Robot Manipulator Research Articles

ABSTRACT This work focuses on the trajectory tracking control of robot manipulators subject to model uncertainties and unknown additive disturbances. The controller design makes use of a self‐adjusting adaptive fuzzy logic‐based term, fused with a robust integral of the sign of the error feedback. In the proposed adaptive fuzzy logic framework, means and variances of the membership functions are updated dynamically during each iteration, allowing for a more precise estimation of the parametric uncertainties. The stability of the closed‐loop system and the convergence properties of the states are established via Lyapunov‐based arguments, where asymptotic stability of the joint tracking error is ensured. Numerical simulations have been conducted to further support the theoretical findings.

If elastic elements are introduced into the mechanical architecture of a robotic manipulator, a free vibration response (Natural Motion) arises that can be exploited to reduce energy consumption in cyclic motions, such as pick-and-place tasks. In this work, this approach is applied to the RR-4R-R manipulator, which is derived from the SCARA robot by replacing the prismatic joint that drives the vertical motion of the end-effector with a four-bar mechanism. This mechanical modification lowers friction and facilitates the introduction of a balancing elastic element. If the elastic element is designed to provide indifferent equilibrium at any position (exact elastic balancing), the actuators need only to overcome the inertial forces; this approach is convenient for slow motions. Conversely, if the elastic element balances gravity exactly only in the median vertical position of the end-effector, Natural Motion around this position arises, and it can be exploited to reduce energy consumption in fast cyclic motions, where inertial forces become prevalent. The threshold of convenience between exact balancing and natural balancing has been evaluated for the RR-4R-R robot by means of a multibody model, assessing different performance indices: the maximum torque of the four-bar actuator, the integral control effort, and the mechanical energy. The simulation campaign was carried out considering different trajectory shapes and the influence of finite stop phases, highlighting the potential benefits of exploiting Natural Motion in robotized manufacturing lines.

ABSTRACT This paper proposes a control approach for achieving practical predefined‐time stabilization in a class of nonlinear systems influenced by matched bounded disturbances and unknown nonlinearities. Radial basis function (RBF) neural networks (NNs) are used for function approximation to tackle these uncertainties. By applying Lyapunov stability theory, we show that the proposed time‐varying control law and adaptive mechanism ensure the convergence of the system states to a region around the equilibrium within a predefined time, while all closed‐loop signals remain bounded. The effectiveness of the approach is validated through simulations on a single‐link flexible‐joint robotic manipulator.

ABSTRACT In this paper, an adaptive dynamic threshold event‐triggered fixed‐time control is proposed for multiple uncertain robotic manipulators with position constraints on the basis of a dynamic gain function. First, a novel dynamic threshold fixed‐time prescribed performance control (DFTPPC) strategy that mixes a time‐varying function with fixed‐time prescribed performance control, which allows the error to be reduced again, is designed. On this basis, the barrier Lyapunov function (BLF) is designed in combination with the hyperbolic tangent function to obtain the system state constraints. Second, the dynamic gain function (DGF) is designed to improve the sensitivity of the error in the feedback control and the anti‐interference ability of the system. Third, a dynamic event‐triggered mechanism (DETM) is designed by incorporating a time‐varying scaling function and an exponentially decreasing term to not only reduce communication resources but also achieve stronger robustness. Finally, an analysis of the theory and comparative simulation experiments verifies that the proposed strategy is feasible.

The article describes the development of a software package entitled «Computer Simulation of HighOrder Nonlinear Systems Control via Feedback», which integrates five illustrative examples: a third-order nonlinear system, motion control of a single-link robotic manipulator, a PID controller for a nonlinear system, output regulation of uncertain time-varying nonlinear systems, and global feedback control of high-order timedelayed time-varying nonlinear systems. The results of the examples are obtained through computer simulation, with Python version 3.12.1 chosen as the primary programming language due to its proven effectiveness as a tool for demonstrating and validating system performance. The paper highlights the applicability of Python libraries and modules, emphasizing that Python remains one of the most suitable programming languages for scientific computing, data science, and machine learning. It enhances efficiency and performance by combining low-level libraries with clear high-level APIs. The developed software package ensures the stability of high-order time-varying nonlinear systems, thereby contributing to the stability, efficiency, and control quality of real engineering systems. It is designed for effective use in scientific research, educational processes, industrial automation, and robotics. The adaptability of the package allows users to modify example parameters according to their requirements, independently analyze the outcomes, and explore the results in detail.

Electrochemical impedance spectroscopy for pear ripeness detection and integration with robotic manipulators

Sim-to-real transfer via a Style-Identified Cycle Consistent Generative Adversarial Network: Zero-shot deployment on robotic manipulators through visual domain adaptation

Effects of robot arm design and movement speed during human-robot interaction.

Tracking of discrete-time unmodeled reference signals in robotic manipulators using output regulation theory and high-gain observers

A Unified Arbitrarily Predefined -Time Convergent Recurrent Neural Network for Motion Control of Redundant Robot Manipulators: A Unified Paradigm

Conditions for Guaranteeing Nonovershooting Control of MIMO Nonlinear Systems With Application to Robot Manipulator

Uncertainty-Guided Robotic Manipulation Through Variational Information Bottleneck in Imitation Learning

HG2P: Hippocampus-inspired high-reward graph and model-free Q-gradient penalty for path planning and motion control.

A Dual Quaternion Approach to Kinematic Modeling and Evaluation of PRR-Type Robotic Manipulators

This paper addresses the challenge of enabling robots to autonomously prepare meals by bridging natural language recipe instructions and robotic action execution. We propose a novel methodology leveraging Actionable Knowledge Graphs to map recipe instructions into six core categories of robotic manipulation tasks, termed Action Cores cutting, pouring, mixing, preparing, pick and place, and cook and cool. Each AC is subdivided into Action Groups which represent a specific motion parameterization required for task execution. Using the Recipe1M + dataset (Marín et al., IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 43, 187–203), encompassing over one million recipes, we systematically analysed action verbs and matched them to ACs by using direct matching and cosine similarity, achieving a coverage of 76.5%. For the unmatched verbs, we employ a neuro-symbolic approach, matching verbs to existing AGs or generating new action cores utilizing a Large Language Model Our findings highlight the versatility of AKGs in adapting general plans to specific robotic tasks, validated through an experimental application in a meal preparation scenario. This work sets a foundation for adaptive robotic systems capable of performing a wide array of complex culinary tasks with minimal human intervention.

The six-degree-of-freedom (6-DoF) interaction forces/torque of the tool-end play an important role in the robotic tool manipulation using a gripper, which are usually indirectly measured by a robot wrist force/torque sensor. However, the real-time decoupling of the tool’s inertial force remains a challenge when different tools and grasping postures are involved. This paper presents a universal tool-end interaction forces estimation approach, which is capable of handling diverse grippers and tools. Firstly, to address uncertainties from varying tools and grasping postures, an online-identifiable tool dynamics model was built based on the Newton–Euler approach for the integrated gripper–tool system. Sensor zero-drift caused by factors such as the tool weight and prolonged operation is incorporated into the dynamic model and identified online in real time, enabling a coarse estimation of the interaction forces. Secondly, a spiking neural network (SNN) is specially employed to compensate for uncertainties caused by the wrist sensor creep effect, since its temporal processing and event-driven characteristics match the time-varying creep effects introduced by tool changes. The proposed method is experimentally validated on a robotic arm with a gripper, and the results show that the root mean square errors of the estimated tool-end interaction forces are below 0.5 N with x, y, and z axes and 0.03 Nm with τx, τy, and τz axes, which has a comparable precision with the in situ measurement of the interaction forces at the tool-end. The proposed method is further applied to robotic scraper manipulation with impedance control, achieving the interaction forces feedback during compliant operation precisely and rapidly.

Background. The clinical data of three patients who underwent robotic procedures for ovarian teratoma removal were retrospectively analyzed to investigate the feasibility and safety of this technology in children. Material and methods. The study included children under 18 years of age who underwent surgery using the new Versius robotic platform manufactured by CMR Surgical, UK. The indication for surgery was an established diagnosis of ovarian teratoma. In all cases, three robotic ports were used: one 12-mm trocar for optics (inserted at the level of the umbilicus in an open manner) and two 5-mm trocars for robotic manipulators. An additional 5-mm laparoport was installed for an assistant surgeon to work with during the robotic surgery. In all cases, the principle of ovarian-preserving surgery was observed, which included resection of the formation within healthy tissues, as well as minimal use of coagulation to prevent electrical injury to healthy ovarian tissue and follicles. The surgery involved dissection of the ovarian tunica albuginea over the tumor and enucleation of the formation. During the study, perioperative patient parameters related to demographic data, surgical details, and early and late complications were recorded. Results. The average age of patients at the time of surgery was 11.3±1.5 years (median 11.0 [11.0; 12.0] years), average weight 52.0±12.5 kg (median 56.0 [47.0; 59.0] kg). The affected side was predominantly left (1:2). The average duration of surgery was 85.0±15.0 min (median 85.0 [78.0; 93.0] min), robot installation time was 10—15 minutes. Patients were transferred to the surgery department on average 3.3±0.6 hours (median 3.0 [3.0; 4.0] hours) after surgery. The operations were not accompanied by conversions to laparoscopic or open surgeries. During the surgeries, no complications associated with bleeding from the ovarian tissue or damage to adjacent structures were noted. The average hospital stay was 4.6±2.0 days (median — 4.0 [4.0; 6.0] days). In the remote period at 1, 3, 6 and 12 months after the operation, no significant complications in the form of relapse of disease symptoms were noted. Conclusion. The initial experience of performing robot-assisted operations confirmed that robotics can be safely and effectively used in children with ovarian teratomas.

A method for compiling object schemes is proposed, which allows constructing algorithms for calculating the kinematic parameters of robotic manipulators. Examples of compiling object schemes for calculating the velocities and accelerations of points selected on the links of the robotic manipulator are considered. An analysis of the computational complexity of the obtained algorithms is carried out and a method for increasing their computational efficiency is proposed. An increase in computational efficiency is achieved based on the use of the associativity property due to the reduction of additional and multiplication operations performed by the algorithm. Graphs of computational processes illustrating the developed algorithms are presented. The developed algorithms allow parallel calculations; this will further increase the efficiency of calculations when using multiprocessor computing systems. As a result of the study, based on the object approach, an effective universal method for calculating the kinematic parameters of robotic manipulators has been developed. This will improve the quality of robot control.

Heterogeneous multirobot systems show great potential in complex tasks requiring coordinated hybrid cooperation. However, existing methods that rely on static or task‐specific models often lack generalizability across diverse tasks and dynamic environments. This highlights the need for generalizable intelligence that can bridge high‐level reasoning with low‐level execution across heterogeneous agents. To address this, a hierarchical multimodal framework that integrates a prompted large language model (LLM) with a fine‐tuned vision‐language model (VLM) is proposed. At the system level, the LLM performs hierarchical task decomposition and constructs a global semantic map, while the VLM provides semantic perception and object localization, where the proposed GridMask significantly enhances the VLM's spatial accuracy for reliable fine‐grained manipulation. The aerial robot leverages this global map to generate semantic paths and guide the ground robot's local navigation and manipulation, ensuring robust coordination even in target‐absent or ambiguous scenarios. The framework is validated through extensive simulation and real‐world experiments on long‐horizon object arrangement tasks, demonstrating zero‐shot adaptability, robust semantic navigation, and reliable manipulation in dynamic environments. To the best of our knowledge, this work presents the first heterogeneous aerial–ground robotic system that integrates VLM‐based perception with LLM‐driven reasoning for global high‐level task planning and execution.

This study develops four enhanced adaptive finite-time control strategies for high-order nonlinear maritime systems, addressing critical limitations in conventional backstepping controllers when applied to vehicles operating under uncertain oceanic conditions. The research proposes four advanced control strategies: multilayer adaptive control, neural network-based adaptive control, model predictive control (MPC) combined with backstepping, and event-triggered control employing barrier Lyapunov functions. These methods have been validated through simulations on a third-order nonlinear system and a cart-pendulum system. Comprehensive MATLAB simulations on third-order nonlinear systems demonstrate substantial performance improvements: the neural network controller achieves 88% faster convergence with 93.4% higher tracking accuracy, the MPC-backstepping approach reduces control energy by 32.7%, and the event-triggered method cuts computational load by 93% while maintaining strict state constraints. Quantitative analysis reveals steady-state error reductions from 0.085 to 0.014 (83.5% improvement) and settling time decreases from 1.2 s to 0.144 s (88% improvement) compared to conventional finite-time backstepping controllers. Furthermore, the proposed controllers were experimentally validated on practical applications, including robotic manipulators, quadrotor UAVs, and industrial hydraulic systems, exhibiting outstanding performance in highly nonlinear and noisy environments.