Robot Manipulator Research Articles

Noise-immune zeroing neural dynamics for dynamic signal source localization system and robotic applications in the presence of noise

Time angle of arrival (AoA) and time difference of arrival (TDOA) are two widely used methods for solving dynamic signal source localization (DSSL) problems, where the position of a moving target is determined by measuring the angle and time difference of the signal's arrival, respectively. In robotic manipulator applications, accurate and real-time joint information is crucial for tasks such as trajectory tracking and visual servoing. However, signal propagation and acquisition are susceptible to noise interference, which poses challenges for real-time systems. To address this issue, a noise-immune zeroing neural dynamics (NIZND) model is proposed. The NIZND model is a brain-inspired algorithm that incorporates an integral term and an activation function into the traditional zeroing neural dynamics (ZND) model, designed to effectively mitigate noise interference during localization tasks. Theoretical analysis confirms that the proposed NIZND model exhibits global convergence and high precision under noisy conditions. Simulation experiments demonstrate the robustness and effectiveness of the NIZND model in comparison to traditional DSSL-solving schemes and in a trajectory tracking scheme for robotic manipulators. The NIZND model offers a promising solution to the challenge of accurate localization in noisy environments, ensuring both high precision and effective noise suppression. The experimental results highlight its superiority in real-time applications where noise interference is prevalent.

Artificial Intelligence in Robotic Manipulators: Exploring Object Detection and Grasping Innovations

The importance of deep learning has heralded transforming changes across different technological domains, not least in the enhancement of robotic arm functionalities of object detection’s and grasping. This paper is aimed to review recent and past studies to give a comprehensive insight to focus in exploring cutting-edge deep learning methodologies to surmount the persistent challenges of object detection and precise manipulation by robotic arms. By integrating the iterations of the You Only Look Once (YOLO) algorithm with deep learning models, our study not only advances the innovations in robotic perception but also significantly improves the accuracy of robotic grasping in dynamic environments. Through a comprehensive exploration of various deep learning techniques, we introduce many approaches that enable robotic arms to identify and grasp objects with unprecedented precision, thereby bridging a critical gap in robotic automation. Our findings demonstrate a marked enhancement in the robotic arm’s ability to adapt to and interact with its surroundings, opening new avenues for automation in industrial, medical, and domestic applications. The impact of this research extends lays the groundwork for future developments in robotic autonomy, offering insights into the integration of deep learning algorithms with robotic systems. This also serves as a beacon for future research aimed at fully unleashing the potential of robots as autonomous agents in complex, real-world settings.

Time-Synchronized Fault-Tolerant Control for Robotic Manipulators

This article proposes a time-synchronized fault-tolerant convergence control method for n-degree-of-freedom robotic manipulators. The main challenge lies in driving the tracking errors of all joints to converge simultaneously, especially in the presence of system faults, external disturbances, and model uncertainties. We introduce a normalized sign function that guarantees the property of ratio persistence for all joints and plays a crucial role in time-synchronized convergence control. A time-synchronized convergence observer is proposed that not only adopts a time-synchronized convergence control framework but also overcomes the lumped uncertainty term, which includes the system fault components, external disturbances, and system uncertainties. A salient feature of this method is that, regardless of the initial state and various uncertainties, each component of the robot manipulator system can simultaneously converge to an equilibrium point. Simulations conducted on a two-link robotic manipulator demonstrate the notable benefits of the designed time-synchronized control method, as evidenced by the comparative results.

Path planning strategy for a space relocatable robotic manipulator based on improved GBNN algorithm

Path planning strategy for a space relocatable robotic manipulator based on improved GBNN algorithm

Plant-inspired decentralized controller for robust orientation control of soft robotic manipulators.

Due to the complexity of deformations in soft manipulators, achieving precise control of their orientation is particularly challenging, especially in the presence of external disturbances and human interactions. Inspired by the decentralized growth mechanism of plant gravitropism, which enables plants' roots and stems to grow in the direction of gravity despite complex environmental interactions, this study proposes a decentralized control strategy for robust orientation control of multi-segment soft manipulators. This gravitropism-inspired decentralized controller was validated through simulations for convergence and robustness, and benchmarked against the traditional inverse Jacobian-based controller on a large-scale multi-segment soft manipulator. Experimental results demonstrate that the decentralized controller achieves comparable convergence and better control precision to the inverse Jacobian-based controller, while significantly outperforming it in disturbance rejection. Even in the presence of partial damage and human interaction, the decentralized controller provides robust control. This study provides a robust new approach for managing disturbances in complex environments, laying the foundation for further exploration of decentralized control strategies in soft robotics.

Designing Spiking Neural Network-Based Reinforcement Learning for 3D Robotic Arm Applications

This study investigates a novel approach to robotic arm control through integrating spiking neural networks with the twin delayed deep deterministic policy gradient reinforcement learning algorithm. Specifically, it presents the first application of spiking neural networks-based twin delayed deep deterministic policy gradient in 3D robotic manipulation, demonstrating its extension from traditional 2D tasks to complex 3D target-reaching scenarios with improved energy efficiency and stability. Additionally, with the inertial measurement unit data the system successfully mimics human arm movements, achieving a success rate of 0.95 among 50 trials and enabling an intuitive and accurate human–robot interaction system. This pioneering attempt highlights the feasibility of combining the biologically inspired spiking neural networks with the reinforcement learning algorithm to address the real-time challenges in high-dimensional robotic environments and advance the field of human–robot interaction systems.

Quantum-Inspired Sliding-Mode Control to Enhance the Precision and Energy Efficiency of an Articulated Industrial Robotic Arm

Maintaining precise and robust control in robotic systems, particularly those with nonlinear dynamics and external disturbances, is a significant challenge in robotics. Sliding-mode control (SMC) is a widely used technique to tackle these issues; however, it is plagued by chattering and computational complexity, which limit its effectiveness in high-precision environments. This study aims to develop and assess a quantum-inspired sliding-mode control (QSMC) strategy to enhance the SMC’s robustness, precision, and computational efficiency, specifically in controlling a six-jointed articulated robotic arm. The methodology involves creating a comprehensive kinematic and dynamic model of the robot, followed by implementing both classic SMC and the proposed Q-SMC in a comparative way. The simulation results confirm that the Q-SMC method outperforms the classic SMC, particularly in reducing chattering, improving tracking accuracy, and decreasing energy consumption by approximately 3.79%. These findings suggest that the Q-SMC technique provides a promising alternative to classical control methods, with potential applications in tasks requiring high precision and efficient robotic manipulations.

Scene_synthesizer: A Python Library for Procedural Scene Generation in Robot Manipulation

scene_synthesizer: A Python Library for Procedural Scene Generation in Robot Manipulation

Compensating the Symptomatic Increase in Plantarflexion Torque and Mechanical Work for Dorsiflexion in Patients with Spastic Paresis Using the “Hermes” Ankle–Foot Orthosis

Background/Objectives: “Hermes” is an ankle–foot orthosis (AFO) with negative stiffness designed to mechanically compensate the symptomatic increase in plantarflexion (PF) torque (i.e., ankle joint torque resistance to dorsiflexion, DF) in patients with spastic paresis. Methods: The effectiveness of “Hermes” was evaluated in twelve patients with chronic unilateral spastic paresis after stroke. Using a robotic ankle manipulator, stiffness at the ankle joint was assessed across three conditions: ankle without Hermes (A), ankle with Hermes applying no torque compensation (A+H0%), and ankle with Hermes tuned to compensate 100% of the patients’ ankle joint stiffness (A+H100%). Results: A significant reduction in PF torque was found with Hermes applying compensation (A+H100%) compared to the conditions without Hermes (A) and with Hermes applying no compensation (A+H0%). Furthermore, a significant reduction in positive dorsiflexion work was found with Hermes applying compensation (A+H100%) compared to the condition with Hermes applying no compensation (A+H0%). Hermes did not significantly contribute to additional PF torque or positive work when applying no compensation (A+H0%). Conclusions: The reductions in PF torque achieved with Hermes are comparable to those seen with repeated ankle stretching programs and ankle robot training. Thus, Hermes is expected to assist voluntary dorsiflexion and improve walking in patients with spastic paresis.

Multistage Synchronous Telescopic Manipulator With End‐Effector–Biased Rotating‐Pulling Mode for Damage‐Free Robotic Picking

ABSTRACTFruit picking is one of the most time‐consuming and labor‐intensive stages of fruit production, characterized by high labor demands and significant labor costs. Traditional fruit‐picking robotic manipulators typically adopt configurations similar to general‐purpose industrial robots, following a predefined path and employing a direct‐pulling mode to detach the fruit. However, due to the constraints of the orchard environment and the varying conditions of the fruit, manipulators should be designed to accommodate the specific horticultural characteristics of the trees to improve picking efficiency. Additionally, the picking process should be optimized based on the biological characteristics of the fruit to ensure quality. In this study, a five‐degree‐of‐freedom manipulator based on a multistage synchronous telescopic mechanism is proposed for fruit picking. Workspace analysis indicates that the manipulator can cover more than 80% of the fruit distribution on the trees. To ensure motion accuracy, a FreeRTOS‐based motion control system is developed for the manipulator. To evaluate picking efficiency and quality, fruit‐picking experiments are conducted in an apple orchard. A rope‐driven, three‐finger end‐effector is mounted in a biased position at the end of the manipulator, complemented by an RGB‐D camera for fruit detection and a ROS‐based control system for robotic operation. The performance of two picking modes (direct‐pulling and biased rotating‐pulling) are compared in these experiments. The results demonstrate that the biased rotating‐pulling mode yields a higher picking success rate and a lower stem damage rate compared with the direct‐pulling mode. Specifically, the damage‐free success rate for the biased rotating‐pulling mode is 80%, with a 9.18% reduction in stem damage compared with the direct‐pulling mode. Furthermore, the average picking cycle time is approximately 14.5 s. In conclusion, the manipulator and its motion control system successfully achieve efficient, nondestructive fruit picking with a high success rate, offering valuable insights for the development of fully automated fruit‐picking robots in the future.

Optimizing Robotic Manipulation With Decision-RWKV: A Recurrent Sequence Modeling Approach for Lifelong Learning

Abstract Models based on the transformer architecture have seen widespread application across fields such as natural language processing (NLP), computer vision, and robotics, with large language models (LLMs) like ChatGPT revolutionizing machine understanding of human language and demonstrating impressive memory capacity and reproduction capabilities. Traditional machine learning algorithms struggle with catastrophic forgetting, detrimental to the diverse and generalized abilities required for robotic deployment. This article investigates the receptance weighted key value (RWKV) framework, known for its advanced capabilities in efficient and effective sequence modeling, integration with the decision transformer (DT), and experience replay architectures. It focuses on potential performance enhancements in sequence decision-making and lifelong robotic learning tasks. We introduce the decision-RWKV (DRWKV) model and conduct extensive experiments using the D4RL database within the OpenAI Gym environment and on the D’Claw platform to assess the DRWKV model’s performance in single-task tests and lifelong learning scenarios, showing its ability to handle multiple subtasks efficiently. The code for all algorithms, training, and image rendering in this study is available online (open source).

Guaranteed performance adaptive fixed-time neural control for robot manipulators with disturbance observer

This paper studies the fixed-time tracking control scheme for robot manipulators within the constraints of prescribed performance bounds (PPB). Firstly, a disturbance observer with exponential convergence performance is developed to estimate the lumped disturbances and actuator failures, a radial basis function neural network (RBFNN) is introduced to reduce the estimation error which generated by the observer. Based on prescribed performance function, an adaptive fixed-time fault-tolerant control (AFFTC) is developed using the back-stepping control framework. This approach does not necessitate complete information about the robot manipulator’s dynamic model, thereby making its implementation in practical, real-life situations more accessible. The stability of the proposed system is established via the Lyapunov criterion and the feasibility of the observer is validated.

An enhanced neuro-adaptive PID sliding mode control for robot manipulators:promoting sustainable automation

An enhanced neuro-adaptive PID sliding mode control for robot manipulators:promoting sustainable automation

Adaptive-constrained admittance control for physical human–robot interaction

In this paper, an adaptive constrained admittance control scheme is proposed, which can effectively solve physical human–robot interaction (pHRI) tasks with output constraints. To ensure the safety of robot behavior, the constraint controller is designed in the trajectory planning layer and the control layer. The asymmetric soft saturation function (ASSF) is designed to obtain variable compliant motion trajectories generated from the desired admittance model. In addition, the controller based on the asymmetric integral barrier Lyapunov function (AIBLF) is designed to deal directly with asymmetric Cartesian space constraints. Finally, radial basis function neural network (RBFNN) is utilized to approximate the dynamics uncertainty of the robot manipulator and to improve the tracking accuracy. According to the Lyapunov stability principles, it can be proved that all states of the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB). Finally, the effectiveness of the proposed control scheme is demonstrated by several simulations and experiments.

Depth-sensor-based shared control assistance for mobility and object manipulation: toward long-term home-use of BCI-controlled assistive robotic devices.



Objective. Assistive robots can be developed to restore or provide more autonomy for individuals with motor impairments. In particular, power wheelchairs can compensate lower-limb impairments, while robotic manipulators can compensate upper-limbs impairments. 
Recent studies have shown that Brain-Computer Interfaces (BCI) can be used to operate this type of devices.
However, activities of daily living and long-term use in real-life contexts such as home require robustness and adaptability to complex, changing and cluttered environments which can be problematic given the neural signals that do not always allow a safe and efficient use. Approach. This article describes assist-as-needed sensor-based shared control methods relying on the blending of BCI and depth-sensor-based control.
The proposed assistance targets the BCI-teleoperation of effectors for tasks that answer mobility and manipulation needs in a at-home context. Main Results. The assistance provided by the proposed methods was evaluated through a wheelchair mobility and reach-and-grasp laboratory-based experiments in a controlled environment, as part of a clinical trial with a quadriplegic patient implanted with a wireless 64-channel ElectroCorticoGram (ECoG) recording implant named WIMAGINE.
Results showed that the proposed methods can assist BCI users in both tasks.
Indeed, the time to perform the tasks and the number of changes of mental tasks were reduced. Moreover, unwanted actions, such as wheelchair collisions with the environment, and gripper opening that could result in the fall of the object were avoided. 
Significance. The proposed methods are steps toward at-home use of BCI-teleoperated assistive robots. Indeed, the proposed shared control methods improved the performance of the two assistive devices. Clinical trial, registration number: NCT02550522.

A Comprehensive Review Based on Analysis Modeling, Mechanical Vibration Control Strategies, and Optimization Methods of Robotics Flexible Manipulator Structures

Flexible Manipulators (FMs) provide a number of benefits, containing reduced weight due to the thinness of the robot’s linkages. Although the initial plan was to use actual robots’ flexibility or slenderness to their advantage, the complex dynamics of the systems piqued interest in using an experimental flexible manipulator as a testing ground for various modeling or control strategies. A review is essential for researchers who want to align their study objectives with those of the field because the literature is extensive and diverse. Due to the widespread usage of flexible manipulators in different mechatronic and robotic applications over the past few decades, many academics worldwide are now interested in researching this topic. Studies are categorized here according to the control and modeling technologies of flexible manipulators and methodologies. Review of recent works on analysis, modeling, mechanical vibration, control algorithms, gyroscope technology and applications, difficulties in managing flexible manipulators and their anticipated future, and the majority of the notable evolutionary and optimization algorithms, including Genetic Algorithm (GA), Differential Evolution (DE), and Fuzzy Logic (FL), as well as modification approaches and techniques, are discussed and underlined. This study examines many publications, thoroughly reviewing the analytical, mathematical, dynamical modeling, mechanical vibration control techniques and most of the notable evolutionary and optimization algorithmic approaches of Robotic Flexible Manipulator (RFM) structures.

Robust motion control based on model-based unknown system dynamics estimator for robot manipulators

Robust motion control based on model-based unknown system dynamics estimator for robot manipulators

Application of Discrete Exterior Calculus Methods for the Path Planning of a Manipulator Performing Thermal Plasma Spraying of Coatings

This paper presents a new method of path planning for an industrial robot manipulator that performs thermal plasma spraying of coatings. Path planning and automatic generation of the manipulator motion program are performed using preliminary 3D surface scanning data from a laser triangulation distance sensor installed on the same robot arm. The new path planning algorithm is based on constructing a function of the geodesic distance from the starting curve. A new method for constructing a geodesic distance function on a surface is proposed, based on the application of Discrete Exterior calculus methods, which is characterized by a high computational efficiency. The developed algorithms and their software implementation were experimentally tested with the robotic microplasma spraying of a protective coating on the surface of a jaw crusher plate, which was then successfully operated for crushing mineral-based raw materials.

Adaptive neural network tracking control for robotic manipulator with input dead zone and function constraints on states

Adaptive neural network tracking control for robotic manipulator with input dead zone and function constraints on states

Optimization of manipulator inverse kinematics using improved particle swarm algorithm for enhanced manufacturing efficiency

In the context of digital manufacturing and intelligent manufacturing systems, optimizing the inverse kinematics (IK) of manipulators is crucial for improving manufacturing efficiency and productivity. The authors propose an improved particle swarm optimizer (IPSO) with adaptive inertia weight and asynchronous learning factors to enhance the optimization performance. The proposed algorithm introduces a novel mutation strategy where selected initial particles undergo mutation to generate new particles, thereby increasing swarm diversity. The best-performing particles after mutation replace the initial ones to improve global optimization capability. The effectiveness of the IPSO algorithm is evaluated through thirteen classical benchmark functions and compared with genetic algorithms (GA), standard particle swarm optimization (PSO), linear decreasing weight PSO (LDWPSO), and biogeography-based PSO (BLPSO), quantum-behaved particle swarm optimization (QPSO), Levy Flight quantum-behaved particle swarm optimization (LQPSO), Gaussian quantum-behaved particle swarm optimization approaches (GQPSO). Experimental mean results demonstrate that the IPSO algorithm outperforms these algorithms. The IPSO algorithm and the other algorithms subsequently were employed to estimate the performance for tackling the IK problem of a manipulator and the comparison results demonstrate the superior performance of IPSO algorithm in terms of accuracy and convergence rate. Finally, the practical efficacy of the IPSO algorithm was validated through experimental trials conducted on a custom-designed six-degree-of-freedom (6-DOF) collaborative robotic manipulator platform, which demonstrated the viability of IPSO algorithm in real-world manufacturing applications. The findings suggest that the proposed IPSO algorithm offers a promising solution for enhancing manipulator control efficiency in digital manufacturing systems. The IPSO code is available at: https://10.6084/m9.figshare.27753549 .