Explore the vast range of titles available.

The ability to deal with articulated objects is very important for robots assisting humans. In this work, a framework to robustly and adaptively operate common doors, using an autonomous mobile manipulator, is proposed. To push forward the state of the art in robustness and speed performance, we devise a novel algorithm that fuses a convolutional neural network with efficient point cloud processing. This advancement enables real-time grasping pose estimation for multiple handles from RGB-D images, providing a speed up improvement for assistive human-centered applications. In addition, we propose a versatile Bayesian framework that endows the robot with the ability to infer the door kinematic model from observations of its motion and learn from previous experiences or human demonstrations. Combining these algorithms with a Task Space Region motion planner, we achieve an efficient door operation regardless of the kinematic model. We validate our framework with real-world experiments using the Toyota human support robot.

This paper introduces two enhanced control approaches to improve the performance of parallel manipulators, addressing their inherent nonlinear dynamics and complex structure. The first approach results in a hybrid control system in joint space, integrating acceleration-based control, sliding mode, and disturbance observer techniques. The control system is designed to correct tracking errors and compensate for generalized disturbances, thus improving accuracy in tracking reference positions. The second approach merges the joint-space and task-space formulations, implementing proportional-derivative controllers in task space to manage the end-effector positions while maintaining safe operational configurations. The stability of the proposed controllers is demonstrated through Lyapunov analysis, while their performance is validated through comprehensive simulations and real-time experiments.

This paper presents a novel object transportation method using deep reinforcement learning (DRL) and the task space decomposition (TSD) method. Most previous studies on DRL-based object transportation worked well only in the specific environment where a robot learned how to transport an object. Another drawback was that DRL only converged in relatively small environments. This is because the existing DRL-based object transportation methods are highly dependent on learning conditions and training environments; they cannot be applied to large and complicated environments. Therefore, we propose a new DRL-based object transportation that decomposes a difficult task space to be transported into simple multiple sub-task spaces using the TSD method. First, a robot sufficiently learned how to transport an object in a standard learning environment (SLE) that has small and symmetric structures. Then, a whole-task space was decomposed into several sub-task spaces by considering the size of the SLE, and we created sub-goals for each sub-task space. Finally, the robot transported an object by sequentially occupying the sub-goals. The proposed method can be extended to a large and complicated new environment as well as the training environment without additional learning or re-learning. Simulations in different environments are presented to verify the proposed method, such as a long corridor, polygons, and a maze.

The objective of this research is to present dynamic modeling and natural frequency analyses of a 3- P RR planar parallel kinematic mechanism (PKM) for CNC machining applications. The orientation of the moving platform is considered a redundant degree of freedom in task space, and its optimal value is obtained during movement to avoid singular configurations, thereby improving accuracy in milling operations. The inverse dynamic problem is also solved and the actuator forces in the presence of milling forces are obtained in both arbitrary and optimal orientations of the moving platform. Moreover, vibration equations of the PKM are determined by considering the distal links as Euler–Bernoulli beams with the prismatic joints modeled as linear springs. Finally, the natural frequencies in different configurations of the moving platform are calculated. The presented formulation can be utilized for the motion planning and control of PKMs, meeting accuracy requirements in applications such as milling. Accordingly, this study makes three key contributions: (1) It leverages task space redundancy to avoid singularities and reduce actuator loads; (2) it presents an analytical method for evaluating vibrations and natural frequencies in PKMs with flexible joints and links; and (3) it improves milling accuracy by tuning natural frequencies through task space redundancy, thereby minimizing vibrations induced by external loads.

This study presents a mathematical and kinematic modeling framework for analyzing trajectory behavior in a virtual reality (VR) welding simulator. Twenty novice participants performed repeated welding trials across three sessions, with torch trajectories recorded at 50 Hz in the task space. The proposed framework combines trial-level performance descriptors with derivative-based dynamic features, including spectral arc length (SPARC), log-normalized jerk (LNJ), and the number of velocity peaks (NVP), to characterize movement smoothness, intermittency, and longitudinal trajectory organization in a computer-simulated manual welding task. The results showed that spatial welding error decreased most clearly during the earliest stage of practice, with mean absolute lateral error declining from approximately 2.8 mm in the first trial to approximately 1.7 mm by the third trial. This early improvement was then broadly preserved across subsequent sessions. In contrast, smoothness- and fragmentation-related metrics exhibited more variable temporal patterns, indicating that improvements in task-space accuracy were not necessarily accompanied by uniform reorganization of movement dynamics. Associations between spatial error and kinematic features remained limited, suggesting that geometric task accuracy and dynamic trajectory organization represent complementary aspects of simulated manual performance. Overall, the findings show that high-frequency trajectory analysis in VR provides a useful basis for the mathematical modeling of dynamic behavior in simulated welding systems and supports the use of computer simulation for process-level investigation of manual task execution.

Autonomous Mobile Robots (AMRs) have become extremely popular in the manufacturing domain, especially for processes involving large factory floors where these robots are used for transporting materials from one location to another. In an environment where there are multiple prioritized tasks to be completed by a school of AMRs, the overall planning problem can be broken down into three sequential steps: task allocation for the school of AMRs, task scheduling for each AMR, and trajectory planning for each individual AMR. This paper focuses on the trajectory generation procedure for each AMR. Unlike traditional approaches that only consider the location an AMR has to travel to during path planning, here, energy efficiency of the AMR is also considered. We present the physics-based model of the AMR as well as an optimal control formulation for energy-conscientious trajectory generation for the AMR. Methods to numerically solve this problem are discussed, and results are presented for each proposed algorithm on approximately 100 test cases, comparing both performance and computational efficiency. The results show that the presented energy-conscientious methods perform better in terms of energy usage (5-10%) compared to commonly-used shortest path techniques while maintaining similar computational and operational efficiency.

PurposeThe purpose of this paper is to solve the tracking problem for free-floating space manipulators (FFSMs) in task space with parameter uncertainties and external disturbance.Design/methodology/approachIn this paper, the novel cerebellar model articulation controller (CMAC) is designed with the feedback controller. More precisely, the parameter uncertainties in the FFSM are considered for achieving the robustness.FindingsBy using the dynamically equivalent model, the CMAC can be designed and trained with the desired performance, such that the prescribed trajectory can be followed accordingly. The simulation results are presented for illustrating the validity of the derived results.Originality/valueBased on the designed CMAC, the tracking error would be approaching zero by choosing appropriate quantization level in CMAC and the corresponding learning rules can be tuned online.

This paper considers the problem of the accurate task space finite-time control susceptible to both undesirable disturbance forces exerted on the end-effector and unknown friction forces coming from joints directly driven by the actuators as well as unstructured forces resulting from the kinematic singularities appearing on the mechanism trajectory. We obtain a class of estimated extended transposed Jacobian controllers which seem to successfully counteract the external disturbance forces on the basis of a suitably defined task-space non-singular terminal sliding manifold (TSM) and the Lyapunov stability theory. Moreover, in order to overcome (or to minimise) the undesirable chattering effects, the proposed robust control law involves the second-order sliding technique. The numerical simulations (closely related to an experiment) ran for a mobile manipulator consisting of a non-holononic platform of (2;0) type and a holonomic manipulator of two revolute kinematic pairs show the performance of the proposed controllers and make a comparison with other well-known control schemes.

Extensive research efforts have been made to address the motion control of rigid-link electrically-driven (RLED) robots in literature. However, most existing results were designed in joint space and need to be converted to task space as more and more control tasks are defined in their operational space. In this work, the direct task-space regulation of RLED robots with uncertain kinematics is studied by using neural networks (NN) technique. Radial basis function (RBF) neural networks are used to estimate complicated and calibration heavy robot kinematics and dynamics. The NN weights are updated on-line through two adaptation laws without the necessity of off-line training. Compared with most existing NN-based robot control results, the novelty of the proposed method lies in that asymptotic stability of the overall system can be achieved instead of just uniformly ultimately bounded (UUB) stability. Moreover, the proposed control method can tolerate not only the actuator dynamics uncertainty but also the uncertainty in robot kinematics by adopting an adaptive Jacobian matrix. The asymptotic stability of the overall system is proven rigorously through Lyapunov analysis. Numerical studies have been carried out to verify efficiency of the proposed method.

This paper addresses the kinematically optimal control problem of the mobile manipulators. Dynamic equations of the mobile manipulator are assumed to be uncertain. Moreover, globally unbounded disturbances are allowed to act on the mobile manipulator when tracking the trajectory by the end-effector. A computationally simple class of the Jacobian transpose control algorithms is proposed for the end-effector trajectory tracking. Such controllers apply a new non-singular Terminal Sliding Mode (TSM) manifold defined by a non-linear integral equality of the second order with respect to the task space tracking error. Based on the Lyapunov stability theory, the proposed Jacobian transpose control schemes are proved to be finite-time stable provided that some well-founded assumptions are fulfilled during the mobile manipulator movement. The performance of the proposed control strategies is illustrated through computer simulations for a mobile manipulator that attains trajectory tracking by the end-effector in a two-dimensional task space and simultaneously minimises some objective function.