Robotic Control Research Articles

Towards Natural Interaction: Hand Landmark Detection for Robotic Arm Control

In recent years, the integration of computer vision algorithms and robotics has shown great potential for revolutionizing various domains, including medical sciences. This paper aims to explore the cutting-edge applications of computer vision and robotics in medical research and healthcare. By leveraging the capabilities of computer vision algorithms and robotic systems, we can enhance medical diagnosis, surgical interventions, and patient care. This research paper provides an in-depth analysis of the key components, challenges, and potential future directions of this interdisciplinary field. In this paper, we propose an innovative approach to addressing the scarcity of professional medical assistance in isolated communities by establishing efficient communication between the human operator and a robotic limb that has enabled the precise replication of human movements in real-time by implementing computer vision, regardless of geographical distance, thereby revolutionizing the remote operation of such robotic devices. This advanced artificial body part aims to perform not only basic health check-ups but also has the potential for more complex medical procedures. Our approach overcomes limitations of existing prosthetic models, such as pre-determined task-specific functionalities and high latency in real-time operation as well as the necessity for the operator and the robotic unit to be nearby. Furthermore, the proposed model reduces the need for frequent calibration and constant monitoring, enhancing its practical applicability. It can also collect valuable medical data, providing patients with a reliable means of tracking their health conditions and medical professionals with vast amounts of structured medical data previously inaccessible. We aim to inspire further research and development in the field of computer vision algorithms and robotics for advancements in medical sciences. Through this interdisciplinary work, we anticipate improved healthcare access and outcomes for individuals residing in isolated communities, ultimately contributing to the overall enhancement of medical services on a global scale.

Firefly Algorithm Optimization of Manipulator Robotic Control Based on Fast Terminal Sliding Mode

In this paper a new algorithm of optimization in the field of manipulator robotic control is presented. The proposed control approach based on fast terminal sliding mode control (FTSMC) in order to guarantee the convergence of the position articulations errors to zero in finite time without chattering phenomena, and the firefly algorithm in order to generate the optimal parameters that ensure minimum reaching time and mean square error and achieve better performances, whose ensure the asymptotic stability of the system using a Lyapunov candidate in presence of disturbances. The simulations are applied on a two-link robotic manipulator with different tracking references by using Matlab/ Simulink , results show the efficiency and confirm the robustness of the proposed control strategy.

Automated Hooking of Biomedical Microelectrode Guided by Intelligent Microscopic Vision

Biomedical microelectrode implantation is a promising approach to achieve invasive brain-machine interface with high bandwidth and good biocompatibility. However, it is difficult to manipulate a microelectrode probe because of its micron-level width and flexible material. Toward the challenging robotic implantation, this article addresses an essential operation step: automated hooking of flexible microelectrode probe with a micro-needle. First, a point-contour of interest extraction network for microscopic image (MicroPCIE) is proposed to obtain key image features for visual guidance. Based on few-shot learning mechanism, MicroPCIE trained on nonspecific dataset can be rapidly deployed to the specific hooking task without requiring data-driven optimization. Second, a robotic hooking control system is designed considering the distributed probe locations, variable needle direction, and efficiency requirements. The needle-probe alignment control is executed under low and high microscope magnifications, to realize larger operation range and permit high alignment precision. The optical design and camera aiming control are proposed to guarantee the visibility and clearness of objects after low-to-high magnification switch. The effectiveness of the proposed methods is validated with a series of experiments.

Gaussian process regression for forward and inverse kinematics of a soft robotic arm

The use of soft robotics to perform tasks and interact with the environment requires good system identification. Data-driven methods offer a promising alternative where traditional analytical model-based techniques have proven insufficient. However, their use has been limited and under-explored in soft robotics. The novelty of this research lies in the application of Gaussian processes to soft robotics and the exploration of approximate Gaussian processes (AGP) and deep Gaussian processes (DGP) methods. It highlights the advantages of Gaussian processes in modeling uncertainty, incorporating prior knowledge, and handling complex systems. This is achieved through the identification of the forward and inverse kinematics of a two-degree-of-freedom soft robotic arm actuated by three tendons. A comparison is made between different configurations using Gaussian processes and the results are also compared with those obtained from the analytical model of the kinematics and an artificial neural network (ANN). The research contributes to the development of more efficient and accurate techniques for system identification, kinematics modeling, and control in soft robotics.

A Model to Predict Deflection of an Active Tendon-Driven Notched Needle Inside Soft Tissue.

The last decade has witnessed major progress in the field of minimally invasive and robotic-assisted surgeries. Needle insertion, a minimally invasive technique, has proven its efficacy in procedures such as brachytherapy, ablation, drug delivery, and biopsy. Manual needle steering inside tissue is a challenging task due to complex needle-tissue interactions, needle and tissue movement, lack of actuation and control, as well as poor sensing and visualization. Recently, active tendon-driven notched needles, and robotic manipulation systems have been proposed to assist surgeons to guide the needles in desired trajectories toward target positions. This work introduces a new deflection model for the active tendon-driven notched needle steering inside soft tissue for intention to use in model-based robotic control. The model is developed to predict needle deflection in a single-layer tissue. To validate the proposed deflection model, five sets of needle insertion experiments with a bevel-tipped active needle into single-layer phantom tissues were performed. A real-time robot-assisted ultrasound tracking method was used to track the needle tip during needle insertion. It was shown that the model predicts needle deflection with an average error of 0.58 ± 0.14 mm for the bevel-tipped active needle insertion into a single-layer phantom tissue.

Research into the Beetle Antennae Optimization-Based PID Servo System Control of an Industrial Robot

Industrial robot speed control remains a critical aspect for efficient operations, especially given the challenges of nonlinearity and multivariable characteristics inherent to servo motor control systems, as well as energy inefficiencies due to a lack of automatic speed control. This study refines an existing control algorithm, beetle antennae optimization (BAO), by integrating elements of particle swarm optimization (PSO) and a beetle antennae search algorithm (BAS), further enhanced by chaos mapping and an adaptive weighting factor. These modifications aim to improve the algorithm’s search capabilities and mitigate the risks of settling into local optima. Unlike previous iterations, this study includes rigorous dynamic and stability analyses focusing on key performance metrics such as settling time, overshoot, and steady-state error. Comparative Simulink/MATLAB modeling demonstrates that the enhanced BAO algorithm significantly outperforms traditional PID control, BAS, and adaptive weighted-PSO in reducing static error, overshoot, and adjustment time under various conditions, including scenarios with external disturbances. Our results indicate a 60% improvement in the optimization performance of speed curve metrics, confirming the enhanced efficacy and robustness of the robotic control system. This research offers valuable insights into the advantages of the refined BAO algorithm, providing a comprehensive basis for its practical application in industrial robotic control systems.

Effectiveness of a vision-based handle trajectory monitoring system in studying robotic suture operation.

Data on surgical robots are not openly accessible, limiting further study of the operation trajectory of surgeons' hands. Therefore, a trajectory monitoring system should be developed to examine objective indicators reflecting the characteristic parameters of operations. 20 robotic experts and 20 first-year residents without robotic experience were included in this study. A dry-lab suture task was used to acquire relevant hand performance data. Novices completed training on the simulator and then performed the task, while the expert team completed the task after warm-up. Stitching errors were measured using a visual recognition method. Videos of operations were obtained using the camera array mounted on the robot, and the hand trajectory of the surgeons was reconstructed. The stitching accuracy, robotic control parameters, balanceanddexterityparameters, and operation efficiency parameters were compared. Experts had smaller center distance (p < 0.001) and larger proximal distance between the hands (p < 0.001) compared with novices. The path and volume ratios between the left and right hands of novices were larger than those of experts (both p < 0.001) and the total volume of the operation range of experts was smaller (p < 0.001). The surgeon trajectory optical monitoring system is an effective and non-subjective method to distinguish skill differences. This demonstrates the potential of pan-platform use to evaluate task completion and help surgeons improve their robotic learning curve.

Highly Stable Ladder‐Type Conjugated Polymer Based Organic Electrochemical Transistors for Low Power and Signal Processing‐Free Surface Electromyogram Triggered Robotic Hand Control

AbstractOrganic electrochemical transistors (OECTs) based complementary inverters have been considered as promising candidates in electrophysiological amplification, owing to their low power consumption, and high gain. To create complementary inverters, it is important to use highly stable p‐type and n‐type polymers with well‐balanced current. In this study, the electrochemical stability of p‐type ladder‐conjugated polymer‐based OECT is improved through an annealing process that maintains its doped‐state drain current from 76% to 105% after 4,500 cycles in ambient environment. Next an OECT‐based complementary inverter made from p‐type and n‐type ladder‐conjugated polymers (PBBTL and BBL) that possess ultra‐low power consumption (≈170 nW), high gain (67 V/V), and high noise margin (92%) with full rail‐to‐rail swing, is presented. Furthermore, its potential for amplifying the envelope of surface electromyography (EMG) for robotic hand control is demonstrated. The high variation in the output (0.35 V) allows the amplified EMG signals to be directly captured by a commercial analog‐to‐digital converter, which in turn controls the robot hand to grasp different objects with low delay and low noise. These results demonstrate the capability of OECT inverter‐based amplifier in future signal processing‐free human‐machine interface, particularly useful for prosthetic control and gesture control applications.

Nonlinear optimal control for permanent magnet synchronous spherical motors

Nonlinear optimal control for permanent magnet synchronous spherical motors

Analog Programmable‐Photonic Computation

AbstractDigital electronics is a technological cornerstone in this modern society that has covered the increasing demand for computing power during the last decades thanks to a periodic doubling of transistor density in integrated circuits. Currently, such scaling law is reaching its fundamental limit, leading to the emergence of a large gamut of applications that cannot be supported by digital electronics, specifically, those that involve real‐time multi‐data processing, e.g., medical diagnostic imaging, robotic control, and autonomous driving, among others. In this scenario, an analog computing approach implemented in a real‐time reconfigurable nonelectronic hardware such as programmable integrated photonics (PIP) can be more efficient than digital electronics to perform these emerging applications. However, actual analog computing models such as quantum and neuromorphic computation were not conceived to extract the unique benefits of PIP (and integrated photonics in general). Here, the foundations of a new computation theory are presented, termed Analog Programmable‐Photonic Computation (APC), explicitly designed to unleash the full potential of PIP technology. Interestingly, APC enables overcoming basic theoretical and technological limitations of existing computational models and can be implemented in other technologies (e.g., in electronics, acoustics or using metamaterials), consequently exhibiting the potential to spark a ground‐breaking impact on the information society.

Continuous Lower Limb Multi‐Intent Prediction for Electromyography‐Driven Intrinsic and Extrinsic Control

Currently, electromyography (EMG)‐driven lower limb control can be divided into computational intrinsic control and interactive extrinsic control based on the participation of neural information in the controllers. However, the former method lacks detailed measurements of the expected motion, whereas the latter is prone to errors in diverse movement situations. Based on this problem, a multi‐intent prediction scheme is proposed that analyzes both rhythmic locomotion and volitional movement intent for AI‐based intrinsic and extrinsic hybrid control (AIEC). A 1D residual shrinkage convolutional network is designed to extract EMG features. The motion state is continuously predicted with an accuracy of 91.66% and the angle completion is also estimated with of 0.9540 and 0.9456 for left and right knee angles, respectively. Additionally, the comparison test further indicates that the motion state classification is significantly improved in the multitask analysis compared with the single‐task approach. This work demonstrates and verifies a novel method in EMG studies that multi‐intent recognition not only compensates for the lack of information in the analysis of single rhythmic or volitional movement, but also enhances the comprehensive performance and makes AIEC feasible, which optimizes the current EMG‐driven control for ampler intent collection and more practical robotic control.

Premature convergence in morphology and control co-evolution: a study

This article addresses the co-evolution of morphology and control in evolutionary robotics, focusing on the challenge of premature convergence and limited morphological diversity. We conduct a comparative analysis of state-of-the-art algorithms, focusing on QD (Quality-Diversity) algorithms, based on a well-defined methodology for benchmarking evolutionary algorithms. We introduce carefully chosen indicators to evaluate their performance in three core aspects: task performance, phenotype diversity, and genotype diversity. Our findings highlight MNSLC (Multi-BC NSLC), with the introduction of aligned novelty to NSLC (Novelty Search with Local Competition), as the most effective algorithm for diversity preservation (genotype and phenotype diversity), while maintaining a competitive level of exploitability (task performance). MAP-Elites, although exhibiting a well-balanced trade-off between exploitation and exploration, fall short in protecting morphological diversity. NSLC, while showing similar performance to MNSLC in terms of exploration, is the least performant in terms of exploitation, contrasting with QN (Fitness-Novelty MOEA), which exhibits much superior exploitation, but inferior exploration, highlighting the effects of local competition in skewing the balance toward exploration. Our study provides valuable insights into the advantages, disadvantages, and trade-offs of different algorithms in co-evolving morphology and control.

Axis-space framework for cable-driven soft continuum robot control via reinforcement learning

Cable-driven soft continuum robots are important tools in minimally invasive surgery (MIS) to reduce the lesions, pain and risk of infection. The feasibility of employing deep reinforcement learning (DRL) for controlling cable-driven continuum robots has been investigated; however, a considerable gap between simulations and the real world exists. Here we introduce a deep reinforcement learning-based method, the Axis-Space (AS) framework, which accelerates the computational speed and improves the accuracy of robotic control by reducing sample complexity (SC) and the number of training steps. In this framework, the SC was reduced through the design of state space and action space. We demonstrate that our framework could control a cable driven soft continuum robot with four tendons per section. Compared with the Double Deep Q-learning Network (DDQN) controller, the proposed controller increased the convergence speed by more than 11-fold, and reduced the positioning error by over 10-fold. This framework provides a robust method for soft robotics control.

Automated Object Sorting System with Real-Time Image Processing and Robotic Gripper Mechanism Control

This work represents an industrial sorting system where image processing is accompanied using a pick-and-place robotic gripper. The sorting of objects is done based on their shape and color. Here the color and shape of different objects are identified using image processing. For this, a webcam is used to capture images of the object in real-time and then process them via a digital computer. Python programming language is used for image processing in this work. After successfully identifying the color and shape of an object, the object is picked and placed at the desired position using the robotic gripper. Controlling the gripper mechanism is also executed using the Python programming language. It is controlled using the Arduino Uno microcontroller and a few DC servo motors. The gripper can move from 0° to 180°. The objects are brought in front of the camera using a belt conveyor system. After the complete fabrication and assembly, 4 objects of different shapes and colors are used to sort objects at 4 different angles. The objects are picked from 90° and is sorted in either 0°, 45°, 135°, or 180° position. This research work not only gives information about robotics but also can help industries sort complex objects automatically without any human interaction.

Built-in Analog Automatic Controls for Small Robots

One of the most promising methods for improving control efficiency is the parallel synthesis of commands by a group of computing devices. However, delays in digital-to-analog conversions and software processing, the need to synchronize calculations, and signal transmission coordination prevent the achievement of maximum performance in distributed systems. Problems can be solved by increasing the power of embedded microprocessors. This leads to an undesirable increase in design complexity, energy consumption, and dimensions of controls. For the miniaturization of embedded control systems, the article proposes to apply the principles of the indivisibility of physical processes in a technical system and the execution of control commands based on the functional combination of aggregates with analog logic devices (agents). A generalized model of changing the states of a technical system is considered, which sets the principles for switching on or off groups of units. The concept of an automaton of ternary logic is introduced, which determines the order of interaction of aggregates in the time domain. The synthesis of the block diagram of the automaton is carried out. In the mathematical model of the automaton, the operations of ternary logic receive a specific physical content in the form of retrospective processing of changes in the states of aggregates. It is shown that ternary logic automata can be combined into open and closed chains, setting hardware control algorithms units robot. The circuits for connecting automata controlled by elements of asynchronous logic are given. When connected in series, the automata control the order of switching on and off the units. Chains of three-valued logic automata can be arbitrarily long, determining the behavior of the system in the process of its operation. Due to the rejection of program control, such automata can operate in real time, limited only by the delay of signals in logic elements. An important advantage of the proposed technical solution is the unification of the automatic control system. The simplicity of the design of devices and the absence of additional add-ons make it possible to integrate such circuits into the controls of microminiature robots, reducing their weight and size parameters. Promising areas of application of analog automata are microelectromechanical and microelectronic devices.

Design of Modular Robotic Arm Remote Control Car based on Arduino Development Board

This research aims to design and implement an Arduino modular robotic arm remote control car, providing new possibilities for improving product universal control and comprehensive development boards. By integrating the PWM expansion chip PCA9685 with integrated I2C communication and two TB6612 motor driver chips, a new development board is designed to realize a modular multi-functional remote control car that can be controlled remotely, using Mecanum wheels with decelerating DC Motors carry out the motion and are equipped with robotic arms that can operate with precision. Using Arduino for design and programming, functions such as image transmission, tracking, Bluetooth, and gravity control can be realized. This robotic arm remote control car has a wide range of applications and can be used in various occasions. Through debugging, optimization and repeated testing, it has shown excellent performance under various conditions, and has certain reference value for the application of Arduino in engineering technology, scientific research and other fields. This article introduces the structural design, implementation, component selection, program design and other processes in detail, in order to provide certain guidance and reference for similar research.

Fault detection and state estimation in robotic automatic control using machine learning

In the commercial and industrial sectors, automatic robotic control mechanisms, which include robots, end effectors, and anchors containing components, are often utilized to enhance service quality. Robotic systems must be installed in manufacturing lines for a variety of industrial purposes, which also increases the risk of a robot, end controller, and/or device malfunction. According to its automated regulation, this may hurt people and other items in the workplace in addition to resulting in a reduction in quality operation. With today's advanced systems and technology, security and stability are crucial. Hence, the system is equipped with fault management abilities for the identification of developing defects and assessment of their influence on the system's activity in the upcoming utilizing fault diagnostic methodologies. To provide adaptive control, fault detection, and state estimation for robotic automated systems intended to function dependably in complicated contexts, efficient techniques are described in this study. This paper proposed a fault detection and state estimation using Accelerated Gradient Descent based support vector machine (AGDSVM) and gaussian filter (GF) in automatic control systems. The Proposed system is called (AGDSVM + GF). The proposed system is evaluated with the following metrics accuracy, fault detection rate, state estimation rate, computation time, error rate, and energy consumption. The result shows that the proposed system is effective in fault detection and state estimation and provides intelligent control automatic control.

A possible approach to the synthesis of behavior of robots used in groups

This paper studies tactical level of control in multi-agent robotics. We propose to use analytical-synthetic modular approach to synthesize tactics of robots acting in group. Within it we investigate alternative scenarios (i.e. modules) which present separate situations consisting in its turn of primitive action. Within this paper we give an idea regard-ing application of this approach on a base of example of UAVs invasion.

Large Curvature Bending Measurable Fiber-Optic Neurons for Multi-Joint Bending Perception

In recent years, dexterous robotic hands have become an indispensable part of intelligent equipment. The introduction of bionic neurons that provide finger motion perception will improve the accuracy of joint control. Here, series- and parallel-integrated inner cladding fiber Bragg gratings (ICFBG) act as bionic neurons to realize multi-joint bending perception in a robotic finger by measuring the reflective intensity changes of the ICFBGs. The fiber-optic neurons (FON) were fabricated using femtosecond laser point-by-point technology in the inner cladding of bend-insensitive single-mode fiber (BIF). The proposed FONs can measure large curvature bending up to 250 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> , with a bending sensitivity of ∼0.07 dB/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> and a measuring resolution of 0.4 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-1</sup> . Additionally, vector bending sensing can be achieved using the two pairs of ICFBGs that are orthogonally distributed to each other. The FONs are insensitive to temperature and axial strain. Furthermore, the proposed FONs were fixed to the knuckles of a robotic finger for multi-joint bending perception. The results show that FONs can potentially be used in robotic gesture recognition and control systems.

Modular robotic manipulator and ground assembly system for on-orbit assembly of space telescopes

In the field of astronomical observation, large space telescopes are key tools for deep space exploration. However, the aperture of space telescopes is limited by the ability to fabricate large monolithic mirrors or lenses and by severe restrictions on the overall volume and mass capacity of the launch vehicle. The use of robots for autonomous assembly while in orbit is a promising option for the deployment of large-aperture space telescopes. In this paper, we describe a modular robotic manipulator that offers high precision and flexibility. A space telescope ground assembly system is then designed, the key components of which are a redundant robotic manipulator and a reconfigurable telescope unit. The ground-based assembly of the space telescope is implemented through a combination of trajectory planning, visual perception, and robotic supple control techniques, validating the ability of the robotic manipulator to automatically assemble modular space telescopes. Finally, the developed robotic manipulator is used to conduct ground assembly demonstration experiments, and the modular space telescope is assembled according to the assembly task plan, accumulating technical experience for future space telescope assembly tasks in orbit.