Advanced Robotic Systems Research Articles

Mapping Vedantic Pañcakośas to AI-Powered Machine Existence: An Analogy of Sheaths in Human and Robotic Life

Vedānta, a prominent philosophical school of thought, describes human existence through the concept of five sheaths, or Pañcakośas: Annamaya Kośa, Prāṇamaya kośa, Manomaya Kośa, Vijñānamaya Kośa, and Ānandmaya Kośa. With the rapid advancement of artificial intelligence (AI), machines specifically robots are increasingly being designed to mimic human functions. At present, these AI-powered robots exhibit limited cognitive abilities, but future advancements may enable them to outperform humans in certain domains. This paper explores the possibility of mapping the Panchkośa framework of Vedāntic philosophy onto the existence of AI machines, providing an analogy between the five (or fewer) sheaths that may characterize a robotic existence and the human sheaths. Furthermore, this work offers insights into the distinctions between human existence and contemporary AI machines, helping to illuminate the philosophical and existential differences between humans and intelligent machines. Further it puts forward few intriguing questions that Indian knowledge systems must address should AI robots achieve a fully developed system of Kośas. This model will evolve as developments in computer science continue to produce more advanced and intelligent robotic systems.

A recurrent YOLOv8-based framework for event-based object detection

Object detection plays a crucial role in various cutting-edge applications, such as autonomous vehicles and advanced robotics systems, primarily relying on conventional frame-based RGB sensors. However, these sensors face challenges such as motion blur and poor performance under extreme lighting conditions. Novel event-based cameras, inspired by biological vision systems, offer a promising solution with superior performance in fast-motion and challenging lighting environments while consuming less power. This work explores the integration of event-based cameras with advanced object detection frameworks, introducing Recurrent YOLOv8 (ReYOLOV8), a refined object detection framework that enhances a leading frame-based YOLO detection system with spatiotemporal modeling capabilities by adding recurrency. ReYOLOv8 incorporates a low-latency, memory-efficient method for encoding event data called Volume of Ternary Event Images (VTEI) and introduces a novel data augmentation technique based on Random Polarity Suppression (RPS) optimized for event-based sensors and tailored to leverage the unique attributes of event data. The framework was evaluated using two comprehensive event-based datasets Prophesee's Generation 1 (GEN1) and Person Detection for Robotics (PEDRo). On the GEN1 dataset, ReYOLOv8 achieved mAP improvements of 5%, 2.8%, and 2.5% across nano, small, and medium scales, respectively, while reducing trainable parameters by 4.43% on average and maintaining real-time processing speeds between 9.2 ms and 15.5 ms. For the PEDRo dataset, ReYOLOv8 demonstrated mAP improvements ranging from 9% to 18%, with models reduced in size by factors of 14.5 × and 3.8 × and an average speed improvement of 1.67 × . The results demonstrate the significant potential of bio-inspired event-based vision sensors when combined with advanced object detection frameworks. In particular, the ReYOLOv8 system effectively bridges the gap between biological principles of vision and artificial intelligence, enabling robust and efficient visual processing in dynamic and complex environments. The codes are available on GitHub at the following link https://github.com/silvada95/ReYOLOv8.

Thermally responsive spatially programmable soft actuators with multiple response states enabled by Grayscale UV light processing.

Soft actuators hold great promise for applications in biomimetic robots, artificial muscles, and drug delivery systems due to their adaptability in diverse environments. A critical aspect of designing thermally responsive soft actuators is to achieve spatially programmable actuation under a global thermal stimulus. Different local actuation behaviors can be encoded in one actuator to enable complex morphing structures for different tasks. However, it is challenging to achieve programmability beyond one or binary states. This work introduces a new grayscale ultraviolet (UV) light processing method to fabricate soft actuators with spatially tunable Young's modulus, enabling multiple programmable states in one actuator. Together with a liquid crystal elastomer actuation layer and a photothermal heating layer, the LCE programming layer with spatially programmable moduli allows different regions of the soft actuator to bend to controllable extents under a global thermal stimulus. Various shape morphing patterns can be encoded using UV photomasks with spatially controlled grayscales. Additionally, caterpillar-inspired robots capable of bi-directional crawling and octopus-arm-inspired structures for object manipulation are demonstrated. This work represents advancements in the programmability of thermally responsive soft actuators, laying the foundation for their applications in advanced soft robotic systems.

Design And Development of a Robotic Arm for Space Debris Mitigation Using Electrostatic and Gecko-Inspired Gripping Technologies

As space debris poses a growing threat to satellite operations, there is an urgent need for advanced robotic systems capable of effective debris capture in low Earth orbit. This paper presents the development and optimization of a robotic arm equipped with an electrostatic adhesion mechanism, designed specifically for microgravity environments. The primary objective is to engineer a versatile, lightweight robotic arm that can securely capture and hold various types of debris, including non-magnetic and composite materials. Key features include electrostatic adhesive pads for adaptable grip, a telescopic extension arm to increase reach with minimal mechanical complexity, and a retractable storage profile to streamline debris retrieval and handling. Through detailed calculations, we establish the required adhesive force to counter both inertial and gravitational forces acting on debris, ensuring secure capture even during minor satellite manoeuvres. An electrostatic charging system is designed to induce sufficient adhesive force, with calculations for charge requirements and pad dimensions optimized for secure adhesion. This paper details the design, force calculations, and component selection that make the robotic arm efficient, lightweight, and adaptable, contributing to safer, more effective space debris removal.

3D/4D Printing in Advanced Robotics Systems—Recent Developments and Applications

3D/4D Printing in Advanced Robotics Systems—Recent Developments and Applications

Ball Launching Robot and Object Retriever with V- Belt Method and Robotic Arm Method

This research develops a ball launcher and object picker robot controlled remotely /wirelessly using a microcontroller. The robot is designed to enhance mechanical work efficiency and control in the Indonesian robot contest, with the ability to automatically throw balls and collect objects in a designated area. The robot's drive system utilizes DC motors for mobility, with ball launching and pneumatic systems used for the object collection mechanism. This study emphasizes designing a more efficient system that offers good responsiveness, aiming to provide optimal robot control. The test results showed that the robot could perform ball launching with adequate accuracy and collect objects with suitable speed and precision. With the application of this technology, the robot is expected to be useful in various fields, including industry, sports games, education, agriculture, and other entertainment activities. This robot research opens opportunities for further development in more advanced and practical remote control robotic systems.

Skill-Learning-Based Trajectory Planning for Robotic Vertebral Plate Cutting: Personalization Through Surgeon Technique Integration and Neural Network Prediction.

In robotic-assisted laminectomy decompression, stable and precise vertebral plate cutting remains challenging due to manual dependency and the absence of adaptive skill-learning mechanisms. This paper presents an advanced robotic vertebral plate-cutting system that leverages patient-specific anatomical variations and replicates the surgeon's cutting technique through a trajectory parameter prediction model. A spatial mapping relationship between artificial and patient vertebrae is first established, enabling the robot to mimic surgeon-defined trajectories with high accuracy. The robotic system's trajectory planning begins with acquiring point cloud data of the vertebral plate, which undergoes preprocessing, Non-Uniform Rational B-Splines (NURBS) fitting, and parametric discretization. Using the processed data, a spatial mapping method translates the surgeon's cutting path to the robotic coordinate system, with simulation validating the trajectory's adherence to surgical requirements. To further enhance the accuracy and stability of trajectory planning, a Backpropagation(BP) neural network is implemented, providing predictive modeling for trajectory parameters. The analysis and training of the neural network confirm its effectiveness in capturing complex cutting trajectories. Finally, experimental validation, involving an artificial vertebral body model and cutting trials on patient vertebrae, demonstrates the proposed method's capability to deliver enhanced cutting precision and stability. This skill-learning-based, personalized trajectory planning approach offers significant potential for improving the safety and quality of orthopedic robotic surgeries.

A flexible proximity-pressure–temperature tri-mode robotic sensor with stimulus discriminability, high sensitivity and wide perception range

Along with the explosive utilization of intelligent and bionic robotics, the rise of somatosensory system with excellent flexibility and multiple biological sensing characteristic emerges as a substantial crux of this domain. Herein, we propose a flexible high-performance multi-mode sensor for real-time proximity–pressure–temperature perception based on a monolithic sensing unit with fingerprint-like hierarchical architecture. The monolithic sensing unit, primarily constituted by a double-permeable ionic liquids/Multi-walled nanotubes conductive network, demonstrates dual-functionality in detecting pressure and temperature. Making use of the further synergy of rational topographical architecture engineering and feasible decoupling algorithm construction, extraordinary progress in sensing performances for both pressure and temperature are attained with negligible mutual interferences. Additionally, the sensor is capable of switching to touchless mode to detect objects at distance up to 200 mm, validating its remarkable proximity sensing ability. The multifunctional nature of sensor is further substantiated through its integration with a robotic hand, highlighting its practical applicability in advanced robotic systems.

Justification of an innovative system for the complete burial of solid, high-level radioactive waste (HLW) in spent open-pit mines

The object of research is the spent open-pit mines themselves where the proposed system could be applied. The primary reason for this studying is the following circumstance: up to the present time period, in all countries of the world, no methods of HLW disposal in a storage facility has been identified that is absolutely safe for any length of time, taking into account the impact of catastrophic natural emergencies and man-made emergencies. The research was conducted to address the problem of safely managing and storing HLW, leveraging the unique characteristics of spent open-pit mines, such as their large volume and geological stability, to prevent environmental contamination and ensure long-term safety. In the article has been justified a novel approach to the burial of sarcophagus containers with solid HLW in exhausted mining pits and studied the usabilities of the basalt sarcofagous container. Robust materials and advanced robotic systems proposed in the article aims to address the challenges associated with long-term radioactive waste disposal effectively. The robotic systems transfer the basalt container with HLW, ensuring personnel safety by minimizing human presence near radioactive materials. In the article have been established the distribution of temperature into the multi-layered composite structure of the basalt sarcophagous with HLW from 300 °C into the inner space to 50 °C onto on the its outer suffer where the thickness of each layers (from inner to outer radius) was respectively: for lead matrix: from r1=0.1 m to r2=0.2 m; for clay layer: from r2=0.2 m to r3=0.3 m; for basalt block: from r3=0.3 m to r4=0.4 m. The findings on temperature distribution are crucial as they directly affect the performance and longevity of the basalt containment system

Deep learning based approach for actinidia flower detection and gender assessment

Pollination is critical for crop development, especially those essential for subsistence. This study addresses the pollination challenges faced by Actinidia, a dioecious plant characterized by female and male flowers on separate plants. Despite the high protein content of pollen, the absence of nectar in kiwifruit flowers poses difficulties in attracting pollinators. Consequently, there is a growing interest in using artificial intelligence and robotic solutions to enable pollination even in unfavourable conditions. These robotic solutions must be able to accurately detect flowers and discern their genders for precise pollination operations. Specifically, upon identifying female Actinidia flowers, the robotic system should approach the stigma to release pollen, while male Actinidia flowers should target the anthers to collect pollen. We identified two primary research gaps: (1) the lack of gender-based flower detection methods and (2) the underutilisation of contemporary deep learning models in this domain. To address these gaps, we evaluated the performance of four pretrained models (YOLOv8, YOLOv5, RT-DETR and DETR) in detecting and determining the gender of Actinidia flowers. We outlined a comprehensive methodology and developed a dataset of manually annotated flowers categorized into two classes based on gender. Our evaluation utilised k-fold cross-validation to rigorously test model performance across diverse subsets of the dataset, addressing the limitations of conventional data splitting methods. DETR provided the most balanced overall performance, achieving precision, recall, F1 score and mAP of 89%, 97%, 93% and 94%, respectively, highlighting its robustness in managing complex detection tasks under varying conditions. These findings underscore the potential of deep learning models for effective gender-specific detection of Actinidia flowers, paving the way for advanced robotic pollination systems.

Transitioning trends into action: A simulation-based Digital Twin architecture for enhanced strategic and operational decision-making

In the dynamic realm of nowadays manufacturing, integrating digital technologies has become paramount for enhancing operational efficiency and decision-making processes. This article presents a novel system architecture that integrates a Simulation-based Digital Twin (DT) with emerging trends in manufacturing to enhance decision-making, accompanied by a detailed technical approach encompassing protocols and technologies for each component. The DT leverages advanced simulation techniques to model, monitor, and optimize production processes in real time, facilitating both strategic and operational decision-making. Complementing the DT, trending technologies such as artificial intelligence, additive manufacturing, collaborative robots, autonomous vehicles, and connectivity advancements are strategically integrated to enhance operational efficiency and facilitate the adoption of the Manufacturing as a Service (MaaS) paradigm. A case study within a MaaS supplier context, deployed in an industrial laboratory with advanced robotic systems, demonstrates the practical application of optimizing dynamic job-shop configurations using Simulation-based DT, showcasing strategies to improve operational efficiency and resource utilization. The results of the industrial experiment were highly encouraging, underscoring the potential for extension to more intricate industrial systems, with particular emphasis on incorporating sustainability and remanufacturing principles.

Robotic surgery in the aspect of liver transplantation

Introduction. Almost 60 years have passed since the first liver transplant performed by Thomas Starzl. During this time, medical technologies have gradually improved, which has made it possible to use more and more new methods and approaches in this type of medical care. One of the new techniques of recent decades is robotic surgery, which is gradually being introduced into medical practice, including in the field of transplant medicine.Objective. The purpose of writing this review was to summarize knowledge and describe the current status of development of robotic surgery in the aspect of liver transplantation, namely: liver resection in donors, as well as graft implantation in the recipient.Material and methods. The review includes foreign and domestic publications on minimally invasive donor liver surgery. Publications on the topic of robotic liver resection in the aspect of liver transplantation were also processed.Conclusion. Robotic surgery using advanced robotic systems represents the next step in the development of minimally invasive technologies in liver transplantation. Robotic systems provide more precise and dexterous control of instruments, allowing surgeons to perform complex procedures with greater precision and less risk to patients. However, the robotic approach is still very limited in geographical distribution and requires much more experience than laparoscopy. The upcoming introduction of new robotic systems that support haptic feedback or cavitronic ultrasonic surgical aspirators will further promote a widespread adoption of robotic liver resection in liver donors and liver recipients.

Guessing Human Intentions to Avoid Dangerous Situations in Caregiving Robots

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

Feasibility and safety study of advanced prostate biopsy robot system based on MR-TRUS Image flexible fusion technology in animal experiments

The advanced prostate biopsy robot system has broad application prospects in clinical practice, but due to the deformation and distortion between MR-TRUS (magnetic resonance transrectal ultrasound) images, it poses challenges in biopsy accuracy and safety. The study utilized an advanced prostate biopsy robot system based on MR-TRUS image flexible registration technology and conducted experiments on animal models. Retrospective analysis of the puncture accuracy of 12 animal experiments undergoing prostate puncture using MR-TRUS flexible registration technology from May 2022 to October 2023, and observation of intraoperative and 7-day postoperative complications. The study obtained MR-TRUS images and utilized image processing algorithms for registration to reduce image deformation and distortion. Then, precise positioning and operation are carried out through the robot system to execute the prostate biopsy program. The experimental results indicate that the advanced prostate biopsy robot system based on MR-TRUS image flexible registration technology has demonstrated good feasibility and safety in animal experiments. Image registration technology has successfully reduced image distortion and deformation, improving biopsy accuracy. The precise positioning and operation of robot systems play a crucial role in the biopsy process, reducing the occurrence of complications.

Arduino-Controlled Multi-Function Robot with Bluetooth and nRF24L01+ Communication

This paper outlines the design and development of an advanced robotic system that integrates hardware implementation with theoretical simulation to address the need for versatile and user-friendly robotic solutions in various environments. Addressing the issue of limited adaptability in existing robotic systems, we propose a wireless, voice and gesture-controlled robot car with an integrated robotic arm capable of performing complex tasks such as line following, obstacle avoidance, object manipulation, and autonomous navigation over one-kilometer range. To improve operational efficiency and user involvement, this paper designs a multifunctional robotic platform that integrates user-friendly control interfaces with inexpensive, state-of-the-art sensor technologies. To achieve this, we integrate a variety of sensors, including ultrasonic sensors for precise distance measurement, infrared sensors for object detection and line following, an L298 motor driver for controlling geared motors, servo motors for controlling robotic arms, a flex sensor for claw control, and an mpu6050 accelerometer for gesture recognition. The system also uses a custom-made Bluetooth app for remote control, nRF24L01+ for long-range wireless control, and Arduino Mega and Nano for processing and control functions. The results demonstrate the robot functions well in dynamic conditions, and it can be used in hospitals to assist healthcare professionals, in restaurants for food delivery, and in industrial settings for object manipulation. The system’s design proves robust in real-world scenarios, offering significant improvements in accessibility and operational efficiency. This study aligns with Sustainable Development Goals (SDGs) 3 (Good Health and Well-being), 9 (Industry, Innovation, and Infrastructure), and 17 (Partnerships for the Goals). The robotic arm's potential application in healthcare settings advances SDG 3, its contribution to industrial productivity advances SDG 9, and collaborations with tech companies to expand and improve the robot's capabilities promote SDG 17.

Adaptive approach for tracking movements of biological targets: application to robot-based intervention for prostate cancer.

In this paper, we introduce an advanced robotic system integrated with an adaptive optimization algorithm, tailored for Brachytherapy in prostate cancer treatment. The primary innovation of the system is the algorithm itself, designed to dynamically adjust needle trajectories in response to the real-time movements of the prostate gland during the local intervention. The system employs real-time position data extracted from Magnetic Resonance Imaging (MRI) to ensure precise targeting of the prostate, adapting to its constant motion and deformation. This precision is crucial in Brachytherapy, where the accurate placement of radioactive seeds directly impacts the efficacy of the treatment and minimizes damage to surrounding safe tissues. Our results demonstrate a marked improvement in the accuracy of radiation seed placement, directly correlating to more effective radiation delivery. The adaptive nature of the algorithm significantly reduces the number of needle insertions, leading to a less invasive treatment experience for patients. This reduction in needle insertions also contributes to lower risks of infection and shorter recovery times. This novel robotic system, enhanced by the adaptive optimization algorithm, improves the coverage of targets reached by a traditional combinatorial approach by approximately 15% with fewer required needles. The improved precision and reduced invasiveness highlight the potential of this system to enhance the overall effectiveness and patient experience in prostate cancer Brachytherapy.

Symmetric Pseudo-Multi-Scroll Attractor and Its Application in Mobile Robot Path Planning

The symmetric multi-scroll strange attractor has shown great potential in chaos-based applications due to its high complexity in phase space. Here, the approach of symmetrization is employed for attractor doubling to generate pseudo-multi-scroll attractors in a discrete map, where a carefully selected offset constant is the key to organizing coexisting attractors. By choosing the Hénon map to generate the pseudo-multi-scroll attractor and implementing the digital circuit on a microcontroller, this study fills a significant gap in the research on discrete chaotic systems. The complexity performance is further validated using a pseudo-random number generator, demonstrating substantial academic contributions to the field of chaos theory. Additionally, a pseudo-multi-scroll attractor-based squirrel search algorithm is first developed, showcasing its practical application in mobile robot path planning. This work not only advances the theoretical understanding of chaotic systems but also provides practical methods for implementation in digital systems, offering valuable insights for policy-making in advanced robotic systems and intelligent manufacturing.

Effective recognition design in 8-ball billiards vision systems for training purposes based on Xception network modified by improved Chaos African Vulture Optimizer

This research paper presents a comprehensive investigation into the utilization of color image processing technologies and deep learning algorithms in the development of a robot vision system specifically designed for 8-ball billiards. The sport of billiards, with its various games and ball arrangements, presents unique challenges for robotic vision systems. The proposed methodology addresses these challenges through two main components: object detection and ball pattern recognition. Initially, a robust algorithm is employed to detect the billiard balls using color space transformation and thresholding techniques. This is followed by determining the position of the billiard table through strategic cropping and isolation of the primary table area. The crucial phase involves the intricate task of recognizing ball patterns to differentiate between solid and striped balls. To achieve this, a modified convolutional neural network is utilized, leveraging the Xception network optimized by an innovative algorithm known as the Improved Chaos African Vulture Optimization (ICAVO) algorithm. The ICAVO algorithm enhances the Xception network's performance by efficiently exploring the solution space and avoiding local optima. The results of this study demonstrate a significant enhancement in recognition accuracy, with the Xception/ICAVO model achieving remarkable recognition rates for both solid and striped balls. This paves the way for the development of more sophisticated and efficient billiards robots. The implications of this research extend beyond 8-ball billiards, highlighting the potential for advanced robotic vision systems in various applications. The successful integration of color image processing, deep learning, and optimization algorithms shows the effectiveness of the proposed methodology. This research has far-reaching implications that go beyond just billiards. The cutting-edge robotic vision technology can be utilized for detecting and tracking objects in different sectors, transforming industrial automation and surveillance setups. By combining color image processing, deep learning, and optimization algorithms, the system proves its effectiveness and flexibility. The innovative approach sets the stage for creating advanced and productive robotic vision systems in various industries.

Advances of surgical robotics: image-guided classification and application.

Surgical robotics application in the field of minimally invasive surgery has developed rapidly and has been attracting increasingly more research attention in recent years. A common consensus has been reached that surgical procedures are to become less traumatic and with the implementation of more intelligence and higher autonomy, which is a serious challenge faced by the environmental sensing capabilities of robotic systems. One of the main sources of environmental information for robots are images, which are the basis of robot vision. In this review article, we divide clinical image into direct and indirect based on the object of information acquisition, and into continuous, intermittent continuous, and discontinuous according to the target-tracking frequency. The characteristics and applications of the existing surgical robots in each category are introduced based on these two dimensions. Our purpose in conducting this review was to analyze, summarize, and discuss the current evidence on the general rules on the application of image technologies for medical purposes. Our analysis gives insight and provides guidance conducive to the development of more advanced surgical robotics systems in the future.

Advancing human-robot collaboration: Predicting operator trajectories through AI and infrared imaging

In industrial applications demanding close interaction between humans and robots, accurate perception of the robot's surroundings is vital to adjust its behavior appropriately. This need becomes especially pertinent when manipulators operate autonomously and execute both separate and common tasks with human operators. To ensure operator safety in such Human-Robot Collaboration (HRC) environments, the present paper proposes an innovative approach that leverages advanced collaborative robotic systems capable of sensing the operator's position to effectively prevent or mitigate potential contact. Our solution combines infrared-thermal imaging technology with a workspace monitoring system, utilizing thermal and depth cameras, to track the operator's current location and predict their future trajectory using a multi-layer Long Short-Term Memory (LSTM) neural network. We also introduce a technique for simulating both human and robot movements to generate a synthetic dataset. This data is shared with the motion planning system to generate trajectories that do not intersect with the operator's future paths, minimizing downtime due to safety stoppages. The solution is integrated and evaluated within an industrial high payload collaborative robot context, and the results have shown improvements in cycle time efficiency and operator approval. This approach offers a promising direction for enhancing safety and productivity in HRC workspaces.