Search Results (64)

Accurate kinematic parameter identification is essential for space manipulators to attain millimeter-level positioning accuracy and robust motion control. This study develops a universal strategy for comprehensive parameter identification by establishing a generalized geometric error model using Denavit–Hartenberg (DH) parameterization. For robotic calibration, the Fibonacci spiral sampling technique optimizes pose selection, ensuring end-effector poses fully cover the manipulator’s workspace to enhance identification convergence. By combining the local convergence capability of the Levenberg–Marquardt (LM) algorithm with the global search characteristics of Particle Swarm Optimization (PSO), we propose a novel hybrid PSO-LM optimization algorithm, achieving synergistic enhancement of global exploration and local refinement. An experimental platform using a laser tracker as the metrology reference was constructed, with a 6-degree-of-freedom (6-DOF) space manipulator selected as a validation case. Experimental results demonstrate that the proposed method significantly reduces the average positioning error from 10.87 mm to 0.47 mm, achieving a 95.7% improvement in relative accuracy. These findings validate that the parameter identification approach can precisely determine the actual geometric parameters of space manipulators, providing critical technical support for high-precision on-orbit operations. Full article

High-precision identification of dynamic parameters is crucial for the on-orbit performance of space manipulators. This paper investigates dynamic modeling and parameter identification under special environmental conditions such as microgravity and vacuum. First, a dynamic model of the manipulator incorporating a nonlinear friction term is established using the Newton-Euler method, and an improved Stribeck friction model is proposed to better characterize high-speed conditions and space environmental effects. On this basis, a hybrid parameter identification method combining Particle Swarm Optimization (PSO) and Levenberg–Marquardt (LM) algorithms is proposed to balance global search capability and local convergence accuracy. To enhance identification performance, Fourier series are used to design excitation trajectories, and their harmonic components are optimized to improve the condition number of the observation matrix. Experiments conducted on a ground test platform with a six-degree-of-freedom (6-DOF) manipulator show that the proposed method effectively identifies 108 dynamic parameters. The correlation coefficients between predicted and measured joint torques all exceed 0.97, with root mean square errors below 5.1 N·m, demonstrating the high accuracy and robustness of the method under limited data samples. The results provide a reliable model foundation for high-precision control of space manipulators. Full article

The art of folding paper, named “origami”, has transformed from serving religious and cultural purposes to various educational and entertainment purposes in the modern world. Significantly, the fundamental folds and creases in origami, which enable the creation of 3D structures from a simple flat sheet with unique crease patterns, serve as a great inspiration in engineering applications such as deployable mechanisms for space exploration, self-folding structures for exoskeletons and surgical procedures, micro-grippers, energy absorption, and programmable robotic morphologies. Therefore, this paper will provide a systematic review of the state-of-the-art origami-inspired structures that have been adopted and exploited in robotics design and operation, called origami-inspired robots (OIRs). The advantages of the flexibility and adaptability of these folding mechanisms enable robots to achieve agile mobility and shape-shifting capabilities that are suited to diverse tasks. Furthermore, the inherent compliance structure, meaning that stiffness can be tuned from rigid to soft with different folding states, allows these robots to perform versatile functions, ranging from soft interactions to robust manipulation and a high-DOF system. In addition, the potential to simplify the fabrication and assembly processes, together with its integration into a wide range of actuation systems, further broadens its capabilities. However, these mechanisms increase the complexity in theoretical analysis and modelling, as well as posing a challenge in control algorithms when the robot’s DOF and reconfigurations are significantly increased. By leveraging the principles of folding and integrating actuation and design strategies, these robots can adapt their shapes, stiffness, and functionality to meet the demands of diverse tasks and environments, offering significant advantages over traditional rigid robots. Full article

Addressing key challenges in unstructured environments, including local optimum traps, limited real-time interaction, and convergence difficulties, this research pioneers a hybrid reinforcement learning approach that combines simulated annealing (SA) with proximal policy optimization (PPO) for robotic arm trajectory planning. The framework enables the accurate, collision-free grasping of randomly appearing objects in dynamic obstacles through three key innovations: a probabilistically enhanced simulation environment with a 20% obstacle generation rate; an optimized state-action space featuring 12-dimensional environment coding and 6-DoF joint control; and an SA-PPO algorithm that dynamically adjusts the learning rate to balance exploration and convergence. Experimental results show a 6.52% increase in success rate (98% vs. 92%) and a 7.14% reduction in steps per set compared to the baseline PPO. A real deployment on the AUBO-i5 robotic arm enables real machine grasping, validating a robust transfer from simulation to reality. This work establishes a new paradigm for adaptive robot manipulation in industrial scenarios requiring a real-time response to environmental uncertainty. Full article

A multi-objective optimal trajectory planning method is proposed for manipulators in this paper to enhance motion efficiency and to reduce component wear while ensuring motion smoothness. The trajectory is initially interpolated in the joint space by using quintic non-uniform B-splines with virtual points, achieving the C⁴ continuity of joint motion and satisfying dynamic, kinematic, geometric, synchronization, and boundary constraints. The interpolation reformulates the trajectory planning problem into an optimization problem, where the time intervals between desired adjacent waypoints serve as variables. Travelling time and the integral of the squared jerk along the entire trajectories comprise the multi-objective functions. A constrained multi-objective dream optimization algorithm is designed to solve the time–jerk optimal trajectory planning problem and generate Pareto solutions for optimized trajectories. Simulations conducted on 6-DOF manipulators validate the effectiveness and superiority of the proposed method in comparison with existing typical trajectory planning methods. Full article

Background: Laparoscopic pancreatic surgery demands high precision and minimal invasiveness, yet conventional robotic systems often face challenges due to complex anatomical environments and uncertainties inherent in surgical procedures. Optimizing key design parameters such as the Remote Center of Motion (RCM) and robotic link lengths is critical for enhancing workspace accessibility and instrument maneuverability. Methods: An integrated optimization framework combining genetic algorithms (GA) with fuzzy logic was developed to determine the optimal RCM position and the ideal lengths of crucial robotic links in a 3-DOF parallel robotic system. The GA explored a large design space based on 6951 tracking points recorded during manual instrument manipulation, while the fuzzy logic system refined fitness evaluations by incorporating expert-defined membership functions and heuristic rules to manage uncertainties and ensure robust performance. Results: Simulation studies demonstrated that the optimized RCM position shifted from an initial [100, 0, 300] to [119.003337, −146.610801, 269.07376], yielding improved workspace coverage and enhanced instrument maneuverability. The GA further determined optimal link lengths of approximately 213.5 mm, 248.5 mm, and 48.6 mm for the primary, tertiary, and minimum secondary links, respectively, which were rounded to practical values of 215 mm, 250 mm, and 50 mm. The optimized design exhibited significant improvements in workspace reachability, precision, and operational stability, as validated by detailed 3D workspace plots and time history diagrams of the instrument tip and joint trajectories. Conclusions: The integrated GA–fuzzy optimization approach effectively enhances the design of a 3-DOF parallel robot for laparoscopic pancreatic surgery by achieving superior kinematic performance. The optimized parameters contribute to improved surgical precision and workspace accessibility, indicating strong potential for clinical application and further experimental validation. Full article

Finding realistic motions for redundant manipulators is essential for complex jobs such as home care and industrial assembly. Motion planning is complex when a task requires standing upright or moving through restricted spaces. This work provides an effective motion-planning strategy for 7-DOF manipulators that improves connections via redundancy. The analytic Cartesian-space-to-joint-space kinematic mapping models for 7-DOF redundant manipulators with diverse configurations are constructed first, and the feasible nodes are determined by sampling the Cartesian space without barriers to satisfy the task requirements. Each Cartesian-space sampling node can provide numerous feasible joint-space nodes because of the redundancy of the robot manipulators. To remove additional valid nodes from a singular position, joint configurations with the same end-effector position orientation are modified iteratively. Finally, we find the nearest nodes in the joint-space constraint manifold and build collision-free smooth pathways. The task constraint levels were varied for a 7-DOF manipulator in simulations and experiments. The proposed planner finds more viable nodes at the same end-position attitude than one-to-one projection. It does not require numerical iterations and achieves high planning efficiency and a high motion-planning success rate. Full article

This study investigates the integration of reinforcement learning (RL) with optimal control to enhance precision and energy efficiency in industrial robotic manipulation. A novel framework is proposed, combining Deep Deterministic Policy Gradient (DDPG) with a Linear Quadratic Regulator (LQR) controller, specifically applied to the ABB IRB120, a six-degree-of-freedom (6-DOF) industrial manipulator, for pick-and-place tasks in warehouse automation. The methodology employs an actor–critic RL architecture with a 27-dimensional state input and a 6-dimensional joint action output. The RL agent was trained using MATLAB’s Reinforcement Learning Toolbox and integrated with ABB’s RobotStudio simulation environment via TCP/IP communication. LQR controllers were incorporated to optimize joint-space trajectory tracking, minimizing energy consumption while ensuring precise control. The novelty of this research lies in its synergistic combination of RL and LQR control, addressing energy efficiency and precision simultaneously—an area that has seen limited exploration in industrial robotics. Experimental validation across 100 diverse scenarios confirmed the framework’s effectiveness, achieving a mean positioning accuracy of 2.14 mm (a 28% improvement over traditional methods), a 92.5% success rate in pick-and-place tasks, and a 22.7% reduction in energy consumption. The system demonstrated stable convergence after 458 episodes and maintained a mean joint angle error of 4.30°, validating its robustness and efficiency. These findings highlight the potential of RL for broader industrial applications. The demonstrated accuracy and success rate suggest its applicability to complex tasks such as electronic component assembly, multi-step manufacturing, delicate material handling, precision coordination, and quality inspection tasks like automated visual inspection, surface defect detection, and dimensional verification. Successful implementation in such contexts requires addressing challenges including task complexity, computational efficiency, and adaptability to process variability, alongside ensuring safety, reliability, and seamless system integration. This research builds upon existing advancements in warehouse automation, inverse kinematics, and energy-efficient robotics, contributing to the development of adaptive and sustainable control strategies for industrial manipulators in automated environments. Full article

Soft grippers perform various handling tasks using passive conformability. This article reviews the improvements in their capabilities of dexterous manipulations, including how they achieve dexterity and how their performance could be evaluated. This article particularly points out the correlations between potential and real dexterity, and the relationship between the space of degrees of freedom and the corresponding dexterity level. Our main contribution is the proposition of a uniform framework for the characterization of soft grippers and their performance. We first present an introduction to soft grippers and those capable of in-hand manipulation. We emphasize their hybridization by combining soft and rigid materials or using several active materials. Next, we define and discuss the manipulation tasks and how to achieve dexterity, making a distinction between stable grasping and stable in-hand manipulation. We finally discuss the means to achieve assessment and how the performance can be evaluated, and we develop a general exploitable approach for characterizing soft grippers and their dexterous performance based on their architecture, DOF space, and physical performance. Full article

Hyper-redundant manipulators based on bionic structures offer superior dexterity due to their large number of degrees of freedom (DOFs) and slim bodies. However, controlling these manipulators is challenging because of infinite inverse kinematic solutions. In this paper, we present a novel reinforcement learning-based control method for hyper-redundant manipulators, integrating path and configuration planning. First, we introduced a deep reinforcement learning-based control method for a multi-target approach, eliminating the need for complicated reward engineering. Then, we optimized the network structure and joint space target points sampling to implement precise control. Furthermore, we designed a variable-reset cycle technique for a continuous multi-target approach without resetting the manipulator, enabling it to complete end-effector trajectory tracking tasks. Finally, we verified the proposed control method in a dynamic simulation environment. The results demonstrate the effectiveness of our approach, achieving a success rate of 98.32% with a 134% improvement using the variable-reset cycle technique. Full article

This study presents the design, simulation, and comparative analysis of three advanced control strategies applied to a 3-Degree-of-Freedom (DoF) robot manipulator. The controllers investigated are a variant from the Computed Torque Control family, a Proportional–Derivative–Integral with fuzzy logic (PD-PI + fuzzy) controller, and a Model Predictive Control (MPC) scheme. The controller performance is evaluated through the tracking of predefined trajectories in the three-dimensional space. The results are analyzed through XYZ coordinate motion graphs and 3D trajectories. To quantify performance, three error indicators are employed: Residual Mean Square (RMS) with a value of 0.0720 for the Computed Torque Controller, Residual Standard Deviation (RSD), and Index of Agreement (IA). The results demonstrate that the proposed controllers achieve accurate trajectory tracking, with IA values close to unity, demonstrating a high degree of concordance between the desired and executed trajectories. Full article

This paper presents the development of a hardware-in-the-loop ground testbed featuring active gravity compensation via software-in-the-loop integration, specially designed to support research in autonomous robotic removal of space debris. The testbed is designed to replicate six degrees of freedom (6DOF) motion maneuvering to accurately simulate the dynamic behaviors of free-floating robotic manipulators and free-tumbling space debris under microgravity conditions. The testbed incorporates two industrial 6DOF robotic manipulators, a three-finger robotic gripper, and a suite of sensors, including cameras, force/torque sensors, and tactile tensors. Such a setup provides a robust platform for testing and validating technologies related to autonomous tracking, capture, and post-capture stabilization within the context of active space debris removal missions. Preliminary experimental results have demonstrated advancements in motion control, computer vision, and sensor fusion. This facility is positioned to become an essential resource for the development and validation of robotic manipulators in space, offering substantial improvements to the effectiveness and reliability of autonomous capture operations in space missions. Full article

The seven-degree-of-freedom space manipulator, characterized by its redundant and aspheric wrist structure, is extensively used in space missions due to its exceptional dexterity and multi-joint capabilities. However, the non-spherical wrist structure presents challenges in solving inverse kinematics, as it cannot decouple joints using the Pieper criterion, unlike spherical wrist structures. To address this issue, this paper presents a closed-form analytical method for solving the inverse kinematics of seven-degree-of-freedom aspheric wrist space manipulators. The method begins by identifying the redundant joint through comparing the volumes of the workspace with different joints fixed. The redundant joint angle is then treated as a parametric joint angle, enabling the derivation of closed-form expressions for the non-parametric joint angles using screw theory. The optimal solution branch is identified through a comparative analysis of various self-motion manifold branches. Additionally, a hybrid approach, combining analytical and numerical methods, is proposed to optimize the parametric joint angle for a trajectory tracking task. Simulation results confirm the effectiveness of the proposed method. Full article

This paper presents the development of the hardware and software architecture of a sixdegrees of freedom (DOF) parallel robot (PARA-SILSROB) by illustrating all the stages undertaken to achieve the experimental model of the robot. Based on the experimental model, the control architecture is also presented, which is primarily based on a master–slave control system through which the surgeon controls the robot using the master console composed of commercial peripheral components (two 3D Space Mouse devices, computer, and keyboard) integrated with the solution developed in this study and presented in this paper. The robot was developed also according to the surgical protocol and surgeon’s requirements, and for the functionality testing of the mechanical structure, two experimental stands were used. The first stand presented several surgical steps, such as manipulation, resection, and suture of experimental tissues (simulating real-life robot-assisted surgical maneuvers) using commercial instruments. The second stand presented a simulation of an esophagectomy for esophageal cancer and digestive reconstruction through a right intercostal approach. For this testing phase, the organs were created using 3D reconstruction, and their simplified models were 3D printed using PolyJet technology. Furthermore, the input trajectory generated using the master console was compared with the robot actuator’s movements and the obtained results were used for validation of the proposed robot control system. Full article