Model Of Robot Research Articles

Application of Path Planning and Tracking Control Technology in Mower Robots

Path planning and tracking is the most basic technology for mowing robots, among which the performance of algorithms has a great impact on their intelligence and efficiency. Based on the research of relevant references on mower robots, it mainly focuses on complete coverage path planning, path tracking control, and obstacle avoidance path planning. In complete coverage path planning, three methods were introduced, including simple complete coverage planning, optimal complete coverage planning, and hybrid complete coverage planning. In the path tracking control section, the control methods are divided into three types based on whether the control method depends on the robot model and the type of model, namely model free control method, kinematic model-based control method, and dynamic model-based control method. In obstacle avoidance path planning, we introduce the environment detection device and obstacle avoidance planning algorithm. Then the relevant research papers are analyzed in classification, comparing the research and validation methods adopted by the researchers in the form of charts. Finally, we pointed out the limitations of path planning technology in the application of mower robots. Meanwhile, future development trends are predicted.

Design and modeling of a novel mobile cable robot with dual-stage end effector

Design and modeling of a novel mobile cable robot with dual-stage end effector

Automatic differential kinematics of serial manipulator robots through dual numbers

Dual Numbers are an extension of real numbers known for its capability of performing exact automatic differentiation of one-valued functions theoretically without error approximation. Also, Differential Kinematics of robots involves the computation of the Jacobian, which is a matrix of partial derivatives of the Forward Kinematic equations with respect to the robot’s joints. Thus, to perform the automatic calculation of the Jacobian matrix, this paper presents an extension of dual numbers composed of a scalar real part and a vector dual part, where the real part represents the angular value of the robot joint, and the dual part represents the direction of the corresponding partial derivative for each joint. The presented method was implemented in Matlab through Object Orientes Programming (OOP), and the results for calculating the Jacobian of the KUKA KR 500 robot model for 1000 random postures were subsequently compared in terms of execution time and Mean Squared Error (MSE) with other conventional methods: the geometric method, the symbolic method, and the finite difference method. The results showed a significant improvement in the computing time for calculating the Jacobian of the robotic model compared to the other methods, as well as a minimum MSE having as reference the numerical value of the symbolic calculations.

Image‐based backbone reconstruction for non‐slender soft robots

AbstractOne advantage of soft robots to conventional robots is their continuous deformation, which makes them highly adaptable to their surroundings. To describe the kinematics of soft robots it is not sufficient to only know the position of the end‐effector. Instead, the steady state of a soft robot can be described by its backbone curvature. An image‐based backbone reconstruction method is used and adopted for a non‐slender soft robot to reconstruct the backbone curve using polynomials to describe an arc length‐dependent curvature. A constant curvature and cubic curvature polynomial have been tested to reconstruct the backbone curve. In the case of a pressurized soft robot without externally applied loads, the constant curvature polynomial can be used to reconstruct the backbone of the soft robot using an image‐based approach, yielding good agreement with the captured images. However, some care has to be put into the consideration of the soft robot geometry in this method, to guide the optimization procedure. From our findings, we can conclude that it is not necessary to apply markers to the soft robot to reconstruct the backbone, nor is it necessary to have a detailed geometric model of the soft robot to be used in a differential rendering process. An image‐based iterative‐closest‐point optimization method, with some carefully placed estimate points, to integrate the volumetric structure of the soft robot can yield good results for the reconstructed backbones.

Utilizing Photovoltaic Solar Panels for Real‐Time Localization and Speed Detection of Approaching Illuminated Objects in Humanoid Robots

ABSTRACTIn the field of humanoid robotics, this paper showcases a promising method of integrating the photovoltaic (PV) solar panels into the “GUCnoid 1.0” humanoid robot model. Instead of the conventional power generation, solar cells are used to supply electrical energy to various sensors to create a system capable of real‐time sensing and perception. The main focus of the study will be the use of PV panels as receptors that are capable of detecting the light, enabling an adaptive system which perceives environmental changes. The behavior of the PV cell is superbly studied when various light sources are approached or depart. They finally reveal unique patterns in the voltage output signal amplitude. Interestingly, these patterns figure out the same symmetric structure, which reflects on a vertical axis by their mirroring. Using this simplicity, the method involves using an artificial neural network that is able to distinguish the light sources coming towards the detector and the ones running away and the rate at which they approach/recede. Outdoor experiment was organized for verification of methods. GUCnoid 1.0— humanoid robot was placed in front of a moving vehicle with different speeds of approach. To be able to identify the vehicle's position and velocity, a PV sensing technique has to be applied. This innovative technology will have wide applications, with much attention paid to improving the speed of finding nearby objects or vehicles in the scenarios where quick detection is a serious life safety issue. Through the process of PV solar panels' smart sensing, we directly connect the areas of high‐tech robotics and renewable energy. This discovery creates an opportunity for companies to build more responsive and flexible humanoid robots that can effectively collaborate with humans to achieve greater outcomes through more secure and efficient interactions between humans and robots.

Performance evaluation of the static thrust and efficiency of an actuated biomimetic jellyfish model

Abstract This study offers an in-depth analysis of a jellyfish-inspired robotic model designed to simulate the expansion and contraction mechanisms found in natural jellyfish. By employing variable Duty Cycles to replicate the cyclic motions of the jellyfish, the thrust generation of the model is measured through static thrust assessments using a load cell, while its efficiency is evaluated using the Thrust-to-Power Ratio, TPR. The findings indicate that increasing the amplitude of the motion leads to more complex wave patterns, which in turn reduce the peak-to-peak force generated. Specifically, a 25% increase in the A/D ratio results in a significant 30% decrease in static thrust. It was observed that higher actuation frequencies do not notably influence thrust generation when the A/D ratio remains constant. Importantly, the TPR was found to be highest at an A/D ratio of 0.10, with an increase of up to 6% observed at higher Duty Cycles, suggesting that smaller actuation amplitudes are more efficient in enhancing thrust performance. These results highlight the critical role of amplitude in the efficiency of static thrust generation within a given Duty Cycle, emphasizing that lower amplitudes generally lead to higher efficiency. The study underscores the importance of optimizing both geometric and kinematic parameters for improved propulsion performance. It suggests that future research should focus on fine-tuning A/D ratios and Duty Cycles, as well as exploring alternative geometric configurations and materials to further enhance the hydrodynamic performance of jellyfish-inspired robots. The insights gained from this study provide a valuable foundation for developing more efficient and effective bio-inspired underwater vehicles.

Enhancing Maneuverability in a Variable Wheelbase Wheeled Mobile Robot Through Dynamic Steering Curvature Control

ABSTRACTVariable wheelbase wheeled mobile robot (VW‐WMR) is capable of maneuvering flexibly and traversing on rough and soil terrains within confined spaces. While the steering radius of the robot model can be robustly changed by the variable wheelbase length, a challenge is posed in accurately tracking a predefined trajectory through the alteration of wheelbase length. A dynamic steering curvature (DSC) control method is proposed in this work to overcome this challenge, which is achieved by two different approaches utilizing the variable wheelbase length and manipulating drifting motions. First, to enable flexible trajectory adjustments, a 3D Ackermann kinematics model, incorporating the lifting motion of the robot box, is developed to control steering curvature by the changes in wheelbase length. Second, to achieve flexible movement for the inner and outer curves of the originally planned curvilinear trajectory, Ackermann drift models are presented by the establishment of two sequence instantaneous centers. Furthermore, the control performance of DSC is validated through simulation experiments on the ROSTDyn Vortex platform, using a six‐wheeled VW‐WMR named HIT‐MRII robot. The effectiveness of DSC for strategically altering the robot's motion trajectory is demonstrated by the results, which show the relation between the changed trajectory position and the variation in wheelbase length, and the variation in the radii of the instantaneous centers (ICs) in the Ackermann drift model, respectively. In addition, the high maneuverability of the robot using the Ackermann model is proven by physical experiments during steering motions on soil terrain. A comprehensive advantage is demonstrated by the results via Ackermann models, which include shorter runtimes, moderate travel distances, and moderate variations in force on the wheels, compared to different velocity, crabbing motion, and equivalent bicycle models.

A parallel mechanism-based virtual biomechanical shoulder robot model: Mechanism design optimization and motion planning

This paper presents the design and optimization process of a Virtual Biomechanical Shoulder Robot Model (VBSRM) based on a 6-4 parallel mechanism. To address the challenges posed by parallel manipulators and the specific biomechanical constraints of the shoulder joint, a comprehensive design analysis is conducted. First, a kinematic model of the VBSRM is developed, followed by an investigation of its geometric model. To obtain an optimal VBSRM design, performance objectives such as condition number, norm of actuator force, and stiffness are identified and optimized. Initially, only condition number of the robot mechanism is optimized using Genetic Algorithm and performance objectives from the optimal design are analyzed. Later, the three objectives are grouped to form a single function and a single objective-based optimization is also conducted. However, further investigation revealed the conflicting nature of the objectives and hence these were simultaneously optimized using the Non-dominated Sorting Genetic Algorithm (NSGA II). The results obtained from various optimization routines are compared and it is found that the results from the NSGA II provide a better tradeoff between the performance objectives. The motion trajectories from the optimal design of the VBSRM are later analyzed vis-à-vis human shoulder motions for its intended use as a robotic model of the human shoulder joint in various applications.

Laboratory work in physics "The study of uniformly accelerated motion without initial velocity" using a robotic kit LEGO MINDSTORMS EDUCATION EV3

The article presents an example of using a robotic kit at physics and informatics lessons — a laboratory work "The study of uniformly accelerated motion without initial velocity" using the LEGO MINDSTORMS EDUCATION EV3 kit. This work is intended for students in 9–10th grades when studying uniformly accelerated motion in a physics course. During the laboratory work, it is necessary to: assemble an experimental setup and create a program for the EV3 control module, which should record the time of movement of a ball along an inclined chute with output to the EV3 module screen; enter the obtained measurement results into a calculation table compiled in an electronic spreadsheet, into which formulas for automatic calculation of acceleration and instantaneous velocity of the ball are preliminarily entered. The laboratory work can be carried out both in a physics and an informatics lessons, and in an optional lesson, you can also conduct an integrated physics and informatics lesson. The use of robotic modeling introduces students to the modern process of conducting physical research (experiments), helps to increase students' interest in experimental work, develop their skills in using robots in research and motivation for engineering and technical creativity.

A biomimetic fruit fly robot for studying the neuromechanics of legged locomotion

For decades, the field of biologically inspired robotics has leveraged insights from animal locomotion to improve the walking ability of legged robots. Recently, 'biomimetic' robots have been developed to model how specific animals walk. By prioritizing biological accuracy to the target organism rather than the application of general principles from biology, these robots can be used to develop detailed biological hypotheses for animal experiments, ultimately improving our understanding of the biological control of legs while improving technical solutions. In this work, we report the development and validation of the robot Drosophibot II, a meso-scale robotic model of an adult fruit fly,Drosophila melanogaster. This robot is novel for its close attention to the kinematics and dynamics ofDrosophila, an increasingly important model of legged locomotion. Each leg's proportions and degrees of freedom have been modeled afterDrosophila3D pose estimation data. We developed a program to automatically solve the inverse kinematics necessary for walking and solve the inverse dynamics necessary for mechatronic design. By applying this solver to a fly-scale body structure, we demonstrate that the robot's dynamics fit those modeled for the fly. We validate the robot's ability to walk forward and backward via open-loop straight line walking with biologically inspired foot trajectories. This robot will be used to test biologically inspired walking controllers informed by the morphology and dynamics of the insect nervous system, which will increase our understanding of how the nervous system controls legged locomotion.

Development of a Mathematical Model of a Highly Maneuverable Robot for Simulation of Robots with Different Types of Designs

The article presents the results of developing a mathematical model and simulation modeling of a three-wheeled highly maneuverable mobile robot. A mathematical model of the robot in the state space has been developed. The possibility of using this robot with rotary modules for analyzing and testing control algorithms for robots of various designs has been considered. The relevance of the study is justified by the high costs of creating physical models of robots for experimental verification of control algorithms. The proposed three-wheeled mobile robot with rotary modules, where each wheel is simultaneously both a driving and a steering wheel, allows reducing these costs, while maintaining the ability to simulate part of the real operating conditions. Simplified diagrams of the robot are given, the main parameters and equations characterizing the change in linear and angular velocity are described. Particular attention is paid to modeling various types of robot drives: automotive type and with a differential drive. In the discrete state space, equations describing the dynamics of the robot were presented, which allows them to be used in software products for modeling. The results of simulation modeling obtained in the SimInTech software environment confirm the effectiveness of the proposed model. The analysis of the obtained results showed that the use of a three-wheeled robot with rotary modules allows successfully simulating the behavior of robots with other types of drives and creating conditions close to real ones for testing control algorithms. The proposed model can be used to improve the quality of design and testing of control algorithms in robotics, while reducing the costs of creating physical models of robots, as well as in the educational process.

A Koopman-based residual modeling approach for the control of a soft robot arm

Soft robots are challenging to model and control due to their poorly defined kinematics and nonlinear dynamics. Recently, Koopman operator theory has been shown capable of constructing control-oriented soft robot models from data. However, building these models requires extensive data collection and they do not necessarily generalize well outside of the training observations. This paper presents a more data-efficient and generalizable approach to soft robot modeling that first identifies a physics-based Koopman model then supplements it with a data-driven residual Koopman model. The resulting combined model is linear and thus compatible with real-time model-based control techniques such as Model Predictive Control (MPC). The efficacy of the approach is demonstrated on several simulated systems and on a real soft robot arm, where it is shown to generate models that are more accurate than purely physics-based models and require less data to construct than purely data-driven models. Using a model-based controller, the soft arm is able to successfully track end effector trajectories, perform a pick-and-place task, and write on a dry-erase board, showcasing the applicability of this framework to increase the capabilities of soft robotic systems.

Flatness-based control in successive loops for dual-arm robotic manipulators

Dual-arm robotic manipulators are used in industry and for assisting humans since they enable dexterous handling of objects and more agile and secure execution of pick-and-place, grasping and lifting, or assembling tasks. In this article, a multi-loop flatness-based controller is proposed for the dynamic model of a dual-arm robot. In the considered application, the dual-arm robotic system has to lift and transfer an object under synchronized motion of its two end-effectors while it has also to compensate for the grasping forces between the two end-effectors and the object. The dynamic model of this robotic system is formulated while it is proven that the state-space description of the robot’s dynamics is differentially flat. The control problem for this robotic system is solved with the use of a flatness-based control approach which is implemented in successive loops. To apply the multi-loop flatness-based control method, the state-space model of the dual-arm robotic manipulator is separated into subsystems, which are connected in cascading loops. Each one of these subsystems can be viewed independently as a differentially flat system and control about it can be performed with inversion of its dynamics as in the case of input–output linearized flat systems. The state variables of the preceding i-th subsystem become virtual control inputs for the subsequent ( i + 1)-th subsystem. In turn, real control inputs are applied to the last subsystem. The whole control method is implemented in successive loops and its global stability properties are also proven through Lyapunov stability analysis. The multi-loop flatness-based control approach is an exact nonlinear control scheme which (i) does not use approximate linearization of the dynamic model of the controlled system and (ii) does not need changes of state variables and complicated state-space model transformations.

Spiking neural networks-based generation of caterpillar-like soft robot crawling motions

Robots have been widely used in daily life in recent years. Unlike conventional robots made of rigid materials, soft robots utilize stretchable and flexible materials, allowing flexible movements similar to those of living organisms, which are difficult for traditional robots. Previous studies have used periodic signals to control soft robots, which lead to repetitive motions and make it challenging to generate environment-adapted motions. To address this issue, control methods can be learned through deep reinforcement learning to enable soft robots to select appropriate actions based on observations, improving their adaptability to environmental changes. In addition, as mobile robots have limited onboard resources, it is necessary to conserve battery consumption and achieve low-power control. Therefore, the use of spiking neural networks (SNNs) with neuromorphic chips enables low-power control of soft robots. In this study, we investigated the learning methods for SNNs aimed at controlling soft robots. Experiments were conducted using a caterpillar-like soft robot model based on previous studies, and the effectiveness of the learning method was evaluated.

Piezoelectric energy harvesting from walking motion of a passive biped robot model with flexible legs

Passive dynamic walking biped robots utilize the inherent dynamics of their structure to achieve walking motion without continuous actuation, enhancing energy efficiency and stability. Replacing conventional rigid legs with flexible elastic beams in the simplest passive walker will be associated with undesirable vibrations. Despite the positive effect of the damper in reducing vibrations, the current research has used the piezoelectric energy harvesting with the aim of reducing the wasted energy of the gait cycle and improving its stability range. For this purpose, the electromechanical Lagrange equations are determined by applying Hamilton's principle and the assumed mode method and then, the new definition of the step function is obtained through numerical methods, solving a boundary value problem to establish proper initial conditions for stable walking. Numerical simulations indicate that the piezoelectric energy harvester can create a stable period-one gait cycle by optimizing the length of the piezo layers attached to the undamped elastic legs despite the presence of small vibrations. Additional results are presented in bifurcation diagrams, investigating the effect of important physical and structural parameters such as slope angle, length and thickness of the piezo layer.

Dynamic modeling of wheeled biped robot and controller design for reducing chassis tilt angle

Abstract The wheeled-legged robot combines the advantages of wheeled and legged robots, making it easier to assist people in completing repetitive and time-consuming tasks in their daily lives. This paper presents a study on the kinematic and dynamic modeling, as well as the controller design, of a wheeled biped robot with a parallel five-bar linkage mechanism as its leg module. During the motion of the robot, the robot relies on the tilt angle of the inverted pendulum, and this angle often results in the tilting of the chassis of the robot, presenting challenges for the installation of upper-body payloads and sensor systems. The controller proposed in this paper, which is developed by decoupling the primary motions of the robot and designing a multi-objective, multilevel controller, addresses this issue. This controller employs the pendulum pitch angle of the equivalent inverted pendulum model as the control variable and compensates for the chassis tilt angle (CTA). This control method can effectively reduce the CTA of such robots and eliminate the need for additional counterweights. It also provides a more spacious structural design for accommodating upper-body devices. The effectiveness of this control framework is verified through variable height control, walking on flat ground, and carrying loads over rough terrain and slopes.

Evaluating Swarm Robotics for Mining Environments: Insights into Model Performance and Application

The mining industry is experiencing a transformative shift with the integration of automation, particularly through autonomous haul truck systems, and further advancements are anticipated with the application of swarm robotics. This study evaluates the performance of four swarm robot models, namely baseline, ant, firefly, and honeybee, in optimizing key mining operations such as ore detection, extraction, and transportation. Simulations replicating real-world mining environments were conducted to assess improvements in operational efficiency, scalability, reliability, selectivity, and energy consumption. The results demonstrate that these models can significantly enhance the precision and productivity of mining activities, especially in complex and dynamic settings. A case study of the Pilbara iron ore mine in Australia is presented to illustrate the practical applicability of these models in an actual mining context. The study also highlights specific enhancements in each model, including role specialization in the ant model, advanced communication in the firefly model, and improved localization combined with hybrid control in the honeybee model. While the honeybee model showed superior performance in high-precision tasks, its reliability was limited under high-error conditions, and it faced a computational resources bottleneck in large-scale operations, highlighting the need for further development. By evaluating these models against performance criteria, the study identifies the most suitable swarm models for various mining conditions, offering insights into achieving more sustainable, scalable, and efficient mining operations.

Kinematics-guided data-driven energy surrogate model for robotic additive manufacturing

With the increase in the usage of industrial robots for manufacturing applications, there will be a corresponding rise in energy consumption. Especially, robotic additive manufacturing (AM) has demonstrated significant potential for working autonomously in hazardous or extraterrestrial settings, as well as for producing large-scale structures. Recent advancements have made it possible for teams of robots to collaborate for printing structures even larger than their sizes together. Utilizing robots efficiently for AM is essential for achieving energy sustainability goals. The literature on energy models for robots and AM processes lacks a comprehensive energy model for robotic AM. To address this gap, a multi-point trajectory-based energy model is proposed for robotic AM. The model is created to help users manufacture parts in an energy-efficient way. These simulations require a significant amount of computation time, which limits their usage to offline purposes only. Hence, we propose two energy surrogate models for real-time predictions: a pure data-driven model and a kinematics-guided data-driven model. The kinematics-guided approach is an enhanced version of pure data-driven model which learns from inverse kinematics solutions along the trajectory to understand the robot’s dynamics and kinematics. There were two test cases conducted. The first test case consisted of paths that were feasible, printable, and within the robot’s reach, while the second test case consisted of a mixture of paths that were both feasible and infeasible. In comparison to the pure data-driven approach, the kinematics-guided approach stood out in both testing cases. Two real-world case studies have been used to demonstrate that the proposed approach can be used in lieu of simulations for real-time applications. Especially in the second case, where the part was placed very close to the robot making a portion of the part infeasible to print, the proposed approach correctly identified that it was not possible to print the geometry due to the robot’s kinematics. The proposed kinematics-guided approach is 195 and 700 times faster than the simulation results, respectively, in two case studies, and thus supporting the use of the proposed approach for real-time applications.

Dynamic modelling and a model-based control strategy of joint servo system for power transmission line inspection robot

Abstract The time-varying load inertia is one of the important dynamic characteristics of the power transmission line inspection robots (PTLIRs) in joint space, which can heavily affect the stability of the robot in the inspection process. In this paper, with considering the time-varying characteristics of load inertia, a method for modelling and control of the flexible joint servo system of the PTLIR is studied. First, the control system of the PTLIR is established based on the two inertia system. In order to calculate the load inertia, a dynamic model of the inspection robot is established. Then, the model-based notch filter is designed to improve dynamic performance of the robot, and suppress the vibration amplitude change which caused by the time-varying load inertia. Finally, the comparison of the control performance between the traditional control strategy and the model-based control strategy with notch filter are compared. The simulation results show that the paper can provide an effective tool for modelling and improving the stability of the PTLIR.

Dynamic analysis of six-bar tensegrity-based robot

Abstract The tensegrity-based robot is a hot research topic in the research field of robotics. The dynamics of the robot, describing the specific motion patterns under the influence of forces and torques, play an important role in the application of robotics. In addition, the dynamics should be considered when revealing the internal mechanisms of robots and predicting their dynamic behaviors. In this work, a dynamic model for a rolling six-bar tensegrity-based robot is established. In terms of kinematic representation, quaternions are chosen as the tool for attitude description, simplifying the derivation process of the complex three-dimensional rotational kinematics calculations. Quaternions play a crucial role in mathematical processing for robot attitude control and path planning due to their effective representation of three-dimensional space rotations. The classic Newton-Euler dynamic framework was adopted to conduct in-depth and detailed studies on the dynamic characteristics of the robot under various force conditions and motion states, with a particular focus on rolling dynamic analysis. Special attention was paid to the dynamic response mechanisms of each component of the robot under the combined action of internal and external forces and torques.