Model Of Robot Research Articles

Development of a Novel Retinal Surgery Robot Based on Spatial Variable Remote Center-of-Motion Mechanism

Abstract This article reports the design, modeling, and experiments of a novel retinal surgery robot based on spatial variable remote center-of-motion (RCM) mechanism. The general design criteria for parallel mechanisms are proposed, and the planar five-bar mechanisms are evaluated and selected. The planar-spatial evolution process, including the parallel connection of the planar mechanism and the equivalent substitution of joints, is adopted to develop a spatial variable RCM mechanism and then the robot. The mobility and singularity of the robot are analyzed, and the forward/inverse kinematics and workspace are modeled. Dimension optimization is conducted based on a comprehensive performance indicator that characterizes the motion range of linear actuators and the global dexterity performance index of robot. The prototyped robot is fabricated and assembled, and the kinematic calibration is performed. The position error of end-effector is within 34 μm, and both the position error and deviation of the RCM point are within 23 μm. The robot is demonstrated to reach the desired position and execute the RCM motion with high precision simultaneously.

Adaptive EC-GPR: a hybrid torque prediction model for mobile robots with unknown terrain disturbances

Adaptive EC-GPR: a hybrid torque prediction model for mobile robots with unknown terrain disturbances

Magnetically-Actuated Endoluminal Soft Robot With Electroactive Polymer Actuation for Enhanced Gait Performance

Abstract Endoluminal devices are indispensable in medical procedures in the natural lumina of the body, such as the circulatory system and gastrointestinal tract. In current clinical practice, there is a need for increased control and capabilities of endoluminal devices with less discomfort and risk to the patient. This paper describes the detailed modeling and experimental validation of a magneto-electroactive endoluminal soft (MEESo) robot concept that combines magnetic and electroactive polymer (EAP) actuation to improve the utility of the device. The proposed capsule-like device comprises two permanent magnets with alternating polarity connected by a soft, low-power ionic polymer-metal composite (IPMC) EAP body. A detailed model of the MEESo robot is developed to explore quantitatively the effects of dual magneto-electroactive actuation on the robot’s performance. It is shown that the robot’s gait is enhanced, during the magnetically-driven gait cycle, with IPMC body deformation. The concept is further validated by creating a physical prototype MEESo robot. Experimental results show that the robot’s performance increases up to 68% compared to no IPMC body actuation. These results strongly suggest that integrating EAP into the magnetically-driven system extends the efficacy for traversing tract environments.

Predefined-time sliding mode position and attitude coupling control for in-cabin robots on space stations

As space science advances, the significance of space robotics research escalates, particularly in control methodologies. Addressing the work efficiency requirements during the mission of the in-cabin robot, it is necessary to develop a fast and robust position and attitude coupling controller for in-cabin robots. This paper presents a predefined-time sliding mode position and attitude coupling control method, employing the SE(3) frame description. The first, the dynamic model of the in-cabin robot is developed within the SE(3) framework. Then, the predefined-time position and attitude coupling sliding mode controller is designed with a sliding surface based on the dynamic error model, and a hyperbolic tangent function is used to suppress system chattering. In addition, the stability of the proposed control method is rigorously confirmed using the Lyapunov theorem, ensuring that the convergence time is accurately predefined. Finally, simulation results are provided to demonstrate the effectiveness of this novel control strategy. A comparative analysis with fixed-time sliding mode control highlights its superior performance.

Modeling and rolling gaits of a strut-actuated 6-strut locomotive tensegrity robot with membranes

Modeling and rolling gaits of a strut-actuated 6-strut locomotive tensegrity robot with membranes

Minimum-energy switching geometric filter on lie groups for differential-drive wheeled mobile robots

Accurate state estimation plays a critical role in various applications, such as tracking, regulation, and fault detection in robotic and mechanical systems. Typically, the Kalman–Bucy filter is used as a linear state observer for this purpose. However, real-world robots often exhibit complex behavior, characterized by a combination of dynamics, making it essential to employ hybrid filters. In this context, the Switching Kalman filter stands out as a well-established solution. In this article we aim to generalize the Brownian-Markov Stochastic Model, a hybrid dynamic model for differential-drive wheeled mobile robots, to the case of a mobile robot whose center of mass is not aligned to the wheels axle middle point, and to design a geometric hybrid state estimator by exploiting the Lie groups theory. The Brownian-Markov Stochastic Model features two modes: “grip” and “slip”. These modes correspond to ideal grip and lateral slippage, with transitions governed by a state-dependent Markov chain. To validate the proposed switching filter, we conduct MATLAB® simulations of the robot’s motion in a scenario prone to lateral grip loss, comparing the state estimates produced by the switching geometric filter with those obtained using the switching Kalman filter.

Advanced sampling discovers apparently similar ankle models with distinct internal load states under minimal parameter modification

Creating valid and trustworthy models is a key issue in biomedical engineering that affects the quality of life of both patients and healthy individuals in various scientific and industrial domains. This however is a difficult task due to the complex nature of biomechanical joints. In this study, a sampling strategy combining Genetic Algorithm and clustering is proposed to investigate biomechanical joints. A computational model of a human ankle joint with 43 input parameters serves as an illustrative case for the procedure. The Genetic Algorithm is used to efficiently search for distinct variants of the model with similar output, while clustering helps to quantify the obtained results. The search is performed in a close vicinity to the original model, mimicking subjective decisions in parameter acquisition. The method reveals twelve distinct clusters in the model parameter set, all resulting in the same angular displacements. These clusters correspond to three unique internal load states for the model, confirming the complex nature of the ankle. The proposed approach is general and could be applied to study other models in mechanical engineering and robotics.

A motivational-based learning model for mobile robots

Humans have needs motivating their behavior according to intensity and context. However, we also create preferences associated with each action’s perceived pleasure, which is susceptible to changes over time. This makes decision-making more complex, requiring learning to balance needs and preferences according to the context. To understand how this process works and enable the development of robots with a motivational-based learning model, we computationally model a motivation theory proposed by Hull. In this model, the agent (an abstraction of a mobile robot) is motivated to keep itself in a state of homeostasis. We introduced hedonic dimensions to explore the impact of preferences on decision-making and employed reinforcement learning to train our motivated-based agents. In our experiments, we deploy three agents with distinct energy decay rates, simulating different metabolic rates, within two diverse environments. We investigate the influence of these conditions on their strategies, movement patterns, and overall behavior. The findings reveal that agents excel at learning more effective strategies when the environment allows for choices that align with their metabolic requirements. Furthermore, we observe that incorporating pleasure as a component of the motivational mechanism affects behavior learning, particularly for agents with regular metabolisms depending on the environment. Our study also unveils that, when confronted with survival challenges, agents prioritize immediate needs over pleasure and equilibrium. These insights shed light on how robotic agents can adapt and make informed decisions in demanding scenarios, demonstrating the intricate interplay between motivation, pleasure, and environmental context in autonomous systems.

Dynamic modeling and experimental analysis of a novel bionic mantis shrimp robot

AbstractSmall carnivorous marine animals have developed agile movement abilities through long‐term natural selection, resulting in excellent maneuverability and high swimming efficiency, making them ideal models for underwater robots. To meet the requirements for exploring narrow underwater zones, this paper designs an underwater robot inspired by mantis shrimp. By analyzing the body structure and swimming mode of the mantis shrimp, we designed a robot structure and hardware system and established a dynamic model for the coupled motion of multiple pleopods. A series of underwater experiments were conducted to verify the dynamic model and assess the performance of the prototype. The experimental results confirmed the accuracy of the dynamic model and demonstrated that the bionic mantis shrimp robot can perform multiangle turns and flexible velocity adjustments and exhibits good motion performance. This approach provides a novel solution for developing robots suitable for detecting complex underwater environments.

Scientific habilty by using DNA and robotic activities in the science class

Educational robotics as an area of pedagogy is a discipline whose objective is the development of robotic prototypes that support the learning of natural sciences, demonstrating its potential as a didactic proposal that integrates technology into transdisciplinary thinking favoring digital thinking in the 21st century, in which we can mention scientific ability and the development of solutions to problems that favor scientific thinking. This educational research aims to connect the fields of chemistry and biology with the growing technology of educational robotics. A descriptive interpretive methodology was applied to a group of 80 high school students; They conducted experimental activities such as extracting DNA from tomatoes and observing the stages of mitosis in onion apical meristems. After observing the chromosomes in a microscope, the students use an augmented reality application, contrasting the information and creating a robotic model with recyclable material, programmed using the open-source ap-plication Arduino. No significant differences were found during the development of the activities between public and private schools, showing that 90% of the students obtained the DNA and 67% managed to develop robotic programming activities.

Deep Koopman-operator-based model predictive control for free-floating space robots with disturbance observer

This paper investigates the optimal tracking control problem of free-floating space robots in the presence of non-ideal factors, such as model uncertainties and external disturbances. To address this issue, we first leverage the deep Koopman operator, facilitated with a deep neural network, to establish an offline formulation of a global linearization model for a micro-nano free-floating space robot. Based on the estimated linearization model, we then employ the model predictive control method for online optimization control, achieving a significantly reduced computational burden. Additionally, a decay factor is integrated into the model predictive control optimization objective function to balance the precision of joint angles tracking with the suppression of base satellite attitude disturbance. To further address the modeling inaccuracies and external disturbances, we incorporate a disturbance observer based on a radial basis function neural network for online compensation within the model predictive control framework. This augmentation enhances tracking precision and robustness. Several groups of simulation results are carried out to demonstrate the effectiveness of the proposed method, showing its capability of energy consumption, disturbance rejection and enhanced robustness. These highlight the potential of the proposed method to improve the control performance of free-floating space robots, even in the absence of a precise dynamic model.

An active SLAM with multi-sensor fusion for snake robots based on deep reinforcement learning

Snake-like robots can imitate the movement patterns of animals in nature and enter the space that traditional robots cannot enter, which adapt to environments that humans cannot reach, and expand the field of human exploration. However, it is often challenging to realize autonomous navigation and simultaneously avoid obstacles under an unknown environment, that is, active SLAM (Simultaneous Localization and Mapping). This paper proposes an autonomous obstacle avoidance method combined with SLAM based on deep reinforcement learning for a wheeled snake robot by using a multi-sensor. Firstly, we design a modular wheeled snake robot structure with lightweight materials based on orthogonal joints and build a three-dimensional model of a snake robot in Gazebo. Secondly, the SLAM based on two-dimensional LiDAR and IMU is used to realize autonomous navigation under an unknown environment and detect obstacles. At the same time, a Deep Q-Learning-based path planning method of the snake robot is proposed to realize obstacles avoidance during navigation. Finally, simulation studies and experiments show that the designed snake-like robot can realize effective path planning and environmental mapping in environments with obstacles. The proposed active SLAM algorithm improves the success rate of snake-like robot path planning, has better obstacle avoidance ability for obstacles, and reduces the number of collisions compared with the traditional A* and the sampling-based RRT* algorithms.

Motion speed control of magnetic microsphere robot under alternating signals

Gradient magnetic field driving method does not have strict structural requirements for micro robots, making processing convenient. However, the speed control parameter of micro robots under gradient magnetic fields is single, which limits the practical application of this type of robots. Therefore, this article proposes to use alternating signals to control the motion speed of magnetic microsphere robots. Here, a motion analysis model of a magnetic microsphere robot driven by alternating signals is established, and a formula for calculating the movement speed of the magnetic microsphere robot is provided. The special motion mode of the magnetic microsphere robot driven by alternating signals is considered, and the correctness of the theoretical calculation is verified by experiments. The mechanisms and conditions for the different motion modes of magnetic microsphere robots are revealed. The movement speed of the magnetic microsphere robot under the alternating voltage signals is determined as a function of signal frequency, signal duty cycle, and bias voltage value. The results show that under the alternating voltage signals, the magnetic microsphere robot has three different motion states, namely rotation -movement alternation, swing-movement alternation, and pure movement. There are three control parameters of the speed, which are signal change frequency, duty cycle, and bias voltage.

A Bio-Inspired Dopamine Model for Robots with Autonomous Decision-Making.

Decision-making systems allow artificial agents to adapt their behaviours, depending on the information they perceive from the environment and internal processes. Human beings possess unique decision-making capabilities, adapting to current situations and anticipating future challenges. Autonomous robots with adaptive and anticipatory decision-making emulating humans can bring robots with skills that users can understand more easily. Human decisions highly depend on dopamine, a brain substance that regulates motivation and reward, acknowledging positive and negative situations. Considering recent neuroscience studies about the dopamine role in the human brain and its influence on decision-making and motivated behaviour, this paper proposes a model based on how dopamine drives human motivation and decision-making. The model allows robots to behave autonomously in dynamic environments, learning the best action selection strategy and anticipating future rewards. The results show the model's performance in five scenarios, emphasising how dopamine levels vary depending on the robot's situation and stimuli perception. Moreover, we show the model's integration into the Mini social robot to provide insights into how dopamine levels drive motivated autonomous behaviour regulating biologically inspired internal processes emulated in the robot.

Development of Driving Robot and Driver Model Applied Regenerative Brake Control of Electrified Vehicles

Real driving emissions (RDE) test procedures for type approval were initiated in Japan in 2022. In Japan, the test vehicle is driven on a test road (test course) by tracing the speed pattern prepared for each RDE test. The vehicle needs to trace the patterns in the same manner as in the conventional chassis dynamometer test. The use of a driving robot is effective for reproducibility in precisely tracing the target vehicle speed during these tests. In this study, we developed a driving robot for electrified vehicles (HEV, plug-in HEV, BEV, and FCV), which have regenerative brake-control systems. We applied a new logic to our robot that was developed in-house for conventional engine vehicles. We focused on braking deceleration of the regenerative brake control to change the operation timing from the accelerator pedal to the brake pedal. We verified the improvement of speed traceability of the driving robot installed with the proposed logic on the chassis dynamometer and test road.

Design and workspace analysis of a cable-driven space capture robot for noncooperative targets

Capturing noncooperative targets in space has garnered continuous research interest in aerospace applications. This study addresses the demands of large-scale, multifaceted activities and varied working conditions for space capture missions by designing a space capture robot composed of multiple cable-driven manipulators operating in parallel. First, single- and multi-segment cable-driven robot models were designed, and a geometric model was subsequently built. The optimal number of segments was determined by analysing the condition number of a Jacobian matrix using the Monte Carlo method. Subsequently, based on the constant-curvature assumption, a kinematic model of the cable-driven space capture robot was formulated, and capture methods for different capture targets were designed using the Monte Carlo method. Finally, an eight-segment cable-driven robot prototype was developed, and compliance and driving experiments were conducted. This robot exhibits promising application potential for space noncooperative target capture and can be feasibly manufactured using on-orbit 3D machining technology.

Modeling the impact of terrain surface deformation on drag force using discrete element method and empirical formulation

Understanding motion-induced terrain deformation is essential for improving predictive models in geotechnical engineering, infrastructure development, and robotics. The existing analysis assumes a uniform and unchanged environment and lacks a description of the terrain deformation induced by motion, which is particularly significant when the grains are loosely packed. To address this gap, we performed numerical investigations to mathematically model the changes in the surrounding environment and their corresponding effects on resistance. Specifically, we examined the response of granular materials and the energy variation of the particles during motion. Increased energy in the direction of motion was categorized as influences above or below the medium surface. Aligned with the influencing factors, two variables were considered to quantify the impact on the drag force. Building upon the energy analysis and the framework of Resistive Force Theory, we proposed a drag force model for buried motion in loose granular terrain. Compared with the experimental data, the average predictive error decreased from 17.8% to less than 2.7%. Our study provides a feasible approach for incorporating the effects of terrain deformation into a drag force model, thereby enhancing the accuracy and contributing to the development of granular locomotion.

РАЗРАБОТКА МОБИЛЬНОГО КОЛЕСНОГО РОБОТА ДЛЯ ДОСТАВКИ ПОСЫЛОК

The study objective is to develop approaches to the design of a mobile wheeled robot for transporting small-sized goods, operating autonomously without the participation of a human operator. At the same time, the main task is to develop a mathematical model of a wheeled robot that takes into account the moments of rolling friction and sliding of the driving wheels on the support surface, and which allows to study the starting operation modes of the electromechanical drive of a mobile device. The increase in the speed of the mobile robot is achieved by minimizing the acceleration time to the required speed values through the use of a 2-stage algorithm for changing the control voltage. The development of a mathematical model of a robotic system is carried out on the basis of methods of analytical mechanics and the theory of automatic control. Using the Laplace transforms, an analytical solution of differential equations describing the dynamics of a mobile robot is obtained. Numerical solution of the obtained equations is carried out in Mathcad interface. The novelty of the work consists in obtaining time dependencies for the speed and current in the circuit of the electric motor of the mobile robot wheeled drive and finding out the basic patterns of movement at its launch. The results of the conducted research can be used in the development of motion control systems for mobile robots with feedback, ensuring the stabilization of a set value of the movement speed in steady-state modes. Conclusions: a mathematical model of a mobile robotic system is developed, the starting modes of its operation and the method of combined motion control are studied, the speed of a mobile wheeled device is increased when moving along a given trajectory.

Using differential-algebraic equations and natural coordinates for modelling and simulating cable-driven parallel robots

This paper proposes a comprehensive approach to the dynamic modelling of Cable-Driven Parallel Robots (CDPRs) by means of Differential-Algebraic Equations (DAEs). CDPRs are usually modelled through a minimal set of Ordinary Differential Equations (ODEs), often by making some simplification or just focusing on the unconstrained platform/end-effector dynamics. The alternative use of redundant DAEs provides several benefits since several non-ideal properties and peculiar operations of CDPRs can be easily and accurately modelled. To provide a comprehensive modelling frame, the typical components of a CDPR with rigid cables are here discussed and modelled by exploiting the concept of DAEs, which use redundant coordinates and embed kinematic constraints in the algebraic part of the equations. Through such advantageous features, it is possible to model swivelling guiding pulleys with non-negligible dimensions and mass. The use of rheonomous constraints is proposed as well, to represent in a simple way the effect of the movable exit-points, that are widely adopted in reconfigurable CDPRs. Finally, the use of Natural Coordinates is proposed for representing spatial end-effectors and modelling some challenging operations such as its overturning or the picking of heavy objects. Numerical simulations and the comparison with the results provided by a benchmark software are shown to demonstrate the accuracy and the computational efficiency of the proposed approach.

Novel Robotic Esophagogastric Anastomosis Simulation Model for Skill Development and Training

BackgroundEsophagogastric anastomosis is a critical step of esophagectomy. We aimed to develop a novel robotic esophagectomy simulator with high rates of fidelity and educational value for trainee surgeons to advance these skills in a low-risk setting. MethodsA porcine esophagus-stomach block was secured on a platform resembling the anatomy during an esophagectomy, and a da Vinci Xi (Intuitive Surgical) robotic system was docked above it. Participants completed 5 key steps (creating the gastric conduit, transecting the esophagus, making the gastrotomy and esophagotomy, creating the anastomosis, and sewing the common enterotomy). The model was assessed through surveys under domains of fidelity (surgical field, reality of materials, anatomy, and experience) and value as a training tool on a scale of 1 to 5 (strongly disagree to strongly agree). ResultsOf 14 participants, 8 (57.1%) were women, 9 (64.3%) were integrated cardiothoracic surgery residents, 1 (7.1%) was a thoracic-track resident, and 10 (71.4%) were in postgraduate year 4 or higher. Participants thought most aspects of the model had high fidelity, including the anatomy of conduit (4.8 ± 0.4) and proximal esophagus (4.9 ± 0.4), realism of the stomach (4.9 ± 0.4) and esophagus (4.9 ± 0.4), stapling (4.7 ± 0.6), suturing (4.8 ± 0.4), and tissue handling (4.4 ± 0.6). Participants rated the model highly overall (4.7 ± 0.5) and as a training tool (4.9 ± 0.4), with strong interrater reliability (0.69). ConclusionsThe robotic esophagogastric simulation model demonstrated high fidelity and value as a training tool, suggesting its potential effectiveness for surgeons with limited experience. However, it warrants further refinement to address limitations and to optimize its value as a training tool.