applications in robot path planning, but there is also a problem of performance overly relying on initial parameter selection. In order to solve this problem and improve the performance of mobile robot path planning, an improved ant colony algorithm based on firefly algorithm was studied and designed in a two-dimensional environment. In order to further explore the performance of ant colony algorithm in solving robot coordinated path planning problems, an improved ant colony algorithm based on heuristic function was also designed. In a three-dimensional environment, an improved ant colony algorithm based on the improved artificial potential field method was designed. The research results show that the maximum running time of the improved ant colony algorithm based on the firefly algorithm in different grid environments is 819.36 s, 847.01 s, and 811.54 s, respectively. The average running time of the improved ant colony algorithm based on heuristic function in different grid environments is 5.19 s, 5.97 s, and 9.09 s, with average path lengths of 29.90 cm, 31.08 cm, and 37.01 cm, and path length variances of 0.35, 0.87, and 2.21, respectively. The ant colony algorithm based on the improved artificial potential field method has a running time of 1.930 s, 3.182 s, and 4.662 s in different grid environments, and a path length of 29.275 cm, 49.447 cm, and 67.057 cm, respectively. The ant colony algorithm for research and design optimization has good performance. The contribution of the research lies in the design of three path planning methods for mobile robots, including two-dimensional path planning and three-dimensional path planning, which improves the time of path planning and shortens the average path length. The novelty of the research is reflected in the design of a path planning method for mobile robots in two-dimensional and three-dimensional environments, which improves the ant colony algorithm through firefly algorithm and heuristic function, and combines the ant colony algorithm with the improved artificial potential field method. The method designed by the research institute can provide technical support for path planning of mobile robots.

Autonomous navigation using machine vision and self-designed fiducial marker in a commercial chicken farming house

Indoor navigation within commercial animal farming houses is crucial for the increasing operations carried out by mobile robots. However, existing methods often suffer from issues such as sensitivity to ambient light, insufficient accuracy for precise animal operations, or complexity in implementation. In this study, we proposed a novel navigation method for mobile chassis in a commercial stacked cage farming house, utilizing an active light source, self-designed fiducial marker, and machine vision. Our navigation system incorporated imaging modules equipped with 850 nm infrared light to minimize animal stress and mitigate the effects of low illumination. The self-designed fiducial marker, DRITag, enabled precise maker identification with a recognition rate of 100 % and achieved high positioning accuracy, with an absolute mean error of 0.34 cm within a range of 1.2 m. To determine the chassis’ yaw angle, we employed the U-net deep learning network, which achieved satisfactory root mean square errors of 0.22° and 0.10° on the ground and upper floors, respectively. Global localization of the chassis across different farm areas was achieved through a combination of DRITag and odometry information. Our proposed method provided a flexible and user-friendly approach to navigation path planning, employing a waypoint task text file that enabled easy modification of the path through simple text file editing. Field tests conducted in a two-story commercial stacked cage chicken farming house demonstrated the high accuracy of our navigation method, with standard deviations of longitudinal deviations below 1.85 cm and 1.82 cm, and standard deviations of lateral deviations below 1.50 cm and 1.12 cm at a speed of below 0.3 m/s on the ground floor and at a speed of 0.2 m/s on the upper floor, respectively. Overall, our study presented an independent-of-ambient-light, highly accurate, and easily implementable navigation method for mobile chassis in commercial chicken farming houses, with significant potential for future research in mobile robotics within animal farming environments.

Real-time local path planning strategy based on deep distributional reinforcement learning

Local path planning and obstacle avoidance in complex environments are two challenging problems in the research of intelligent robots. In this study, we develop a novel approach grounded in deep distributional reinforcement learning to address these challenges. Within this methodology, agents instantiated by deep neural networks perceive real-time local environmental information through sensor data, addressing inherent stochasticity and local path planning tasks in complex environments. End-to-end training is facilitated via distributional reinforcement learning algorithms and reward functions informed by heuristic knowledge. Optimal actions for path planning are determined through return value distributions. Finally, the simulation results show that the success rate of the proposed distributed algorithm is 98% in a random environment and 94% in a dynamic environment. This proves that the algorithm has better generalization and flexibility than the non-distributed algorithm.

Effects of Demonstrating Consensus Between Robots to Change User’s Opinion

In recent years, the research of humanoid robots that can change users’ opinions has been conducted extensively. In particular, two robots have been found to be able to improve their persuasiveness by cooperating with each other in a sophisticated manner. Previous studies have evaluated the changes in opinions when robots showed consensus building. However, users did not participate in the conversations, and the optimal strategy may change depending on their prior opinions. Therefore, in this study, we developed a system that adaptively changes conversations between robots based on user opinions. We investigate the effect on the change in opinions when the discussion converges to the same position as the user and when it converges to a different position. We conducted two subject experiments in which a user and virtual robotic agents talked to each other using buttons in a crowded setting. The results showed that users with confidence in their opinions increased their confidence when the robot agents’ opinions converged to the same position and decreased their confidence when the robot agents’ opinions converged to a different position. This will significantly contribute to persuasion research using multiple robots and the development of advanced dialogue coordination between robots.

Trends and Knowledge Structure in Collaborative Robot Research in the Knowledge Economy Era: A Bibliometric Analysis

Trends and Knowledge Structure in Collaborative Robot Research in the Knowledge Economy Era: A Bibliometric Analysis