Field Of Robotics Research Articles

The Integration of Artificial Intelligence in Architectural Visualization Enhances Augmented Realism and Interactivity

The construction industry is an important part of the national economic market of various countries; since 2013, the construction industry's added value in the gross domestic product has been more than 6%, reaching 6.89% in 2022, and is a pillar industry of the national economy. Intelligent construction is the realistic demand to promote the high-quality development of China's construction industry. It is the key focus of transforming and upgrading the traditional construction industry to information, digital, and intelligent. As a new production factor, construction robots have become the key to promoting intelligent construction. Under the guidance of various national and industry policies, thanks to China's huge construction market volume and rich application scenarios, many innovative and entrepreneurial entities have entered the field of construction robots. Architectural visualization is a crucial aspect of architectural design and communication. With the development of science and technology, artificial intelligence (AI) technology is increasingly becoming an essential tool for architectural visualization and communication. The emergence of AI technology has provided architects with more flexible and creative ways to present design ideas. All along, designers who love architecture have been passionate about exploring better solutions for architecture, but there is never an optimal solution for architectural design; AI is the fire of the future era; it brings us more opportunities but also forces us to face new challenges from the future.

Localized Control of the Swarming of Kinesin-Driven Microtubules Using Light.

The swarming of self-propelled cytoskeletal filaments has emerged as a new aspect in the field of molecular machines or robotics, as it has overcome one of the major challenges of controlling the mutual interaction of a large number of individuals at a time. Recently, we reported on the photoregulated swarming of kinesin-driven cytoskeletal microtubule filaments in which visible (VIS) and ultraviolet (UV) light triggered the association and dissociation of the swarm, respectively. However, systematic control of this potential system has yet to be achieved to optimize swarming for further applications in molecular machines or robotics. Here, we demonstrate the precise and localized control of a biomolecular motor-based swarm system by varying different parameters related to photoirradiation. We control the reversibility of the swarming by changing the wavelength or intensity of light and the number of azobenzenes in DNA. In addition, we regulate the swarming in local regions by introducing different-sized or shaped patterns in the UV light system. Such a detailed study of the precise control of swarming would provide new perspectives for developing a molecular swarm system for further applications in engineering systems.

TendrilBot: Modular soft robot with versatile radial grasping and locomotion capabilities

This paper introduces TendrilBot, a novel modular soft robot designed for versatile manipulation, stiffness modulation, and locomotion tasks. Built upon previously developed modular units, the TendrilBot consists of multiple actuators connected linearly in linear, cylindrical, or helix configurations. Cylindrical configurations (single row or stacked) enable the robot to wrap around objects for grasping and manipulation in a single row or stacked configuration when interconnected with the magnets on sides of the units. Additionally, such configurations further facilitate internal grasping within hollow tubes or objects holding them with its reconfigurable shape. Both wrapping and internal grasping are experimentally validated to demonstrate radial stiffness modulation and squeezing capabilities. By further rearranging these modular units, TendrilBot can transform into a robotic arm with four or more degrees of freedom, significantly enhancing its manipulative capabilities. In addition, two types of locomotion are demonstrated. When connected in a linear configuration, TendrilBot exhibits locomotion through a sinusoidal motion pattern, similar to sidewinding. When connected in a circular configuration, TendrilBot can achieve locomotion via rolling with steering capabilities, utilizing its cylindrical or helical shape, respectively. Presented are detailed design configurations and characterization of TendrilBot functionalities and capabilities. In addition, we developed a data-driven model that can accurately capture bending of the modular soft actuators, which was validated by the experimental results. Comparison with other similar modular soft robots is provided and discussed. Extensive experimental validations are performed to showcase TendrilBot's potential for diverse applications in the field of modular soft robotics.

Adaptive robot localization in dynamic environments through self-learnt long-term 3D stable points segmentation

In field robotics, particularly in the agricultural sector, precise localization presents a challenge due to the constantly changing nature of the environment. Simultaneous Localization and Mapping algorithms can provide an effective estimation of a robot’s position, but their long-term performance may be impacted by false data associations. Additionally, alternative strategies such as the use of RTK-GPS can also have limitations, such as dependence on external infrastructure. To address these challenges, this paper introduces a novel stability scan filter. This filter can learn and infer the motion status of objects in the environment, allowing it to identify the most stable objects and use them as landmarks for robust robot localization in a continuously changing environment. The proposed method involves an unsupervised point-wise labelling of LiDAR frames by utilizing temporal observations of the environment, as well as a regression network, called Long-Term Stability Network (LTS-NET) to learn and infer 3D LiDAR points long-term motion status. Experiments demonstrate the ability of the stability scan filter to infer the motion stability of objects on a real agricultural long-term dataset. Results show that by only utilizing points belonging to long-term stable objects, the localization system exhibits reliable and robust localization performance for long-term missions compared to using the entire LiDAR frame points.

Research on Corn Leaf and Stalk Recognition and Ranging Technology Based on LiDAR and Camera Fusion.

Corn, as one of the three major grain crops in China, plays a crucial role in ensuring national food security through its yield and quality. With the advancement of agricultural intelligence, agricultural robot technology has gained significant attention. High-precision navigation is the basis for realizing various operations of agricultural robots in corn fields and is closely related to the quality of operations. Corn leaf and stalk recognition and ranging are the prerequisites for achieving high-precision navigation and have attracted much attention. This paper proposes a corn leaf and stalk recognition and ranging algorithm based on multi-sensor fusion. First, YOLOv8 is used to identify corn leaves and stalks. Considering the large differences in leaf morphology and the large changes in field illumination that lead to discontinuous identification, an equidistant expansion polygon algorithm is proposed to post-process the leaves, thereby increasing the average recognition completeness of the leaves to 86.4%. Secondly, after eliminating redundant point clouds, the IMU data are used to calculate the confidence of the LiDAR and depth camera ranging point clouds, and point cloud fusion is performed based on this to achieve high-precision ranging of corn leaves. The average ranging error is 2.9 cm, which is lower than the measurement error of a single sensor. Finally, the stalk point cloud is processed and clustered using the FILL-DBSCAN algorithm to identify and measure the distance of the same corn stalk. The algorithm combines recognition accuracy and ranging accuracy to meet the needs of robot navigation or phenotypic measurement in corn fields, ensuring the stable and efficient operation of the robot in the corn field.

Soft Fibrous Syringe Architecture for Electricity-Free and Motorless Control of Flexible Robotic Systems.

Flexible robotic systems (FRSs) and wearable user interfaces (WUIs) have been widely used in medical fields, offering lower infection risk and shorter recovery, and supporting amiable human-machine interactions (HMIs). Recently, soft electric, thermal, magnetic, and fluidic actuators with enhanced safety and compliance have innovatively boosted the use of FRSs and WUIs across many sectors. Among them, soft hydraulic actuators offer great speed, low noise, and high force density. However, they currently require bulky electric motors/pumps, pistons, valves, rigid accessories, and complex controllers, which inherently result in high cost, low adaptation, and complex setups. This paper introduces a novel soft fibrous syringe architecture (SFSA) consisting of two or more hydraulically connected soft artificial muscles that enable electricity-free actuation, motorless control, and built-in sensing ability for use in FRSs and WUIs. Its capabilities are experimentally demonstrated with various robotic applications including teleoperated flexible catheters, cable-driven continuum robotic arms, and WUIs. In addition, its sensing abilities to detect passive and active touch, surface texture, and object stiffness are also proven. These excellent results demonstrate a high feasibility of using a current-free and motor-less control approach for the FRSs and WUIs, enabling new methods of sensing and actuation across the robotic field.

Current Perspective on Different Bionic Robots in Medical Field

Current Perspective on Different Bionic Robots in Medical Field

A soft robotic system imitating the multimodal sensory mechanism of human fingers for intelligent grasping and recognition

The sensory function is crucial for achieving precise feedback control and environmental interaction in soft robots. Therefore, the multimodal sensory capabilities that mimic human fingers have always been a research hotspot. This study proposes a design method for a soft robotic gripper that can emulate the multimodal perception mechanism of human fingers and achieve high-precision recognition of grasped objects using machine learning algorithms. In addition, a finger texture structure inspired by human fingerprints is designed using a negative pressure-induced buckling method to enhance the soft gripper's ability to perceive minute surface textures. Inspired by slow adapting (SA) receptors and fast adapting (FA) receptors of human fingers, we have innovatively designed a capacitive curvature sensor (CS) and a triboelectric texture sensor (TS) to detect the size, material, and texture of objects. Furthermore, a non-contact medium identification sensor (MIS), having a sensitivity of more than 1300 times higher compared to the traditional MISs, is fabricated using the principle of electromagnetic resonance. It is shown that by employing a deep learning-based multi-channel feature fusion network, the soft grasping system with multimodal sensing has achieved a recognition accuracy of 98.43 % for different objects. This work provides an innovative approach for the application of soft robots in various fields, such as logistics sorting and food safety.

Designing speculative nonhuman-robot scenarios: An artistic approach

In the rapidly evolving field of robotics, a new paradigm is emerging that goes beyond anthropocentric design and embraces the innate ingenuity of the natural world. This article seeks to unveil ecocentric and more-than-human approaches, along with innovative materials, in creating nonhuman robotic scenarios within an artistic framework. Building on the foundation laid by anthropomorphic concepts depicted in science fiction, this article seeks to show cutting-edge experiments and material applications that imagine potential more-than-human scenarios in a robotic context. By highlighting examples from robotics, soft robotics, plant neurobiology, and material research, the article aims to propose an alternative concept to anthropomorphism. It endeavors to offer an overview of cutting-edge scientific efforts, experimental designs, and material explorations, all of which converge toward the realisation of robotic scenarios inspired by the animal and natural kingdom.

Recent Progress on Flexible Self-Powered Tactile Sensing Platforms for Health Monitoring and Robotics.

Over the past decades, tactile sensing technology has made significant advances in the fields of health monitoring and robotics. Compared to conventional sensors, self-powered tactile sensors do not require an external power source to drive, which makes the entire system more flexible and lightweight. Therefore, they are excellent candidates for mimicking the tactile perception functions for wearable health monitoring and ideal electronic skin (e-skin) for intelligent robots. Herein, the working principles, materials, and device fabrication strategies of various self-powered tactile sensing platforms are introduced first. Then their applications in health monitoring and robotics are presented. Finally, the future prospects of self-powered tactile sensing systems are discussed.

Mouse Escape Behaviors and mPFC-BLA Activity Dataset: Understanding Flexible Defensive Strategies Under Threat

Responding to threats in the real world demands a sophisticated orchestration of freeze and flight behaviors dynamically modulated by the neural activity. While the medial prefrontal cortex-basolateral amygdala (mPFC-BLA) network is known to play a pivotal role in coordinating these responses, the mechanisms underlying its population dynamics remain vague. As traditional Pavlovian fear conditioning models fall short in encapsulating the breadth of natural escape behaviors, we introduce a novel dataset to bridge this gap, capturing the defensive strategies of mice against a spider robot in a natural-like environment. The adaptive escape behaviors and concurrent mPFC-BLA activity in eight mice were monitored using wireless local field potential (LFP) and video recordings, both individually and in groups. Our data offers a unique avenue to explore the neural dynamics that govern fear- and vigilance-induced threat responses in isolated and social contexts. Supplemented by detailed methodologies and validation, the dataset allows for the analysis of the transient neural oscillatory dynamics, with prospective implications for the fields of neuroscience, robotics, and artificial intelligence.

Soft Robots: Computational Design, Fabrication, and Position Control of a Novel 3-DOF Soft Robot

This paper presents the computational design, fabrication, and control of a novel 3-degrees-of-freedom (DOF) soft parallel robot. The design is inspired by a delta robot structure. It is engineered to overcome the limitations of traditional soft serial robot arms, which are typically low in structural stiffness and blocking force. Soft robotic systems are becoming increasingly popular due to their inherent compliance match to that of human body, making them an efficient solution for applications requiring direct contact with humans. The proposed soft robot consists of three soft closed-loop kinematic chains, each of which includes a soft actuator and a compliant four-bar arm. The complex nonlinear dynamics of the soft robot are numerically modeled, and the model is validated experimentally using a 6-DOF electromagnetic position sensor. This research contributes to the growing body of literature in the field of soft robotics, providing insights into the computational design, fabrication, and control of soft parallel robots for use in a variety of complex applications.

Collaborative Robotics: A Survey From Literature and Practitioners Perspectives

Collaborative robotics possesses the potential to revolutionise industrial automation by offering affordable and accessible solutions with reasonable skill requirements. However, identifying the most valuable and appropriate applications for this technology remains a challenge. This study conducted a comprehensive literature review to analyse the existing collaborative robotics applications, and the results showed that only a limited number of applications can be considered true collaboration, with even fewer classified as intelligent collaboration. The study comprised a survey designed to offer valuable insights to not only enhance the state-of-the-art analysis in the identification of existing challenges in the field of collaborative robotics but also to provide motivation to guide future advancements. By leveraging the survey results, researchers and practitioners will be better equipped to navigate the complex landscape of collaborative robotics and develop innovative solutions to tackle the identified challenges. This study also informs on the latest research and development in the field.

Novel Lightweight Hydraulic Integration Methodology for Robotic Applications

Hydraulic integration is one of the novel technologies in the field of robotics and assistive devices. Researchers have applied recent technologies starting from non-conventional machining methodologies and ending with additive manufacturing of metals. However, those methodologies have several drawbacks related to the cost, time, and robot weight. This motivates the research of new methodologies toward developing compact, cost-effective, and lightweight hydraulic integrated robotics mechanisms. This paper presents a novel methodology for fabricating hydraulically integrated parts by using lightweight and high-strength materials. The proposed materials, fabrication steps and the obtained results are thoroughly discussed. Silicon pipes are used for building the network of internal passages. This network is built inside a 3D-printed mould which is designed accordingly. A theoretical study is conducted for the newly manufactured hydraulic parts to define the design parameters and the working pressure at which the manufactured parts can withstand in addition to stresses and deformations analysis. The theoretical results are validated using finite element modelling (FEM) and experimental testing. The simulation results were consistent with the FEM results and the experimental results by 95% and 90% respectively. The proposed methodology is cheap and simple to implement in fabrication. Hence, this methodology can be used to fabricate hydraulic integrated components in hydraulically actuated humanoid robots successfully.

Hybrid dynamical modeling of shape memory alloy actuators with phase kinetic equations

Shape memory alloy morphing actuators are a type of composite soft actuator with many attractive properties such as large deformation, small form factor, self-sensing ability, and physical reservoir computing potential. These actuators are composed of active shape memory alloy wires and a passive material to magnify the overall deflection. However, the dynamic modeling of these actuators is difficult due to both shape memory alloy characteristics and the nonlinearity of the passive layer. Here, a hybrid dynamical model is proposed that couples the phase kinetics and thermal modeling for the shape memory alloy with a dynamic Cosserat beam model. This hybrid model is benchmarked against experimental linear and morphing actuators resulting in a root mean squared error of 0.87 mm for the linear actuator and root mean squared error of 1.34 and 1.42 mm for the two morphing actuator configurations evaluated in this work. This model applies continuous phase kinetic equations in a comprehensive hybrid dynamical model to accurately simulate the hysteretic transition of the alloy, which is then coupled to a high deformation beam model. This work can expand the capability and design of novel morphing actuators to achieve specified dynamic characteristics for increased application in robotic fields.

The Multi-Modal Robot Perception, Language Information, and Environment Prediction Model Based on Deep Learning

This paper presents a deep learning-based model to enhance robot perception and prediction in complex environments. The model utilizes CNN, LSTM, and attention mechanisms to address multimodal perception and environment prediction tasks. The introduction provides background on the importance of robots in various fields and emphasizes the need for multimodal perception. The methodology section explains the working principles of CNN, LSTM, and attention mechanisms. The experimental results demonstrate significant improvements in robot perception and prediction, with low errors and high accuracy. The proposed model supports robot applications in complex environments such as autonomous navigation and human-robot interaction. It effectively handles multimodal data and enhances robot perception and prediction capabilities. The experimental results validate the model's effectiveness, providing important support for robot decision-making in complex environments.

Design and Implementation of a 3 Degrees of Freedom Robotic Arm Powered by Pneumatic Artificial Muscle

The development of a 3-DOF robotic arm powered by pneumatic artificial muscles is represented as an innovative approach in the field of robotics, with the advantages of lightweight and flexible design being combined with the power and control benefits of pneumatic actuation. The design process, challenges, and solutions that were encountered in the development of the robotic arm are outlined in this paper, with an emphasis on its potential applications in industrial and research settings being highlighted. Additionally, the robotic arm’s modular design is enabled to allow easy customization and scalability, making it possible for the arm to be tailored to a wide range of tasks, from precise laboratory work to more robust industrial applications.

Enhancing Adaptability and Autonomy in Cooperative Selective Compliance Assembly Robot Arm Robots: Implementation of Coordination and Rapidly Exploring Random Tree Algorithms for Safe and Efficient Manipulation Tasks

In this study, a cooperative robotic system comprising two Selective Compliance Assembly Robot Arm (SCARA) robots was developed and simulated. An algorithm was proposed for the coordination of robots in cooperative tasks, along with a Rapidly exploring Random Tree (RRT) path planner for obstacle avoidance. The proposed system proved effective in transferring objects between robots and in handling various scenarios of variable complexity without collisions. The implementation of advanced trajectory planning and coordination algorithms significantly improves the adaptability and autonomy of robotic systems, allowing robots to predict and react to the movements of their counterparts and changes in the environment in real time. This capability is crucial for maintaining a safe and efficient work environment. The importance of synchronization and effective communication between robots is highlighted to avoid collisions and optimize trajectories and cycle times. All tests were conducted in virtual environments, allowing for the evaluation and refinement of the performance of the robots’ performance under controlled conditions. The positive results obtained in the simulations suggest that the system is well suited for future practical implementation in industrial and manufacturing applications, such as chemical handling, collaborative welding, quality inspection, among others. These findings underscore the potential of the cooperative SCARA system to improve the efficiency and safety in industrial applications using advanced algorithms and control techniques, establishing a solid foundation for future research and development in the field of cooperative robotics.

An adaptive genetic algorithm for the optimization of multi-mobile robot collaboration

The proposed approach utilizes the Robot Operating System (ROS) to simulate multi-robot collaboration across various scenarios, ensuring rigorous testing and validation of the algorithm. Our simulation environment encompasses complex tasks such as 3D digitalization, which demand precise coordination and efficient resource management among robots. The adaptive genetic algorithm (GA) continuously adjusts its parameters to improve performance, making it highly suitable for dynamic and unpredictable environments. Our results demonstrate that the adaptive GA significantly enhances the efficiency and effectiveness of Multi-Robot Task Allocation (MRTA) compared to traditional methods that lack optimization. By incorporating a cost function with various weighted factors, the task allocation process becomes both comprehensive and adaptable to specific mission requirements. This ensures that the robots can allocate tasks efficiently, even as conditions change. This study underscores the potential of adaptive genetic algorithms to advance the capabilities of mobile multi-robot systems, particularly in applications that require high levels of collaboration and precision. Our approach not only improves task allocation efficiency but also enhances the overall coordination and performance of the robotic system. The adaptability and robustness of the GA make it a promising solution for real-world applications, including search-and-rescue missions, environmental monitoring, and industrial automation. So, the potential of adaptive genetic algorithm presents a significant advancement in optimizing mobile multi-robot collaboration. Also, its ability to dynamically adjust to changing conditions and improve task allocation processes highlights its potential for a wide range of applications, marking a notable step forward in the field of collaborative robotics.

SolarBot: A Dual-Mode, Bluetooth-Enabled Two-Wheel Robot Powered by Renewable Energy

This research paper explores the development and deployment of a two-wheel drive robot car that leverages renewable solar energy stored in lithium-ion batteries. The system is designed with an L298N motor driver, an Arduino microcontroller, and an HC-05 Bluetooth module, providing the robot with both autonomous and manual control capabilities. The solar panel, integrated with a charging module and voltage regulator, is mounted on a zero PCB, ensuring a sustainable and uninterrupted power supply. The robot is equipped with ultrasonic sensors, enabling it to detect and navigate around obstacles automatically, ensuring a safe and efficient operation. In its autonomous mode, the robot can navigate predefined paths, making real-time adjustments based on sensor input to avoid collisions. This feature is particularly useful for applications such as automated plant watering, where the robot can move through a garden and irrigate plants while avoiding obstacles. Additionally, the robot can be utilized as a floor cleaning device, capable of maneuvering around furniture and other objects. The RC Bluetooth app facilitates manual control, allowing users to operate the robot remotely for various tasks. The project demonstrates a practical application of renewable energy in robotics, emphasizing the benefits of sustainable energy solutions and versatile operational modes. The use of a wooden platform provides a sturdy base for the robot's components, ensuring durability and reliability. This work contributes to the field of robotics by showcasing how solar power can be effectively harnessed to create energy-efficient, multifunctional robotic systems.