Intelligent Robot Research Articles

Mechanical Computing with Transmissive Snapping of Kirigami Shells

AbstractContinuum shape‐morphing structures with the capability to encode memory and execute logic operations have garnered significant interest for the development of mechanical systems with embodied intelligence and soft robots. Achieving the integration of memory and computing within a mechanical system necessitates building blocks that possess a range of tunable, metastable states. Prior efforts have been dedicated to constructing mechanical memory and logic through the exploitation of snap‐through instabilities in multistable structures. Typically, the creation of each logic gate demands a distinct structural design. Here, presents an unconventional design strategy that leverages a single kirigami architecture to perform and switch between multiple fundamental logic operations. By utilizing the kirigami architecture as the fundamental element, mechanical signal transmission is demonstrated and half‐adder computations are performed. It is envisioned that this design strategy can be applied to a wide range of materials and structures, and reduce the complexity of developing materials systems with embodied intelligence.

Design and Implementation of Intelligent Robot Control System Integrating Computer Vision and Mechanical Engineering

With the rapid development of science and technology and industry, robot technology has become a key pillar in many fields, especially in industrial manufacturing, robots have been widely used in automated operations. However, in the face of the complex and changeable demands of modern industry, simple mechanized operation can no longer be satisfied, and intelligence has become the inevitable trend of robot technology development. By combining computer vision technology, this study provides robots with powerful environmental awareness and target recognition capabilities, thus achieving higher-level autonomous operation and decision-making. Aiming at the intelligent sorting system, this research adopts XYZ mechanical arm as the execution platform, equipped with TCS34725 color sensor to identify the color of articles, and integrates Hikvision industrial camera and NVIDIA Jetson TX2 image processor to capture and process image data, so as to achieve accurate positioning of articles. In the aspect of software design, this research realizes the function of accurately sorting different color items from the conveyor belt by combining color recognition algorithm and image processing technology with mechanical engineering control. The test results of the system show that the system has high accuracy and stability in color recognition and object positioning, and the trajectory accuracy of the manipulator meets the requirements, which verifies the realization of the research goal. The intelligent robot control system designed in this paper not only improves the level of industrial automation and reduces labor costs, but also broadens the application fields of robots in complex environments.

Simulation of artificial intelligence robots in dance action recognition and interaction process based on machine vision

Artificial intelligence robots based on machine vision have shown great potential in dance action recognition and interaction. In order to perform action recognition, a large amount of dance action video data was collected as the basis for training and testing the model. In motion recognition, various computer vision techniques are used to extract motion features from images or videos to capture key information of dance movements. Machine learning technology was adopted to construct an action classification model. By using the data collected in the early stage and the extracted features, an efficient and accurate classifier has been studied and trained. It can classify the input dance actions into different categories, segment continuous video frames into different action segments, and ensure accurate differentiation of different dance actions during the recognition process, avoiding misjudgment or confusion. By accurately capturing and analyzing the posture information of the human body, we can better understand and simulate dance movements, and enhance the performance and realism of artificial intelligence robots in dance interactions. The experimental results show that the system can accurately recognize various dance movements and achieve good interaction with users.

Research on the Influence of Humor Response in the Context of Artificial Intelligence Service Failure: Moderating Effect Based on User Humor

With the development of high and new technology, artificial intelligence technology has been widely used in all walks of life. However, limited by the technical development level and the complexity of the service environment, the failure of artificial intelligence robots is inevitable. How to make an effective remedy has become the focus of attention of enterprises and academia. Based on cognitive-affective system theory of personality, benign violation theory and personality trait congruence theory, this paper conducts three situational experiments. This paper explores the relationship between artificial intelligence humor response, perceived sincerity and user forgiveness in the context of artificial intelligence service failure, and discusses the moderating effect of user humor degree. It is found that artificial intelligence humor response has a positive effect on user forgiveness in the context of service failure, and perceived sincerity plays a part of mediating role. In addition, for users with different degrees of humor, the same humorous reply will cause users to perceive different sincerity, and then lead to different users' forgiveness. This study complements the research on consumer psychology and behavior theory and remedy strategies in the context of service failure by artificial intelligence robots. At the same time, it provides a reference for enterprises how to dynamically adjust the artificial intelligence humor response level according to the user portrait, and further provides practical enlightenment for enterprises to implement effective remedy strategies.

Machine Learning Assisted Electronic/Ionic Skin Recognition of Thermal Stimuli and Mechanical Deformation for Soft Robots.

Soft robots have the advantage of adaptability and flexibility in various scenarios and tasks due to their inherent flexibility and mouldability, which makes them highly promising for real-world applications. The development of electronic skin (E-skin) perception systems is crucial for the advancement of soft robots. However, achieving both exteroceptive and proprioceptive capabilities in E-skins, particularly in terms of decoupling and classifying sensing signals, remains a challenge. This study presents an E-skin with mixed electronic and ionic conductivity that can simultaneously achieve exteroceptive and proprioceptive, based on the resistance response of conductive hydrogels. It is integrated with soft robots to enable state perception, with the sensed signals further decoded using the machine learning model of decision trees and random forest algorithms. The results demonstrate that the newly developed hydrogel sensing system can accurately predict attitude changes in soft robots when subjected to varying degrees of pressing, hot pressing, bending, twisting, and stretching. These findings that multifunctional hydrogels combine with machine learning to decode signals may serve as a basis for improving the sensing capabilities of intelligent soft robots in future advancements.

A Review of Orchard Robot Research

In modern agricultural production, orchards, as one of the important agricultural fields, are facing difficulties such as labor shortage, low production efficiency, and rising costs. To cope with these difficulties, orchard robots were introduced as an emerging technology. With the rapid development of robotics, artificial intelligence and machine learning, the application of intelligent robots in the agricultural field is becoming more and more mature. Advances in sensing technology, navigation technology, image recognition and other technologies have provided technical support for the research and application of orchard robots. Through the precise operation of orchard robots, precise management of orchard crops can be realized, including precise fertilization, pest and disease monitoring, directional irrigation, etc., so as to improve the yield and quality of orchards. This increases production efficiency, reduces costs, increases yields, and drives agriculture towards sustainable development. This paper combs the research status of picking robots by combining literature collation and market research, shows the scientific research results of various types of orchard picking robots at home and abroad, makes an in-depth analysis of the existing problems, and discusses the basic system composition of orchard picking robots Existing problems, and propose solutions to the problems found. Orchard picking robot has a huge application space in China, but due to the difficulty of research and development, there are still few economical and practical orchard picking robot products at home and abroad. It is necessary to increase the research and development of intelligent picking robots, integrate multidisciplinary high and new technologies, vigorously carry out innovative research on intelligent picking technology and equipment, improve the precision, portability, and low cost of robots, and promote the early application of orchard picking robots in China.

Wearable glove gesture recognition based on fiber Bragg grating sensing using genetic algorithm-back propagation neural network

The recognition of gestures through intelligent wearable devices has a broad array of applications, including low-visibility industrial sites and robotics. However, accurately and swiftly recognizing gestures poses a significant challenge. This study addresses the complex interplay between multimodal information sensing and signal processing with flexible sensors in wearable data gloves by conducting theoretical and experimental investigations into gesture recognition using fiber Bragg grating (FBG) flexible sensors. First, the degree of bending and bending angle of the FBG sensor were calibrated, and the impact of temperature was investigated. Subsequently, a human hand wearable data glove gesture recognition system based on fiber grating sensing was constructed, and gesture recognition experiments were conducted. Finally, an enhanced error backpropagation (BP) neural network model for gesture recognition based on genetic algorithms (GA) was established and tested and the results were compared with those achieved using a conventional error BP neural network. The comparison revealed that under the same network structure, the recognition accuracy of the GA-BP neural network of approximately 100% was higher than the conventional BP neural network’s accuracy of 95.77%. Thus, the development of gesture recognition using FBG sensors provides a theoretical foundation and technical support for multiple applications of wearable data gloves, driving their adoption in areas such as human–computer interactions, intelligent robotics, and complex high-risk environments.

Enhancing Mobile Robot Navigation: Optimization of Trajectories through Machine Learning Techniques for Improved Path Planning Efficiency

Efficient navigation is crucial for intelligent mobile robots in complex environments. This paper introduces an innovative approach that seamlessly integrates advanced machine learning techniques to enhance mobile robot communication and path planning efficiency. Our method combines supervised and unsupervised learning, utilizing spline interpolation to generate smooth paths with minimal directional changes. Experimental validation with a differential drive mobile robot demonstrates exceptional trajectory control efficiency. We also explore Motion Planning Networks (MPNets), a neural planner that processes raw point-cloud data from depth sensors. Our tests demonstrate MPNet’s ability to create optimal paths using the Probabilistic Roadmap (PRM) method. We highlight the importance of correctly setting parameters for reliable path planning with MPNet and evaluate the algorithm on various path types. Our experiments confirm that the trajectory control algorithm works effectively, consistently providing precise and efficient trajectory control for the robot.

A planar compound eye based microsystem for high precision 3D perception

Three-dimensional (3D) panoramic vision system plays a fundamental role in the biological perception of external information, and naturally becomes a key system for embodied intelligence to interact with the outside world. A binocular vision system with rotating eyeball has long baseline, large volume and weak sensitivity to motion. A compound eye system has small volume, high sensitivity to motion but poor precision. Here, a planar compound eye microsystem for high precision 3D perception is proposed by combining semiconductor manufacturing process and biological compound eye structure. Using a semiconductor planar image sensor as the sensing unit, a space-coded planar sub-eye array is designed and its sub field of view (FOV) is dynamically mapped to the image sensor. It solves the problem that a traditional vision system cannot simultaneously accommodate wide FOV with long focal length and high sensitivity to motion with high resolution. The parallax among different sub-eyes enables the system to accurately perceive and dynamically track the 3D position of the target in the range of 10 m and within the FOV of 120 ° in a single compound eye. This system is of great significance in the fields of intelligent robot and intelligent perception.

High-precision apple recognition and localization method based on RGB-D and improved SOLOv2 instance segmentation

Intelligent apple-picking robots can significantly improve the efficiency of apple picking, and the realization of fast and accurate recognition and localization of apples is the prerequisite and foundation for the operation of picking robots. Existing apple recognition and localization methods primarily focus on object detection and semantic segmentation techniques. However, these methods often suffer from localization errors when facing occlusion and overlapping issues. Furthermore, the few instance segmentation methods are also inefficient and heavily dependent on detection results. Therefore, this paper proposes an apple recognition and localization method based on RGB-D and an improved SOLOv2 instance segmentation approach. To improve the efficiency of the instance segmentation network, the EfficientNetV2 is employed as the feature extraction network, known for its high parameter efficiency. To enhance segmentation accuracy when apples are occluded or overlapping, a lightweight spatial attention module is proposed. This module improves the model position sensitivity so that positional features can differentiate between overlapping objects when their semantic features are similar. To accurately determine the apple-picking points, an RGB-D-based apple localization method is introduced. Through comparative experimental analysis, the improved SOLOv2 instance segmentation method has demonstrated remarkable performance. Compared to SOLOv2, the F1 score, mAP, and mIoU on the apple instance segmentation dataset have increased by 2.4, 3.6, and 3.8%, respectively. Additionally, the model’s Params and FLOPs have decreased by 1.94M and 31 GFLOPs, respectively. A total of 60 samples were gathered for the analysis of localization errors. The findings indicate that the proposed method achieves high precision in localization, with errors in the X, Y, and Z axes ranging from 0 to 3.95 mm, 0 to 5.16 mm, and 0 to 1 mm, respectively.

Bio-inspired multimodal learning with organic neuromorphic electronics for behavioral conditioning in robotics

Biological systems interact directly with the environment and learn by receiving multimodal feedback via sensory stimuli that shape the formation of internal neuronal representations. Drawing inspiration from biological concepts such as exploration and sensory processing that eventually lead to behavioral conditioning, we present a robotic system handling objects through multimodal learning. A small-scale organic neuromorphic circuit locally integrates and adaptively processes multimodal sensory stimuli, enabling the robot to interact intelligently with its surroundings. The real-time handling of sensory stimuli via low-voltage organic neuromorphic devices with synaptic functionality forms multimodal associative connections that lead to behavioral conditioning, and thus the robot learns to avoid potentially dangerous objects. This work demonstrates that adaptive neuro-inspired circuitry with multifunctional organic materials, can accommodate locally efficient bio-inspired learning for advancing intelligent robotics.

ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING ENHANCE ROBOT DECISION-MAKING ADAPTABILITY AND LEARNING CAPABILITIES ACROSS VARIOUS DOMAINS

This study examines the transformative role of artificial intelligence (AI) and machine learning (ML) in enhancing robot decision-making, adaptability, and learning capabilities. By integrating AI and ML algorithms, robots are evolving from pre-programmed tools into intelligent collaborators capable of operating effectively in complex and dynamic environments. This article provides a comprehensive overview of AI and ML in robotics, exploring their fundamental principles, diverse applications across industries, and associated challenges. Through a case-study-based approach, the study analyzes real-world implementations of AI-powered robots in manufacturing, healthcare, logistics, and industrial inspection, demonstrating their significant impact on efficiency, productivity, and safety. The findings highlight the potential of AI and ML to revolutionize various sectors, while emphasizing the need for ongoing research and responsible development to address ethical considerations and ensure the safe and beneficial integration of intelligent robots into society.

ADM-SLAM: Accurate and Fast Dynamic Visual SLAM with Adaptive Feature Point Extraction, Deeplabv3pro, and Multi-View Geometry.

Visual Simultaneous Localization and Mapping (V-SLAM) plays a crucial role in the development of intelligent robotics and autonomous navigation systems. However, it still faces significant challenges in handling highly dynamic environments. The prevalent method currently used for dynamic object recognition in the environment is deep learning. However, models such as Yolov5 and Mask R-CNN require significant computational resources, which limits their potential in real-time applications due to hardware and time constraints. To overcome this limitation, this paper proposes ADM-SLAM, a visual SLAM system designed for dynamic environments that builds upon the ORB-SLAM2. This system integrates efficient adaptive feature point homogenization extraction, lightweight deep learning semantic segmentation based on an improved DeepLabv3, and multi-view geometric segmentation. It optimizes keyframe extraction, segments potential dynamic objects using contextual information with the semantic segmentation network, and detects the motion states of dynamic objects using multi-view geometric methods, thereby eliminating dynamic interference points. The results indicate that ADM-SLAM outperforms ORB-SLAM2 in dynamic environments, especially in high-dynamic scenes, where it achieves up to a 97% reduction in Absolute Trajectory Error (ATE). In various highly dynamic test sequences, ADM-SLAM outperforms DS-SLAM and DynaSLAM in terms of real-time performance and accuracy, proving its excellent adaptability.

The Next Generation of Robotics: The 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems in Detroit, MI, USA [Conference Highlights]

The Next Generation of Robotics: The 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems in Detroit, MI, USA [Conference Highlights]

Empowering customer satisfaction chatbot using deep learning and sentiment analysis

The rapid advancement of technology holds great promise for various types of users, clients, or service providers. Intelligent robots, whether virtual or physical, can simplify the reservation process. With the development of machines and processing tools, natural language processing (NLP) and natural language understanding (NLU) have emerged to help people comprehend spoken language through machines. In order to facilitate seamless human-machine interaction, we aim to address customer needs through a chatbot. The objective of this paper is to incorporate sentiment analysis techniques with deep learning algorithms to cater to customers’ needs during message exchanges. This study aims to create an intelligent chatbot to engage customers during their routine operations and offer support. In addition, it offers to companies a manner to detect sarcastic messages. The proposed chatbot utilizes deep learning techniques to predict users’ intentions based on the questions asked and provide a helpful and convenient answer. A new chatbot for the customer is presented to overcome with challenges related to a wrong statement like sarcastic one and feedback towards user messages. A comparison between deep and transfer learning gives a new insight to include sentiments and sarcasm detection in the conversion process.

Biomimic and bioinspired soft neuromorphic tactile sensory system

The progress in flexible and neuromorphic electronics technologies has facilitated the development of artificial perception systems. By closely emulating biological functions, these systems are at the forefront of revolutionizing intelligent robotics and refining the dynamics of human–machine interactions. Among these, tactile sensory neuromorphic technologies stand out for their ability to replicate the intricate architecture and processing mechanisms of the brain. This replication not only facilitates remarkable computational efficiency but also equips devices with efficient real-time data-processing capability, which is a cornerstone in artificial intelligence evolution and human–machine interface enhancement. Herein, we highlight recent advancements in neuromorphic systems designed to mimic the functionalities of the human tactile sensory system, a critical component of somatosensory functions. After discussing the tactile sensors which biomimic the mechanoreceptors, insights are provided to integrate artificial synapses and neural networks for advanced information recognition emphasizing the efficiency and sophistication of integrated system. It showcases the evolution of tactile recognition biomimicry, extending beyond replicating the physical properties of human skin to biomimicking tactile sensations and efferent/afferent nerve functions. These developments demonstrate significant potential for creating sensitive, adaptive, plastic, and memory-capable devices for human-centric applications. Moreover, this review addresses the impact of skin-related diseases on tactile perception and the research toward developing artificial skin to mimic sensory and motor functions, aiming to restore tactile reception for perceptual challenged individuals. It concludes with an overview of state-of-the-art biomimetic artificial tactile systems based on the manufacturing–structure–property–performance relationships, from devices mimicking mechanoreceptor functions to integrated systems, underscoring the promising future of artificial tactile sensing and neuromorphic device innovation.

A multi-agent reinforcement learning approach for ART adaptive control in automated container terminals

Intelligent transportation equipment represented by Artificial Intelligence Robot of Transportation (ART) has been gradually applied to automated container terminals (ACT). ARTs have higher intelligence and distributed autonomous decision-making capability compared to AGVs. The production and operation of ACT require strict loading plans based on container types and destination ports, which puts strict requirements on the ART loading task sequence. ART travel time from the yard to the quay is highly uncertain due to special scenarios such as traffic flow convergence and turning in narrow areas, which will lead to long waits for ART due to out-of-sequence arrivals at the quay. Therefore, it is necessary to regulate the speed of ART to adjust the timing of ART arriving at the quay. However, existing scheduling systems tend to ignore the impact of ART speed and loading sequence constraints on terminal operations. To optimize the multi-ART control problem of ACT, the waiting time of ART at the quay is taken as the optimization goal, and the speed regulation of ART during each task is defined as a Markov decision process. Then, by incorporating reinforcement learning and a multi-agent approach, an adaptive decision-making method is developed to generate the optimal speed regulation strategy. This method interacts with the multi-agent simulation environment to obtain real-time status information and learns strategies online to improve the intelligence of individual agents and the collaborative capabilities of the agent group. Finally, a multi-agent simulation model embedded with a reinforcement learning agent is established on AnyLogic software. Simulation results show that this method can effectively reduce the waiting time of ART, improve the coherence and efficiency of loading operations, and exhibit good performance in online scenarios that consider emergency tasks. The results indicate that the speed of ART in ACTs with parallel layout of the yard can impact the operational efficiency of the system. Furthermore, by appropriately defining the reward function and training method, collaborative behaviour among equipment can be enhanced, ultimately optimizing the overall performance of the system.

Types of AI and Their Suitability for Performing Daily Activities

Abstract The usage of robots and artificial intelligence in the business field and in the everyday life of consumers has been a topic of discussion for a long time. While some people are fascinated by the opportunity of having robots supporting them in their daily activities, others are more hesitant. Nowadays, there are different types of robots with artificial intelligence, and consumers, depending on their preferences and needs, prefer a certain type of robot. With the help of an online survey, we found out which type of robot is most often used by the consumer and why. Among the various types of robots with artificial intelligence that were mentioned in the questionnaire, personal home assistants are the robot with artificial intelligence that consumers prefer when they want to perform an action. The second most preferred robot with artificial intelligence is the self-service portal with AI, and in third place is the personal voice assistant. The main results show that personal home assistants are robots with artificial intelligence, which is used most often by the consumer to order food, different products and to perform different activities. With the help of the results, we can also observe that consumers also use the self-service portal with AI as a navigation system or as an alarm clock. The personal voice assistant is the type of artificial intelligence robot that is least used by consumers to do something, which is again an important result.

Incorporating Humanoid Artificial Intelligence (AI) Robots into Early Childhood Education

Incorporating Humanoid Artificial Intelligence (AI) Robots into Early Childhood Education

Human-like intelligent automatic treatment planning of head and neck cancer radiation therapy

Objective. Automatic treatment planning of radiation therapy (RT) is desired to ensure plan quality, improve planning efficiency, and reduce human errors. We have proposed an Intelligent Automatic Treatment Planning framework with a virtual treatment planner (VTP), an artificial intelligence robot built using deep reinforcement learning, autonomously operating a treatment planning system (TPS). This study extends our previous successes in relatively simple prostate cancer RT planning to head-and-neck (H&N) cancer, a more challenging context even for human planners due to multiple prescription levels, proximity of targets to critical organs, and tight dosimetric constraints. Approach. We integrated VTP with a real clinical TPS to establish a fully automated planning workflow guided by VTP. This integration allowed direct model training and evaluation using the clinical TPS. We designed the VTP network structure to approach the decision-making process in RT planning in a hierarchical manner that mirrors human planners. The VTP network was trained via the Q-learning framework. To assess the effectiveness of VTP, we conducted a prospective evaluation in the 2023 Planning Challenge organized by the American Association of Medical Dosimetrists (AAMD). We extended our evaluation to include 20 clinical H&N cancer patients, comparing the plans generated by VTP against their clinical plans. Main results. In the prospective evaluation for the AAMD Planning Challenge, VTP achieved a plan score of 139.08 in the initial phase evaluating plan quality, and 15 min of planning time with the first place ranking in the adaptive phase competing for planning efficiency while meeting all plan quality requirements. For clinical cases, VTP-generated plans achieved an average VTP score of 125.33±11.12 , which outperformed the corresponding clinical plans with an average score of 117.76±13.56 . Significance. We successfully integrated VTP with the clinical TPS to achieve a fully automated treatment planning workflow. The compelling performance of VTP demonstrated its potential in automating H&N cancer RT planning.