Robot Motion Research Articles

Shortest coordinated motions for square robots

We study the problem of determining minimum-length coordinated motions for two axis-aligned square robots translating in an obstacle-free plane: Given feasible start and goal configurations (feasible in the sense that the two squares are interior disjoint), find a continuous motion for the two squares from start to goal, comprising only robot-robot collision-free configurations, such that the total Euclidean distance traveled by the two squares is minimal among all possible such motions. In this paper we present an adaptation of the tools developed for the case of disks to the case of squares. We show that in certain aspects the case of squares is more complicated, requiring additional and more involved arguments over the case of disks.

Vibration stimulation enhances robustness in teleoperation robot system with EEG and eye-tracking hybrid control

IntroductionThe application of non-invasive brain-computer interfaces (BCIs) in robotic control is limited by insufficient signal quality and decoding capabilities. Enhancing the robustness of BCIs without increasing the cognitive load remains a major challenge in brain-control technology.MethodsThis study presents a teleoperation robotic system based on hybrid control of electroencephalography (EEG) and eye movement signals, and utilizes vibration stimulation to assist motor imagery (MI) training and enhance control signals. A control experiment involving eight subjects was conducted to validate the enhancement effect of this tactile stimulation technique.ResultsExperimental results showed that during the MI training phase, the addition of vibration stimulation improved the brain region activation response speed in the tactile group, enhanced the activation of the contralateral motor areas during imagery of non-dominant hand movements, and demonstrated better separability (p = 0.017). In the robotic motion control phase, eye movement-guided vibration stimulation effectively improved the accuracy of online decoding of MI and enhanced the robustness of the control system and success rate of the grasping task.DiscussionThe vibration stimulation technique proposed in this study can effectively improve the training efficiency and online decoding rate of MI, helping users enhance their control efficiency while focusing on control tasks. This tactile enhancement technology has potential applications in robot-assisted elderly care, rehabilitation training, and other robotic control scenarios.

Cable tension minimization of a redundant cable-driven robot with flexible end-effector

Abstract The operation of robots in narrow spaces is a challenge in aerospace engineering. In order to fulfill the working requirements within some specific space, this paper proposes a design and optimization method for a redundant flexible cable-driven robot. According to the relevant restrictions of the robot's structure, a distributed flexible-cable driving strategy is investigated with a focus on minimizing the maximum tension of every cable. Through symmetry group and physical group, both the search task and computational cost are greatly reduced. Therefore, this algorithm is available to reduce the calculation and the selection of flexible cables for the lightweight design of a robot. Based on the design and optimization method proposed in this article, an experimental prototype was manufactured and systematically tested. The results show that the robot's motion and control parameters meet theoretical expectations very well, validating the effectiveness and efficiency of the proposed method. Consequently, this method can be utilized to similar flexible cable-driven robots for tension optimization, material selection, and structural design of cables.

A spectral conjugate gradient method for motion control of robotic manipulators

PurposeThe purpose of this paper is to introduce a spectral conjugate gradient method specifically designed to solve unconstrained optimization problems, particularly concerning the motion control of a two-joint planar robotic manipulator.Design/methodology/approachOur approach integrates a spectral parameter derived from the strong Wolfe line search with the conjugate gradient method of Dai and Kou. This approach is evaluated through computational simulations, demonstrating its effectiveness in motion control scenarios. Our design and methodology underscore a rigorous analytical framework combined with practical, application-oriented testing to validate the proposed algorithm’s efficiency and applicability in robotic motion control.FindingsWe were able to find an effective way to select the spectral parameter, which is crucial for the optimization process in robotic motion control. This improved selection process directly contributes to enhanced numerical performance. Moreover, the implementation of the proposed method in motion control of a two-joint planar robotic manipulator demonstrates its effectiveness. This suggests that the approach not only works theoretically but also proves to be viable in practical, real-world applications. Furthermore, the findings indicate that this approach could potentially be adapted or extended to other types of robotic systems or motion control challenges.Originality/valueThis paper introduces an enhancement to the spectral conjugate gradient method, marked by a clever parameter selection strategy originating from the strong Wolfe line search, tailored specifically for robotic motion control.

Multi-objective path integral policy improvement for learning robotic motion

Abstract This paper proposes a new multi-objective reinforcement learning (MORL) algorithm for robotics by extending policy improvement with path integral ( $$\text {PI}^2$$ PI 2 ) algorithm. For a robot motion acquisition problem, most existing MORL algorithms are hard to apply, because of the high-dimensional and continuous state and action spaces. However, policy-based algorithms such as $$\text {PI}^2$$ PI 2 can be applied to solve this problem in single-objective cases. Based on the similarity of $$\text {PI}^2$$ PI 2 and evolution strategies (ESs) and the fact that ESs are well-suited for multi-objective optimization, we propose an extension of $$\text {PI}^2$$ PI 2 and some techniques to speed up the learning. The effectiveness is shown via numerical simulations.

GPT-Driven Gestures: Leveraging Large Language Models to Generate Expressive Robot Motion for Enhanced Human-Robot Interaction

GPT-Driven Gestures: Leveraging Large Language Models to Generate Expressive Robot Motion for Enhanced Human-Robot Interaction

Dynamics of a vibro-impact capsule robot self-propelling in the small intestine with multiple circular folds

Abstract Despite advancements in capsule endoscopy, existing models lack controllable motion, limiting their effectiveness in navigating complex folds and the variable stiffness of intestinal tissue. To address this gap, this study investigates the motion of a self-propelled capsule robot designed to overcome the limitations of current endoscopic technology by actively navigating the small intestine, particularly considering the influence of multiple circular folds. In this research, we employed the dynamic model developed by Yan et al. (Eur. J. Mech. A-Solid, 105:105233, 2024). The model was validated using two-dimensional finite element modelling and an experimental setup with an artificial gut model, showing high consistency with theoretical predictions. Our analysis focuses on key parameters, such as fold height, fold thickness, and tissue stiffness, finding that higher and thinner folds on harder tissues present greater resistance. This increased resistance necessitates the application of greater force by the capsule for effective navigation. These findings suggest that while self-propelled capsule robot can achieve consistent motion under various conditions, its movement may become irregular in complex physiological environments. This underscores the need for optimising advanced control strategies to enhance their performance. By improving navigation through the small intestine, this technology has the potential to enhance the accuracy and reliability of gastrointestinal diagnoses, leading to better clinical outcomes and advancements in non-invasive diagnostic techniques.

Advances in Zeroing Neural Networks: Bio-Inspired Structures, Performance Enhancements, and Applications

Zeroing neural networks (ZNN), as a specialized class of bio-Iinspired neural networks, emulate the adaptive mechanisms of biological systems, allowing for continuous adjustments in response to external variations. Compared to traditional numerical methods and common neural networks (such as gradient-based and recurrent neural networks), this adaptive capability enables the ZNN to rapidly and accurately solve time-varying problems. By leveraging dynamic zeroing error functions, the ZNN exhibits distinct advantages in addressing complex time-varying challenges, including matrix inversion, nonlinear equation solving, and quadratic optimization. This paper provides a comprehensive review of the evolution of ZNN model formulations, with a particular focus on single-integral and double-integral structures. Additionally, we systematically examine existing nonlinear activation functions, which play a crucial role in determining the convergence speed and noise robustness of ZNN models. Finally, we explore the diverse applications of ZNN models across various domains, including robot path planning, motion control, multi-agent coordination, and chaotic system regulation.

A multi-task deep reinforcement learning framework based on curriculum learning and policy distillation for quadruped robot motor skill training

A multi-task deep reinforcement learning framework based on curriculum learning and policy distillation for quadruped robot motor skill training

Review of On-Orbit Assembly Technology with Space Robots

With the accelerated pace of human space exploration and the progress of other related researches, there is an increasingly urgent demand for space infrastructure, equipment, and diversified spacecraft construction for space missions, and how to efficiently, intelligently, and autonomously build corresponding facilities and equipment on orbit according to the functional requirements of different missions has become a great challenge in the field of space technology research. As an important means of automated manufacturing, the construction of on-orbit assembly systems centered on space robotics has become an emerging development trend. In view of its importance, space agencies and research institutes have successively proposed and developed a series of related programs. In order to comprehensively understand the progress of on-orbit assembly with space robots (OASR) and scientific problems involved, this paper investigates the current status of research and technological development in OASR. Firstly, the significance of OASR for space exploration and other space missions is analyzed. Secondly, the existing classification forms of on-orbit assembly are outlined and a classification idea is proposed from the point of view of the combination of space robot motion capability and assembly goals. Thirdly, the research and development status of OASR in the United States, Europe, Canada, Japan, and China is investigated. Then, based on a review of the literature on space robots to realize on-orbit assembly in space facilities, some of the key technologies involved are reviewed and discussed. Finally, this paper discusses and looks ahead to the future development trend and application prospect of the technology of OASR, reveals and explains the crucial position it occupies as well as the important role it can play in the process of human space exploration, and is expected to provide useful references for the in-depth research and development of future on-orbit assembly technology.

The Obstacle Avoidance Path Planning of Six Degrees of Freedom

In the rapidly advancing field of robotics, motion planning and control of robotic arms have emerged as critical research areas, driven by their widespread applications in manufacturing, healthcare, and automation. This paper focuses on single and six-degree-of-freedom (DOF) robotic arm systems, implementing a motion control method based on S-type velocity planning. S-type planning divides the arms motion into three distinct phases: acceleration, uniform velocity, and deceleration. This approach ensures smooth transitions between phases, avoids abrupt acceleration changes, and significantly reduces vibrations and mechanical impacts, thereby enhancing the systems overall performance and longevity. The paper derives the mathematical model of S-type planning, providing a detailed analysis of velocity, acceleration, and jerk curves. Experimental results demonstrate its effectiveness in improving motion stability for single-DOF arms, showcasing its ability to deliver precise and reliable control. For more complex six-DOF systems, an innovative joint velocity coordination algorithm is introduced. This algorithm addresses the inherent joint coupling challenges, ensuring overall motion coordination and improving the trajectory accuracy of the end-effector. Rigorous simulation experiments validate the methods superiority in enhancing stability, minimizing vibrations, and maintaining high precision in multi-DOF systems. In conclusion, this research provides robust theoretical foundations and practical insights for multi-DOF robotic arm motion planning. The proposed S-type velocity planning method and joint coordination algorithm offer significant potential for real-world engineering applications, paving the way for further advancements in robotics technology. These contributions are expected to play a pivotal role in optimizing robotic systems for diverse industrial and scientific applications.

Rancang Bangun Pengembangan Robot Pembersih Sampah Berbasis Internet of Thing (IOT) Untuk Pemantauan dan Pengontrolan Jarak Jauh.

The increasing waste problem requires innovative solutions that are efficient and sustainable. This study aims to design and build an Internet of Things (IoT) based garbage cleaning robot that can be monitored and controlled remotely. This system is designed by utilizing a microcontroller as the main brain, sensors to detect the presence of garbage, and an IoT-based communication module that allows monitoring and control of the robot via mobile devices or the web. Based on the results of the analysis and testing carried out, this study shows that the use of ESP8266 in the RC Trans Robot motor control with the Blynk application has succeeded in significantly increasing system efficiency. The system successfully responds to user input via a mobile application and can control the movement of the robot in real-time. The performance of the robot system controlled via a WiFi network shows good stability in various test scenarios. The robot can operate effectively within a distance that matches the range of the WiFi network, with fast control response and reliable communication.

Learning Rate of the Model Algorithm for Iterative Learning Control in Lung Nodule Surgical Continuum Robot Systems

ABSTRACTDuring surgical operations, the distal end of lung nodule surgical robots is frequently confronted with diverse and intricate disturbances, thereby posing significant challenges for nonlinear control of such continuum robot systems. The continuum robot has a complex nonlinear dynamic model, and the coupling between the joints will affect each other, which makes the joint control of the continuum robot difficult. In addition, the motion of the continuum robot also needs a real‐time control strategy. Based on the above analysis, this paper proposes a nonlinear iterative learning method, which is grounded in model algorithmic learning rates, for the control of the distal end of a surgical robot utilized in pulmonary nodule operations. This method not only considers the control error and its higher derivative, but also includes the parameters of the system model. Then, based on the learning rate determined by the model algorithm and the actual control input from the current iteration, the control input for the next iteration is calculated, thereby advancing the iterative learning process. Finally, the stability of the entire nonlinear iterative learning process is proved by the spectral radius condition under the global Lipschitz condition. The effectiveness and robustness of the proposed method have been verified through MATLAB/Simulink, demonstrating high precision and superior performance.

Plasma-Induced Wrinkle-Crack Dual Structure for Robust Directional Strain Sensing in Dynamic Motion Perception.

Flexible multi-directional sensors hold vast potential for complex application scenarios, yet creating sensitive, stable, and linear strain sensors capable of flexibly detecting multi-directional strain remains a significant challenge. Here, a plasma-induced wrinkle-crack dual structure is introduced for multi-directional force detection of high-reliability flexible strain sensors. By combining pre-stretching and oxidative plasma bombardment, a multi-layer structure exhibits film stress engineering with varying elastic moduli established on the surface of the elastomer. The rigid silicon oxide hardened layer induces shear film stress at the pre-stretched interface. Periodic wrinkles are generated upon release from pre-stretching, which not only suppresses crack propagation but also significantly enhances the linearity between the signal and applied force, thanks to the conductive network's wrinkles. By adjusting the direction of pre-stretching under plasma treatment, the morphology and orientation of the wrinkles on the conductive sensitive layer can be effectively controlled. The coexisting parallel wrinkle and perpendicular crack structures impart anisotropic properties, significantly improving the sensor's directional detection capabilities. With a high gauge factor (GF = 454), excellent cyclic durability (over 100000 cycles), and multi-directional force detection capability, this sensor demonstrates promising applications in wearable electronics and robot motion detection, positioning it as a next-generation flexible strain sensor.

A Laser Thermal-Curing Printing: Integrating Fabrication, Reparability, Reconfigurability, and Reprogrammability for Magnetic Soft Robots.

Untethered magnetic soft robots can broaden the working scenarios of robots and have numerous potential applications in space exploration, industry, and medicine. However, existing magnetic soft robots face challenges such as limited reparability, difficulty expanding functions, and difficulty adjusting motion mode. Herein, an efficient and comprehensive laser thermal-curing printing method is proposed for magnetic soft robots. In this method, the directionality and photothermal effect of the infrared laser and thermal-curing property of thermosetting resin are utilized to achieve efficient fabrication, precise repair, and seamless reconstruction of thermosetting resin-based magnetic soft robots. Besides, the method enables reprogrammability of magnetic soft robots by exploiting photothermal-induced demagnetization. Further, the laser thermal-curing printing method is applied to repair a gyro robot for controlled movement; to reconstruct an underwater robot for salvaging cargo, a robot for repairing electrical circuit, and a wheel robot with three-dimensional structure; and to reprogram the motion of a six-leaf magnetic soft robot. These applications demonstrate that the laser thermal-curing printing method achieves the integration of fabrication, reparability, reconfigurability, and reprogrammability for soft robot, which is expected to drive a paradigm shift in soft robotics manufacturing and provide a groundbreaking strategy for fabricating magnetic soft robots with complex structures.

Effects of Mobile Robot Passing-Motion Path Curvature on Human Affective States in a Hallway Environment

Abstract For social robots to move in ways that are socially desirable to the people that they encounter, it is necessary to understand the types of robot motions that people find more or less appealing. An experiment was conducted in which participants ( $$N=20$$ N = 20 ) encountered a mobile robot in a virtual reality hallway environment. The robot passed them following trajectories shaped like cubic Bezier curves. The sharpness of curvature of the robot motions was varied, as was the distance from the person at which the passing-motion was initiated. Participants scored each motion both on how arousing and pleasurable they found the experience of being passed by the robot to determine the affective state induced by the robot motion. Our data show that the curvature of passing-motions has an effect on the affective state of the person being passed, while other proxemic constraints are held constant. Participants respond more positively to paths that have moderate curvature, and more negatively to passing-motions that are either too sharply curved, or too straight. This finding enables the development of path planning algorithms that respect the empirically–defined path curvature constraint to improve human acceptability in this context.

Learning from Octopuses: Cutting-Edge Developments and Future Directions.

This paper reviews the research progress of bionic soft robot technology learned from octopuses. The number of related research papers increased from 760 in 2021 to 1170 in 2024 (Google Scholar query), with a growth rate of 53.95% in the past five years. These studies mainly explore how humans can learn from the physiological characteristics of octopuses for sensor design, actuator development, processor architecture optimization, and intelligent optimization algorithms. The tentacle structure and nervous system of octopus have high flexibility and distributed control capabilities, which is an important reference for the design of soft robots. In terms of sensor technology, flexible strain sensors and suction cup sensors inspired by octopuses achieve accurate environmental perception and interaction. Actuator design uses octopus muscle fibers and movement patterns to develop various driving methods, including pneumatic, hydraulic and electric systems, which greatly improves the robot's motion performance. In addition, the distributed nervous system of octopuses inspires multi-processor architecture and intelligent optimization algorithms. This paper also introduces the concept of expected functional safety for the first time to explore the safe design of soft robots in failure or unknown situations. Currently, there are more and more bionic soft robot technologies that draw on octopuses, and their application areas are constantly expanding. In the future, with further research on the physiological characteristics of octopuses and the integration of artificial intelligence and materials science, octopus soft robots are expected to show greater potential in adapting to complex environments, human-computer interaction, and medical applications.

Determination method of limit interpolation points in industrial robot control system

Abstract With advancements in industrial robot technology and the ongoing enhancements in control system performance, the demand for precise robot motion is increasing. Generally, an increased number of interpolation points enhances the precision of robot movement, but excessive points can lead to jittering and out-of-step issues. This paper investigates the relationship between the number of motion interpolation points and the response times of the control system and the robot’s terminal velocity, based on the theoretical calculation and experimental analysis of the limit interpolation points for the control system of a self-developed 6-DOF (Six Degree of Freedom) robot. The method for calculating limit interpolation points is refined using the least squares method, and equations are derived for different control system response time and robot’s terminal velocity reaction times. The validity of the prediction curves is verified through experimental analysis.

Enhancing low-frequency motion of passive vibration-driven robots with inertial amplification structure: Modeling, simulations and experiments

Enhancing low-frequency motion of passive vibration-driven robots with inertial amplification structure: Modeling, simulations and experiments

Model predictive motion/force control in robotic grinding system for turbine blade.

Model predictive motion/force control in robotic grinding system for turbine blade.