Robot Dynamics Research Articles

Multi-Modal and Adaptive Robot Control through Hierarchical Quadratic Programming

This paper proposes a novel Hierarchical Quadratic Programming (HQP)-based framework that enables multi-tasking control under multiple Human-Robot Interaction (HRI) scenarios. The proposed controllers’ formulations are inspired by real-world contact-rich scenarios, which currently constitute one of the main limitations in terms of widespread practical deployment. Indeed, HRI can occur through different modalities, based on human’s needs. The objective is to create a unique framework for various types of possible interactions, avoiding the necessity of switching between different control architectures, which requires dealing with discontinuities. To achieve this, we firstly propose a HQP-based hybrid Cartesian/joint space impedance control formulation. Based on the robot’s dynamics, this controller enables an adaptive compliance behaviour, while achieving hierarchical motion control. This is validated through a series of experiments that show the accuracy of trajectory tracking, which remains in the order of 10mm during fast motions thanks to the addition of the robot dynamics. Besides, the hybrid compliance behaviour allows to deviate from such accuracy when an interaction is present. We then consider the case in which the human needs to move the robot directly, by proposing a hybrid admittance/impedance controller, that is again based on a HQP formulation and provides inherent softening when conflicting tasks are present, or in close-to-limit and near-singular configurationsa. This is validated through several experiments in which the human easily moves the robot in the workspace via direct physical interaction. Next, we formulate an additional hierarchy that enables force control and allows to maintain a specific interaction force at the end effector. We then extend this to simultaneous force and trajectory tracking. Overall, we obtain a multi-purpose HQP-based control framework, that seamlessly switchwes between interaction modes, enabling multiple hierarchical behaviours, and covering a wide spectrum of interaction types, essential for synergistic HRI.

Interaction model estimation-based robotic force-position coordinated optimization for rigid-soft heterogeneous contact tasks

Inspired by Model Predictive Interaction Control (MPIC), this paper proposes differential models for estimating contact geometric parameters and normal-friction forces and formulates an optimal control problem with multiple constraints to allow robots to perform rigid-soft heterogeneous contact tasks. Within the MPIC, robot dynamics are linearized, and Extended Kalman Filters are used for the online estimation of geometry-aware parameters. Meanwhile, a geometry-aware Hertz contact model is introduced for the online estimation of contact forces. We then implement the force-position coordinate optimization by incorporating the contact parameters and interaction force constraints into a gradient-based optimization MPC. Experimental validations were designed for two contact modes: “single-point contact” and “continuous contact”, involving materials with four different Young’s moduli and tested in human arm “relaxation-contraction” task. Results indicate that our framework ensures consistent geometry-aware parameter estimation and maintains reliable force interaction to guarantee safety. Our method reduces the maximum impact force by 50% and decreases the average force error by 42%. The proposed framework has potential applications in medical and industrial tasks involving the manipulation of rigid, soft, and deformable objects.

Adaptive neural network event-triggered secure formation control of nonholonomic mobile robots subject to deception attacks

This paper investigates the adaptive neural network (NN) event-triggered secure formation control problem for nonholonomic mobile robots (NMRs) subject to deception attacks. The NNs are employed to approximate unknown nonlinear functions in robotic dynamics. Since the transmission channel from sensor-to-controller is vulnerable to deception attacks, a NN estimation technique is introduced to estimate the unknown deception attacks. In order to alleviate the amount of communication between controller-and-actuator, an event-triggered mechanism with relative threshold strategy is established. Then, an adaptive NN event-triggered secure formation control method is proposed. It is proved that all closed-loop signals of controlled systems are bounded and the formation tracking errors converge a neighborhood of the origin in the presence of deception attacks. The comparative simulations illustrate the effectiveness of the proposed secure formation control scheme.

Stair-Climbing Wheeled Robot Based on Rotating Locomotion of Curved-Spoke Legs.

This study proposes a new wheel-leg mechanism concept and formulations for the kinematics and dynamics of a stair-climbing robot utilizing the rotating leg locomotion of curved spokes and rolling tires. The system consists of four motor-driven tires and four curved-spoke legs. The curved-spoke leg is semicircle-like and is used to climb stairs. Once the spoke leg rolls on the surface, it lifts and pulls the mating wheel toward the surface, owing to the kinematic constraint between the spoke and the wheel. Single-wheel climbing is a necessary condition for the stair climbing of whole robots equipped with front and rear axles. This study proposes the design requirements of a spoke leg for the success of single-wheel climbing in terms of kinematic inequality equations according to the scenario of single-wheel climbing. For a design configuration that enables single-wheel climbing, the required minimum friction coefficient for the static analysis of the stair-climbing wheeled robots is demon-strated. Thereafter, the stair-climbing ability is validated through the dynamic equations that enable the frictional slip of the tires, as well as the curved-spoke legs. Lastly, the results revealed that the rotating locomotion of the well-designed curved-spoke legs effectively enables the stair climbing of the whole robot.

Lie-theory-based dynamic model identification of serial robots considering nonlinear friction and optimal excitation trajectory

Abstract Accurate dynamic model is essential for the model-based control of robotic systems. However, on the one hand, the nonlinearity of the friction is seldom treated in robot dynamics. On the other hand, few of the previous studies reasonably balance the calculation time-consuming and the quality for the excitation trajectory optimization. To address these challenges, this article gives a Lie-theory-based dynamic modeling scheme of multi-degree-of-freedom (DoF) serial robots involving nonlinear friction and excitation trajectory optimization. First, we introduce two coefficients to describe the Stribeck characteristics of Coulomb and static friction and consider the dependency of friction on load torque, so as to propose an improved Stribeck friction model. Whereafter, the improved friction model is simplified in a no-load scenario, a novel nonlinear dynamic model is linearized to capture the features of viscous friction across the entire velocity range. Additionally, a new optimization algorithm of excitation trajectories is presented considering the benefits of three different optimization criteria to design the optimal excitation trajectory. On the basis of the above, we retrieve a feasible dynamic parameter set of serial robots through the hybrid least square algorithm. Finally, our research is supported by simulation and experimental analyses of different combinations on the seven-DoF Franka Emika robot. The results show that the proposed friction has better accuracy performance, and the modified optimization algorithm can reduce the overall time required for the optimization process while maintaining the quality of the identification results.

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.

Design and Fabrication of SCARA for Image Processing in Industry

The scope of our project is the design and fabrication of SCARA (Selective Compliance Articulated Robot Arm) robots optimized for image processing tasks within industrial environments. This report presents a comprehensive review of the design, control, and applications of SCARA robots, focusing on their integration with image processing technologies to enhance industrial automation. We delve into the underlying principles governing the kinematics and dynamics of SCARA robots, elucidating their operational mechanisms and capabilities. Advancementsin end-effector design and sensor integration have enhanced the adaptability of SCARA robots, enabling them to tackle complex tasks in unstructured environments.

Investigation of Dynamic Characteristics of Holonomic Robot with Mecanum wheels Based on 3D Parametric Simulation Model

The paper analyzes dynamic characteristics of a four-wheeled mobile robot driven by omnidirectional mecanum wheels. To obtain information about dynamic characteristics of the robot, a computer model was developed in the MATLAB Simulink block modeling software environment. The constructed simulation model allowed to research the behavior of a holonomic mobile object with mecanum wheels in start-braking modes, based not only on its mathematical model, but also ta-king into account the weight and size parameters of the robot, introduced into the model through the integration of a three-dimensional digital prototype of the object. The investigation of the omnidirectional robot dynamics was considered within modeling the acceleration, uniform movement and deceleration of an object on a plane along a rectilinear trajectory. As a result, the dependences of kinematic, dynamic and mechanical characteristics were obtained, such as the dependence of angular velocity and torque of each wheel on time; the dependence of linear velocity of the robot's center of mass on time; the dependence of distance traveled by the robot on time; positioning errors while processing a given movement; spatial visualization of speed fluctuations. With the help of Mechanical Explorer tool, using a digital clone of the robot integrated into the model, an animation of the object’s movement along the trajectory was obtained. This approach, based on the integration of mathematical block and three-dimensional parametric models of the object with the possibility of visualization and animation of the results, allows to most fully investigate the dynamics and kinematics of nonlinear mechatronic systems. The obtained information of positioning errors and speed fluctuations in three coordinates allowed to conclude that there were random fluctuations during the robot’s movement, but their presence did not have a noticeable effect on the accuracy of the specified trajectory.

Seafloor Landing Dynamics of a Novel Underwater Robot Using ALE Algorithm

Seafloor landing research is crucial for underwater robots tasked with long-term fixed-point observation missions, particularly those requiring prolonged operations on the seabed. Developing safe and stable landing technology is essential for the success of these missions. This paper introduces a novel underwater robot by establishing its dynamic model and using the arbitrary Lagrangian–Eulerian (ALE) method to conduct a comprehensive study of its deep-sea landing process. Prior to detailed parameter analysis, the ALE algorithm’s effectiveness and accuracy were validated through experimental data and simulation results from previous studies. This study examines the penetration depth and force conditions during the landing process by varying factors such as seabed soil properties, landing velocity, and the robot’s mass. Findings indicate that seabed soil properties significantly influence penetration depth and pressure, with variations in soil density and strength affecting the robot’s landing behavior. In contrast, the robot’s mass has a relatively minor effect, suggesting that the choice of structural materials has limited impact on penetration depth and pressure during landing. Additionally, landing velocity was found to significantly affect penetration depth and pressure; higher velocities result in greater penetration depths and pressures. This highlights the importance of controlling landing velocity to minimize impact forces and protect the robot. The results of this study provide theoretical support and data for developing deep-sea landing technology for novel underwater robots and inform the selection of structural materials, ensuring the successful execution of long-term fixed-point observation missions in deep-sea environments.

Data-Driven Model-Free Adaptive Containment Control for Uncertain Rehabilitation Exoskeleton Robots with Input Constraints

This paper presents a data-driven model-free adaptive containment control (MFACC) scheme for uncertain rehabilitation exoskeleton robots, where the robotic exoskeleton dynamics are uncertain with saturation constraints. To handle uncertainties of the robotic dynamics, a model-free adaptive control (MFAC) strategy is established by linearizing the robotic exoskeleton dynamics into an equivalent data model. Considering the integral additive effect of the traditional MFAC method, an improved MFAC controller is designed in this paper. Since actuators with saturation constraints constantly affect the safety of patients during rehabilitation training, we construct a new criterion function with active constraints for the critical function of the MFAC algorithm and adopt the Hildreth quadratic programming algorithm to find the constrained optimal solution to overcome this limitation. The proposed MFACC scheme is rigorously proven by the compression mapping method to demonstrate model-free stability. Finally, the proposed control scheme is verified to be effective by simulation studies of the robotic SimMechanics model.

Enhancing Robot End‐Effector Trajectory Tracking Using Virtual Force‐Tracking Impedance Control

This article presents an extended Cartesian space robot control framework that features a virtual force tracking impedance control to enhance the end‐effector trajectory tracking performance. Initially, the concept of a virtual surface is introduced, which is assumed to be at some constant distance from the desired end‐effector trajectory. This virtual surface generates a virtual contact force when interacting with the torque‐controlled robot end‐effector. The interaction is then manipulated using an impedance control model to track a constant desired force. If the robot end‐effector deviates from the desired trajectory, the constant force‐tracking impedance control generates a compliance trajectory that regulates the end‐effector movements, constraining it to the desired trajectory. For robust force tracking, impedance parameters are optimally tuned using a closed‐loop dynamic model incorporating both robot and impedance dynamics. Additionally, super twisting sliding mode control (STSMC) is integrated to overcome uncertainties and the impact of robot dynamics on force‐tracking performance. Experimental validation confirms the theoretical claims of the proposed approach. It demonstrates that force‐tracking impedance control improves the end‐effector trajectory tracking by quickly reacting to the dynamic trajectories compared to position control only and effectively maintains it on the desired trajectories.

Enhanced tracking control for n-DOF robotic manipulators: A fixed-time terminal sliding mode approach with time delay estimation

This work proposes a fixed-time terminal sliding mode control scheme with time delay estimation (FxTSM-TDE) for unknown nonlinear robotic systems under uncertainties and unknown bounded disturbances. The idea of the fixed-time terminal sliding mode control method (FxTSM) is thoroughly explained and analyzed. The FxTSM approach has been used to achieve nonsingular fixed-time control, non-chatter control inputs, and superior trajectory tracking performance. Then, the proposed approach for estimating the unknown robot dynamics utilizes the TDE approach. The Lyapunov analysis is used to establish the fixed-time stability of the system. The effectiveness of the suggested approach is evaluated, demonstrated, and contrasted with the existing control schemes by illustrating the results of the proposed method's enhanced performance in tracking accuracy, robustness, and convergence speed when implemented in robotic manipulator dynamics. The results indicate that the existing scheme has corresponding root mean square (RMS) error values of 0.0235 and 0.0283, while the proposed scheme has values of 0.0178 and 0.0188, respectively.

3D dynamics and control of a snake robot in uncertain underwater environment

Abstract The snake robot can be used to monitor and maintain underwater structures and environments. The motion of a snake robot is achieved by lateral undulation which is called the gait pattern of the snake robot. The parameters of a gait pattern need to be adjusted for compensating environmental uncertainties. In this work, 3D motion dynamics of a snake robot for the underwater environment is proposed with vertical motion using the buoyancy variation technique and horizontal motion using lateral undulation. “The neutral buoyant snake robot motion in hypothetical plane and added mass effect is negligible”, these previous assumptions are removed in this work. Two different control algorithms are designed for horizontal and vertical motions. The existing super twisting sliding mode control (STSMC) is used for the horizontal serpentine motion of the snake robot. The control law is designed on a reduced-ordered dynamic system based on virtual holonomic constraints. The vertical motion is achieved by controlling the mass variation using a pump. The water pumps are controlled using the event-based controller or Proportional Derivative (PD) controller. The results of the proposed control technique are verified with various external environmental disturbances and uncertainties to check the robustness of the control approach for various path following cases. Moreover, the results of STSMC scheme are compared with SMC scheme to check the effectiveness of STSMC. The practical implementation of the work is also performed using Simscape Multibody environment where the designed control algorithm is deployed on the virtual snake robot.

Robot-assisted pedestrian evacuation in fire scenarios based on deep reinforcement learning

Indoor fires pose a significant challenge to the safe evacuation of pedestrians. In response to fire hazards, pedestrians instinctively seek alternative evacuation routes to avoid the hazard zone. However, the specific location and intensity of the fire hazard zone can influence pedestrians' decisions, leading to varying congestion levels in different areas. To address this challenge and enhance overall evacuation efficiency, this paper introduces an improved social force model to depict pedestrian movement in fire scenarios and proposes a methodology that leverages dynamic robot for pedestrian evacuation, employing Deep Reinforcement Learning (DRL) and Human-Robot Interaction (HRI). The results show that in the no-robot scenario, pedestrians will detour according to the varying locations of fire hazard zones and emergency levels, resulting in congestion at different positions. In the static robot scenario, robots placed in different locations exhibit varied effects on evacuation depending on the fire hazard zones' locations and intensities. In the DRL-control robot scenario, the robot controlled by DRL and HRL can always navigate to the appropriate position to promote evacuation, regardless of the fire's location and emergency levels or the robot's initial placement. Furthermore, our findings reveal that strategically positioned robots can enhance evacuation efficiency by alleviating crowding and increasing the distance between pedestrians and fire hazard zones in most cases, thereby improving pedestrian safety. This study offers practical guidance for managing pedestrian evacuation during fire incidents and establishes a theoretical foundation for refining evacuation strategies and safety measures at fire scenes.

Gait Generation of a 10-DOF Humanoid Robot on Deformable Terrain Based on Spherical Inverted Pendulum Model

Abstract Gait generation of a humanoid robot on deformable terrain is a complex problem as the foot and terrain interaction and terrain deformation has to be included in the dynamics. In order to simplify the dynamics of walk on deformable terrain, we used a spherical inverted pendulum (SIP) to represent the single support phase, in which the effect of terrain deformation is represented by a spring and damper contact model. The impact model for leg transition is derived from angular momentum conservation. In order to minimize the energy loss due to impact, the double support phase is modeled as a suspended pendulum. Based on the motion of the SIP model, the hip and leg trajectories of a 10 DOF humanoid robot are generated. The joint angles of the humanoid are obtained from inverse kinematics. The motion of the center of mass is analyzed by inverse dynamics of a floating-base robot. The proposed gait generation method has been experimentally validated using a Kondo KHR-3HV humanoid robot on deformable terrain. The results show that the humanoid can effectively track the trajectories of the SIP model.

Learning complex nonlinear dynamics of a 3D translational parallel manipulator using neural network

This study investigates modeling the dynamics of a 3D translational parallel manipulator with closed chains using feedforward neural networks (FFNNs). The dataset exceeds 50,000 samples, incorporating experimental data collected from a robot prototype using MATLAB® real-time workshop and the National InstrumentsTM DAQ toolbox, as well as CAD simulation data from MSC ADAMS software. While achieving satisfactory mean squared error (MSE), some predictions did not fully capture the manipulator's dynamics, with small overfitting observed. A Deep Neural Network (DNN) was tested but faced overfitting and high computational costs, despite being trained on a subset of the dataset. This highlighted the limitations of DNNs for modeling such complicated parallel robots with closed chains and parallelograms. FFNNs were preferred for their simplicity and lower overfitting risk. L2 regularization and k-fold validation were applied to improve performance. Transfer learning (TL) was also employed, fine-tuning a new network with weights from pre-trained FFNNs using a smaller, unseen dataset. This approach significantly reduced MSE and completely eliminated overfitting, demonstrating the effectiveness of TL in refining model performance for forward and inverse dynamics. These findings suggest that FFNNs, combined with TL, L2 regularization, and k-fold validation, offer a robust method for accurately modeling complex robotic dynamics, enhancing control and optimization strategies for complicated robotic systems. Training for all networks was conducted within the MATLAB® environment.

Innovations and Challenges in Soft Robotic Dynamics: Advanced Modeling, Interaction, and Control Mechanisms

Innovations and Challenges in Soft Robotic Dynamics: Advanced Modeling, Interaction, and Control Mechanisms

Dynamics Parameter Identification of Articulated Robot

Dynamics parameter identification in the establishment of a multiple degree-of-freedom (DOF) robot’s dynamics model poses significant challenges. This study employs a non-symbolic numerical method to establish a dynamics model based on the Newton–Euler formula and then derives a proper dynamics model through decoupling. Initially, a minimum inertial parameter set is acquired by using QR decomposition, with the inclusion of a friction model in the robot dynamics model. Subsequently, the least squares method is employed to solve for the minimum inertial parameters, forming the basis for a comprehensive robot dynamics parameter identification system. Then, after the optimization of the genetic algorithm, the Fourier series trajectory function is used to derive the trajectory function for parameter identification. Validation of the robot’s dynamics parameter identification is performed through simulation and experimentation on a 6-DOF robot, leading to a precise identification value of the robot’s inertial parameters. Furthermore, two methods are employed to verify the inertia parameters, with analysis of experimental errors demonstrating the effectiveness of the robot dynamics parameter identification method. Overall, the effectiveness of the entire calibration system is verified by experiments, which can provide valuable insights for practical engineering applications, and a complete and effective robot dynamics parameter identification scheme for a 6-DOF robot is established and improved.

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.

Soft Robots: Computational Design, Fabrication, and Position Control of a Novel 3-DOF Soft Robot

This paper presents the computational design, fabrication, and control of a novel 3-degrees-of-freedom (DOF) soft parallel robot. The design is inspired by a delta robot structure. It is engineered to overcome the limitations of traditional soft serial robot arms, which are typically low in structural stiffness and blocking force. Soft robotic systems are becoming increasingly popular due to their inherent compliance match to that of human body, making them an efficient solution for applications requiring direct contact with humans. The proposed soft robot consists of three soft closed-loop kinematic chains, each of which includes a soft actuator and a compliant four-bar arm. The complex nonlinear dynamics of the soft robot are numerically modeled, and the model is validated experimentally using a 6-DOF electromagnetic position sensor. This research contributes to the growing body of literature in the field of soft robotics, providing insights into the computational design, fabrication, and control of soft parallel robots for use in a variety of complex applications.