Movement Of Robot Research Articles

ABDALAMEER. DEVELOPMENT OF AUTONOMOUS MOBILE ROBOTS CONTROL SYSTEMS

The development of an efficient control system for mobile autonomous robots is explored. It delineates key stages in this process, starting with the necessity of precise trajectory planning followed by the creation of a mathematical model for controlling the robotic device. Subsequently, the implementation of navigation algorithms in the management system is emphasized. The importance of conducting comprehensive error analysis to address discrepancies during robot movement is underscored. Post the primary phase of control system creation, evaluating the cost-effectiveness and ensuring workplace safety are deemed crucial management steps. Localization emerges as a pivotal factor enabling the robot's effective autonomous movement relative to its target position. This, in turn, aids in orienting the robot within its external environment. Spatial information acquisition is underscored through the significance of traffic planning, cartography and specialized robotics sensors. Furthermore, the article delves into proprioceptive sensors, detailing their components, such as optical and electrical conductor sensors operating via the Hall effect discovered in 1879. It provides a formula to accurately ascertain conductivity state, involving parameters like hole and electron concentration, electron and hole mobility and elementary charge. In essence, the control system parallels the human brain, requiring a recognition system, action planning and execution functions. Navigation is identified as a critical element facilitating the robot's movement in three-dimensional space. The article integrates technical intricacies about proprioceptive sensors, highlighting their pivotal role within robot control systems.

Beyond simulation: Unlocking the frontiers of humanoid robot capability and intelligence with Pepper's open-source digital twin

This research paper presents a high-fidelity, open-source digital-twin of the Pepper robot developed within the framework of the Robot Operating System 2 (ROS 2) for better simulation realism in complex tasks of machine learning. We developed a dedicated, custom ROS 2 package with modern simulation tools, such as Gazebo Sim, MoveIt 2, Rviz2, that brings complete, realistic environments in line with the exact behaviors and interactions of robots in reality. Better accuracy of the physical movement of Pepper robot's simulation was shown on the digital twin, validated by the Choregraphe software and real robot performance, to be a strong platform of collaboration and further research by the community. This development greatly pushes the envelope of human-like humanoid robotics further by offering a scaled, flexible, and plausible training environment conducive to integrating complex algorithms of robot learning and interaction capabilities.

Agar-based Phantom for Evaluating Targeting of High-intensity Focused Ultrasound Systems for Breast Ablation

Aim: Phantoms are often utilized for the preclinical evaluation of novel high-intensity focused ultrasound (HIFU) systems, serving as valuable tools for validating efficacy. In the present study, the feasibility of a homogeneous agar-based breast-shaped phantom as a tool for the preclinical evaluation of HIFU systems dedicated to breast cancer was assessed. Specifically, the effect of the increased phantom curvature on temperature increase was examined through sonications executed on two sides having varied curvatures. Materials and Methods: Assessment was performed utilizing a 1.1 MHz focused transducer. Sonications on the two phantom sides were executed at varied acoustical power in both a laboratory setting and inside a 1.5 T magnetic resonance imaging scanner. Sonications were independently performed on two identical phantoms for repeatability purposes. Results: Temperature changes between 7.1°C–34.3°C and 5.1°C–21.5°C were recorded within the decreased and increased curvature sides, respectively, for acoustical power of 3.75–10 W. High-power sonications created lesions which were approximately symmetrically formed around the focal point at the decreased curvature side, while they were shifted away from the focal point at the increased curvature side. Conclusions: The present findings indicate that increased curvature of the breast phantom results in deformed focal shapes and decreased temperatures induced at the focal area, thus suggesting treatment correction requirements in the form of focus control or accurate robotic movement. The developed breast-shaped phantom can be utilized as an evaluation tool of HIFU systems dedicated to breast cancer since it can visually verify the efficacy of any system.

Magnetic arthropod soft robot with triboelectric bionic antennae for obstacle identifying and avoidance

Magnetic soft robots exhibit attractive advantages such as nontethered constraints and remote control capabilities, with broad prospects in environmental exploration, minimally invasive surgery and other fields. However, constrained by the magnetic control mode and the high flexibility of the soft materials, achieving rapid movement and perception-feedback of magnetic soft robots are still challenging. Therefore, this paper aims to draw inspiration from biomimetic joint structures to design and fabricate a magnetic arthropod soft robot capable of rapid cyclic extension-contraction deformation under an alternating magnetic field. First, a type A magnetic arthropod soft robot (MASR-A) is developed with a speed up to 1.4 body length per second (BL/s) with three-joint-unit. Next, we designed a type B magnetic arthropod soft robot (MASR-B) that offers higher flexibility and deformation adaptability with bodies owing five-joints structure. Then, inspired by snail’s tentacles, we developed bionic antennae based on triboelectric tactile sensors (TTSs), which can convert external mechanical collisions into electrical signals output, with a sensitivity of up to 0.13 V KPa−1. By mounting the bionic antennae on the head of MASR-B to realize the magnetic soft robot’s sensing function, enabling MASR-B to detect collisions with obstacles of various materials (PMMA, Resin, PLA and etc.). Particularly, the bionic antennae consists of two completely independent triboelectric sensing units (LTTS and RTTS). The signals from these two TTSs are received and identified by a microcontroller to determine the direction of obstacle relative to the robot. The relay is then controlled to alter the magnetic field generated by the electromagnetic coils to enable MASR-B to avoid obstacle. This paper provides a biomimetic design approach for the rapid movement and flexible sensing of magnetic soft robots.

Ground Adaptability of Crawler Mobile Robots with Sub-Crawler Rotary Joint Compliance

Remote-controlled mobile robots are expected to be used in difficult- or impossible-to-access environments for inspection workers and responders, such as in investigations and search activities at accident/disaster sites and inspection/investigation work at plants/infrastructure. Among ground mobile robots, crawler mobile robots with sub-crawlers (also known as ground-adaptive crawler robots) excel at in-ground adaptability and stack escape; however, their operators require advanced remote-control technology and experience. Therefore, the introduction of semi-autonomous control to assist the operator is required. In this study, the principle of the pushing-up sequence and the possibility of mobiligence emerging from interaction with obstacles caused by the robot movement were described. In addition, the sub-crawler rotary joint’s compliance, which significantly contributes to ground adaptability, was hypothesized, and a compliance control system design method that uses the sub-crawler constraint angle as a design condition was proposed. It was confirmed that the model robot for the evaluation, which used the proposed method, could adapt to unknown obstacles without measuring their height and shape and traverse them based on experimental results. In addition, based on the numerical calculation results, it was determined that the optimum solution for the restriction angle of the sub-crawler was approximately 35°–50° from the perspective of propulsive force and tumble stability.

Real-Time Remote-Controlled Human Manipulation Medical Robot Using IoT Module

INTRODUCTION: Innovative robotics and advanced computer vision technology converge in the Human Manipulation-Controlled Robot, utilized for medical applications. The robot operates through human gestures, and includes a camera module for real-time visual feedback, enhancing its functionality and user interaction. OBJECTIVES: The primary goal of the research was to harness the natural expressiveness of human gestures to provide a more intuitive and engaging method of controlling medical robots. The focus is on enabling precise control through programmed responses to specific gestures, ensuring effective interaction with medical tasks. METHODS: The robot’s hardware configuration consists of a mobile platform with motorized components, an ESP32 module, gesture recognition sensors and a camera modules. The ESP32 module interprets signals from gesture recognition sensors to execute precise commands for the robot's movements and actions. Simultaneously, the camera module captures live footage, providing visual feedback through an intuitive interface for seamless interaction. RESULTS: The Human Manipulation-Controlled Robot has been successfully developed, featuring a fetch arm capable of autonomous movement and object manipulation. This research address critical needs in medical centers, demonstrating the feasibility of using only minimalistic EEG electrode wireless transmission to operate a robot effectively. CONCLUSION: Through the provision of a more intuitive and engaging method of controlling and interacting with medical robots, this innovation has the potential to significantly improve user experience. It represents a most important development in medical robotic vehicles, enhancing user experience and operational efficiency through advanced human-robot interaction techniques.

Entertainment robots based on internet of things and artificial intelligence in the process of student stress and emotion recognition

For different entertainment robot tasks and action requirements, this article adopts multiple control algorithms and optimizes the algorithms. There are certain errors in the movement and interaction process of entertainment robots, which can affect the control effect and user experience of the robots. Therefore, the study models and analyzes the errors of robots by establishing error models, and proposes corresponding correction strategies to correct the errors, thereby improving the control accuracy and reliability of entertainment robots. In terms of identifying and alleviating student stress emotions, multimodal fusion comprehensively utilizes multiple sensors and data sources to obtain emotional information of students, more accurately identifying and understanding their stress emotions. Based on these analysis results, entertainment robots can adopt corresponding relief strategies to help students alleviate stress emotions. By comparing the changes in stress emotions and student feedback before and after the experiment, entertainment robots based on the Internet of Things and artificial intelligence have potential application value in the process of student stress emotion recognition. The remote control system and optimized control effect of robots provide flexibility and efficiency for robot operation. The evaluation results of stress relief effect show that entertainment robots have a positive impact on the relief of student stress emotions.

Regressionsbasierte Ermittlung von Ausführungszeiten in Mensch-Roboter-Kollaboration

Abstract The intensifying competitive pressures in manufacturing companies, combined with the expanding array of product variants, are propelling the integration of human-robot collaboration in operational frameworks. Effectively planning assembly processes demands particularly streamlined methods for assessing execution times, especially in the initial planning phases. While predetermined motion time systems are designed for human tasks, this contribution puts forth an approach for the time-economic analysis of robot movements without resorting to simulation.

An improved genetic algorithm for robot path planning

The conventional genetic algorithm (GA) for path planning exists several drawbacks, such as uncertainty in the direction of robot movement, circuitous routes, low convergence rates, and prolonged search time. To solve these problems, this study introduces an improved GA-based path-planning algorithm that adopts adaptive regulation of crossover and mutation probabilities. This algorithm uses a hybrid selection strategy that merges elite, tournament, and roulette wheel selection methods. An adaptive approach is implemented to control the speed of population evolution through crossover and mutation. Combining with a local search operation enhances the optimization capability of the algorithm. The proposed algorithm was compared with the traditional GA through simulations, demonstrating shorter path lengths and reduced search times.

A contact sensor-free framework for ground reaction force observation in heavy-legged robots considering unknown loads

As a direct response to robot movements and load distributions, the ground reaction force (GRF) is pivotal for heavy-legged robot (HLR) applications. This study presents a technical framework for GRF monitoring in an electric cylinder-driven HLR, eliminating the need for information on body motion, load distributions, and measured servo outputs. Traditional joint space-based dynamics are extended to include servo currents, compensating for the influence of unknown servo outputs. An essential contribution is incorporating the impact of floating bases and unknown loads into a virtual spatial force (VSF) applied to the HLR hip joint. The VSF is obtained through the nonlinear disturbance observer when the HLR is in a stable contact phase. Subsequently, a high-order GRF observer (HOGO), compensated with VSF, enables GRF observations without the pre-acquired body movement and load distribution data. In contrast to conventional GRF observations, the proposed framework could determine virtual payloads acting on the hip joint while ensuring precise GRF monitoring without requiring supplementary sensors. The GRF observations of the proposed framework are experimentally superior to those of the conventional methods with unknown HLR body motion and load information.

Моделювання системи стабілізації обладнання мобільного робота в умовах руху по місцевості з нахилом та нерівностями

The article is devoted to the development of a system of stabilization and guidance of equipment, which is installed on land-based small-scale mobile robots of the wheel type. During the operation of such objects, problems arise due to modes of movement, over-coming obstacles, movement on terrain with different types and profiles of the terrain. During such a movement, there are disturbances in the place of installation of the equip-ment, which leads to its deviation from the horizon plane. A stabilization system is used to ensure accuracy requirements in a wide range of movement speeds and operating condi-tions of mobile robots. In the article, the simulation of the stabilization system under the action of external disturbances caused by the influence of the movement of the mobile robot through terrain with a complex profile in combination with the inclination of the different steepness of the movement surface was carried out. A stabilization system simulation scheme with a basic structure and with an additionally introduced proportional-integrating controller was developed, realizations of various types of disturbances were generated by a combination of different inclination angles and different road profile classes. As a result of the simula-tion, the time realizations of the system’s response to the generated disturbances, which are the dependences of the stabilization error, were obtained, the root mean square devia-tions and the maximum value of the angle of deviation of the place of installation of the equipment from the horizon plane were determined. The results of the study showed that the system with an additionally introduced pro-portional-integrating regulator provides acceptable stabilization error values under the con-ditions of action of all considered types of disturbances. The obtained results will be used for the development of an adaptive digital system of stabilization of equipment installed on small-sized mobile robots of the wheel type.

Precise Obstacle Avoidance Movement for Three-Wheeled Mobile Robots: A Modified Curvature Tracking Method

This paper proposes a precise motion control strategy for a three-wheeled mobile robot with two driven rear wheels and one steered front wheel so that an obstacle avoidance motion task is able to be well implemented. Initially, the motion laws under nonholonomic constraints are expounded for the three-wheeled mobile robot in order to facilitate the derivation of its dynamic model. Subsequently, a prescribed target curve is converted into a speed target through the nonholonomic constraint of zero lateral speed. A modified dynamical tracking target that is aligned with the dynamic model is then developed based on the relative curvature of the prescribed curve. By applying this dynamical tracking target, path tracking precision is enhanced through appropriate selection of a yaw motion speed target, thus preventing speed errors from accumulating during relative curvature tracking. On this basis, integral sliding mode control and feedback linearization methods are adopted for designing robust controllers, enabling the accurate movement of the three-wheeled mobile robot along a given path. A theoretical analysis and simulation results corroborate the effectiveness of the proposed trajectory tracking control strategy in preventing off-target deviations, even with significant speed errors.

New Approach to Planning the Complex Movements of 6-DOF Industrial Robot Subjected to Acceleration Constraints

New Approach to Planning the Complex Movements of 6-DOF Industrial Robot Subjected to Acceleration Constraints

Indoor Quadruped Robot Navigation Algorithm Based on ORB-SLAM

In recent years, the development of artificial intelligence, big data, and the Internet of Things technologies has revealed unprecedented potential and value in mobile robots across various sectors of automation and intelligence. Among these, quadruped robots have shown unique applications in the domain of indoor security and inspection, as they can navigate multi-storey buildings by ascending and descending stairs. To enhance the stability of visual navigation in indoor inspection environments for quadruped robots, this paper builds on the ORB-SLAM framework. It introduces the AGC algorithm to address the issue of reduced feature point extraction during night patrols, thereby increasing the number of feature points extracted. Additionally, to tackle the problem of high oscillation intensity during the movement of quadruped robots, the EKF algorithm has been incorporated for sensor fusion with IMU, enhancing the robustness of visual navigation. This has been validated through physical experiments.

Pillar electrodes embedded in the skeletal muscle tissue for selective stimulation of biohybrid actuators with increased contractile distance.

Electrodes are crucial for controlling the movements of biohybrid robots, but their external placement outside muscle tissue often leads to inefficient and non-selective stimulation of nearby biohybrid actuators. To address this, we propose embedding pillar electrodes within the skeletal muscle tissue, resulting in enhanced contraction of the target muscle without affecting the neighbor tissue with a 4 mm distance. We use finite element method simulations to establish a selectivity model, correlating the VIE(volume integration of electric field intensity within muscle tissue) with actual contractile distances under different amplitudes of electrical pulses. The simulated selective index closely aligns with experimental results, showing the potential of pillar electrodes for effective and selective biohybrid actuator stimulation. In experiments, we validated that the contractile distance and selectivity achieved with these pillar electrodes exceed conventional Au rod electrodes. This innovation has promising implications for building biohybrid robots with densely arranged muscle tissue, ultimately achieving more human-like movements. Additionally, our selectivity model offers valuable predictive tools for assessing electrical stimulation effects with different electrode designs.

Automatic Patient Injection Robot With 6 Degrees of Freedom by 3D Simulation Using Webots Software

The manipulator robot is one of the most widely used robots in the industry. The movement of the robot manipulator in this sector is a mechanical system that requires a mathematical modeling method to represent its geometric aspects and manipulate its dynamic parts. One of the most critical industries is healthcare. In this paper, a simulation is carried out on one of the factors, namely patient injection. The research results from this simulation used Webots software and was successfully simulated using a 6-degree-of-freedom robot. The positions simulated in several movements include: gripper position opens to take the needle, gripper closes to take the needle, the needle is held and directed to the patient, the needle is inserted into the patient area and pulled out again, returning to its initial position by placing the needle. However, this simulation does not add a needle so that only the 3D simulated robot movement can move by entering the expected position value to inject the patient according to the target.

Innate Orientating Behavior of a Multi-Legged Robot Driven by the Neural Circuits of C. elegans.

The objective of this research is to achieve biologically autonomous control by utilizing a whole-brain network model, drawing inspiration from biological neural networks to enhance the development of bionic intelligence. Here, we constructed a whole-brain neural network model of Caenorhabditis elegans (C. elegans), which characterizes the electrochemical processes at the level of the cellular synapses. The neural network simulation integrates computational programming and the visualization of the neurons and synapse connections of C. elegans, containing the specific controllable circuits and their dynamic characteristics. To illustrate the biological neural network (BNN)'s particular intelligent control capability, we introduced an innovative methodology for applying the BNN model to a 12-legged robot's movement control. Two methods were designed, one involving orientation control and the other involving locomotion generation, to demonstrate the intelligent control performance of the BNN. Both the simulation and experimental results indicate that the robot exhibits more autonomy and a more intelligent movement performance under BNN control. The systematic approach of employing the whole-brain BNN for robot control provides biomimetic research with a framework that has been substantiated by innovative methodologies and validated through the observed positive outcomes. This method is established as follows: (1) two integrated dynamic models of the C. elegans' whole-brain network and the robot moving dynamics are built, and all of the controllable circuits are discovered and verified; (2) real-time communication is achieved between the BNN model and the robot's dynamical model, both in the simulation and the experiments, including applicable encoding and decoding algorithms, facilitating their collaborative operation; (3) the designed mechanisms using the BNN model to control the robot are shown to be effective through numerical and experimental tests, focusing on 'foraging' behavior control and locomotion control.

Real-time precision detection algorithm for jellyfish stings in neural computing, featuring adaptive deep learning enhanced by an advanced YOLOv4 framework

IntroductionSea jellyfish stings pose a threat to human health, and traditional detection methods face challenges in terms of accuracy and real-time capabilities.MethodsTo address this, we propose a novel algorithm that integrates YOLOv4 object detection, an attention mechanism, and PID control. We enhance YOLOv4 to improve the accuracy and real-time performance of detection. Additionally, we introduce an attention mechanism to automatically focus on critical areas of sea jellyfish stings, enhancing detection precision. Ultimately, utilizing the PID control algorithm, we achieve adaptive adjustments in the robot's movements and posture based on the detection results. Extensive experimental evaluations using a real sea jellyfish sting image dataset demonstrate significant improvements in accuracy and real-time performance using our proposed algorithm. Compared to traditional methods, our algorithm more accurately detects sea jellyfish stings and dynamically adjusts the robot's actions in real-time, maximizing protection for human health.Results and discussionThe significance of this research lies in providing an efficient and accurate sea jellyfish sting detection algorithm for intelligent robot systems. The algorithm exhibits notable improvements in real-time capabilities and precision, aiding robot systems in better identifying and addressing sea jellyfish stings, thereby safeguarding human health. Moreover, the algorithm possesses a certain level of generality and can be applied to other applications in target detection and adaptive control, offering broad prospects for diverse applications.

Contact detection in a rib-reinforced vacuum driven actuator with an embedded CSR bridge sensor

ABSTRACT Stable control and accurate sensing are necessary for the safe operation of soft robots, and flexible sensors that can follow the flexible movements of soft robots are needed. In this paper, flexible bending sensors are embedded in a rib-reinforced vacuum-driven actuator with no risk of rupture, considering the application of flexible sensors to human cooperative work and wearable devices. The bending sensor is a bridge circuit with conductive silicone rubber (CSR) for variable resistance. The CSR bridge sensor consists of four CSRs that capture deformation and a circuit with a conductive coating spray printed on a laminated sheet. The sensor value is the output voltage divided by the input voltage, and the effects of the drift and input voltage variations are canceled out. The bending sensor embedded in the silicone actuator has a linear relationship, and the rib-reinforced vacuum driven actuator provides angle PID control for continuously changing reference inputs by feeding back sensor estimated angle. Furthermore, contact detection was established by using the pressure estimated from the CSR bridge sensor and the pressure sensor error as the threshold. The algorithm provides reliable contact detection and force estimation with actuator response thresholds and anti-chattering.

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.