3D Manipulation Research Articles

Knowledge Database-Based Multiobjective Trajectory Planning of 7-DOF Manipulator With Rapid and Continuous Response to Uncertain Fast-Flying Objects

The problems of a 7-degree of freedom (DOF) manipulator with rapid and continuous response to uncertain fast-flying objects are addressed: 1) how to effectively solve trajectory planning of the 7-DOF manipulator with multiple criteria; and 2) how to make the 7-DOF manipulator realize the rapid and continuous response to uncertain fast-flying objects. In the proposed approach, based on the trajectory parameterization of the 7-DOF manipulator, a multiobjective teaching-learning-based optimization (MOTLBO) algorithm is adopted to find a close representation of the Pareto optimal set rather than a single solution. As such, an optimal solution can be chosen as digital knowledge information. A new methodology based on a knowledge base representing and learning the operation environment, that is, skill digitization, is presented, which enables the 7-DOF manipulator to realize the rapid and continuous response skill. Simulation and practical testing results of a ping-pong robot validate the feasibility and effectiveness of the proposed approach, in which the online trajectory generation spends only around 1 ms.

Asymptotically optimal inspection planning via efficient near-optimal search on sampled roadmaps

Inspection planning, the task of planning motions for a robot that enable it to inspect a set of points of interest, has applications in domains such as industrial, field, and medical robotics. Inspection planning can be computationally challenging, as the search space over motion plans grows exponentially with the number of points of interest to inspect. We propose a novel method, Incremental Random Inspection-roadmap Search (IRIS), that computes inspection plans whose length and set of successfully inspected points asymptotically converge to those of an optimal inspection plan. IRIS incrementally densifies a motion-planning roadmap using a sampling-based algorithm and performs efficient near-optimal graph search over the resulting roadmap as it is generated. We prove the resulting algorithm is asymptotically optimal under very general assumptions about the robot and the environment. We demonstrate IRIS’s efficacy on a simulated inspection task with a planar five DOF manipulator, on a simulated bridge inspection task with an Unmanned Aerial Vehicle (UAV), and on a medical endoscopic inspection task for a continuum parallel surgical robot in cluttered human anatomy. In all these systems IRIS computes higher-quality inspection plans orders of magnitudes faster than a prior state-of-the-art method.

Merging Global and Local Planners: Real-Time Replanning Algorithm of Redundant Robots Within a Task-Priority Framework

Task-priority inverse kinematics is a popular motion control algorithm which efficiently handles redundancy in robot manipulators. It has been recently extended in order to handle also set-based control objectives or inequality constraints. As any local motion planner it is prone to the occurrence of local minima. This work further extends set-based inverse kinematics by adding a motion planner in order to avoid such occurrence. Motion planners are usually computationally heavy especially in their eventual implementation with a task-priority architecture. To reduce this issue, the planner is implemented as a sampling-based algorithm which works in the reduced-dimensionality of the robot workspace applying Cartesian constraints only. The output trajectory is then checked against the inverse kinematics algorithm exploiting the redundancy and verifying the fulfillment of the joint-based task constraints. During the motion, inverse kinematics is then used also in real-time to ensure a reactive behavior to address, e.g., mismatch between the a-priori information and real-time perception acquisition. Also, the motion planner runs in background to adapt to changes in the environment or to accommodate incremental mapping. Comparison with alternative approaches are investigated and discussed. The most promising method is validated first in hundreds of numerical simulations to provide a solid statistical analysis and then experimentally with a Kinova Jaco2 7 DOFs manipulator equipped with an RGB-D sensor. Note to Practitioners—In this work we propose a motion planning algorithm that allows to effectively deploy robot manipulators in partially unstructured environments. It is structured in two layers: a Cartesian space global planner guarantees the feasibility of the trajectory with respect to potential obstacles in the environment, while a reactive local planner checks the feasibility at joint level. This architecture allows to define all the safety constraints both at Cartesian and joint space needed to operate in unstructured environments, while keeping the computation time lower with respect to standard approaches that define all the constraints in an offline global planner. The reduced computation time allows to effectively perform real-time replanning operations when needed, making the robotic system capable of adapting to a dynamic environment and suitable for sharing its workspace with human operators.

Research on trajectory planning of mobile manipulator based on improved gradient projection algorithm

This paper takes a 10 DOF mobile manipulator as the research object. Aiming at the problem of joint acceleration overrun in the trajectory planning of a 10 DOF Manipulator, firstly, the scale factor is obtained by the extremum method. Compared with the commonly used extremum method, the optimization degree and the self motion limit factor are introduced, and a new algorithm considering joint acceleration constraint is deduced and proposed. By comparing the simulation results of the inverse solution algorithm, the effectiveness of the new algorithm is verified. Finally, it is applied to a mobile manipulator with 10 degrees of freedom for example motion test. The test results meet the design requirements. The results will be actually applied in engineering projects.

Structure Design and Path Planning for Cleaning Manipulator

Robots can replace people to complete many complex, and repetitive tasks, and can also replace people to work in dangerous and toxic and harmful environments. At present, most of the car cleaning methods are manual cleaning, which is time-consuming and laborious. It is also easy to damage the car surface and dirt will endanger the health of staff. The car cleaning robot can replace people to clean. The structure and cleaning path of the cleaning manipulator have an important impact on the cleaning efficiency. The paper designs the structure of the cleaning manipulator and plans its cleaning path. Firstly, the functions of the studied manipulator are analyzed through investigation; Determine the overall structure of the cleaning manipulator according to the required functions to ensure that the car can be cleaned in all directions; Secondly, carry out detailed structural design for the 6-DOF manipulator, complete the clamping of rags and other cleaning tools through the connecting rod structure, and assemble rollers on both sides of the connecting rod to ensure that the robot can effectively complete the cleaning of the car, and carry out digital 3D modeling; Finally, the cleaning path of the manipulator is planned in the MATLAB environment to ensure the smooth motion of the robot. The digital motion simulation of the manipulator is completed by solving the forward.

Uncalibrated Adaptive Visual Servoing of Robotic Manipulators with Uncertainties in Kinematics and Dynamics

In the study, we propose a novel adaptive visual servoing control scheme for robotic manipulators with kinematic and dynamic uncertainties, where the camera used is uncalibrated, which implies that its intrinsic and extrinsic parameters are unavailable for measurement. For our scheme, a depth-independent composite Jacobian matrix is constructed to make visual parameters and robotic physical parameters appear linearly in a parametrized uniform form so that an adaptive algorithm can be developed to estimate their values. With the raised adaptive algorithm, the potential singularity of the Jacobian matrix can be well circumvented by updating estimated parameters in an appropriate tiny range of actual values. With our scheme, the asymptotic convergence of the image tracking error to zero is established successfully, in addition to the signal boundedness of the closed-loop system. The effectiveness of the proposed scheme is confirmed by simulation results based on a 6-DOF PUMA manipulator.

Dexterous workspace optimization for a six degree-of-freedom parallel manipulator based on surrogate-assisted constrained differential evolution

Parallel manipulators are increasingly applied because of their high stiffness, load capacity, and motion precision. However, the workspace of a parallel manipulator is relatively small compared to that of a serial manipulator. Therefore, appropriate structural designs of parallel manipulators for maximizing the workspace are essential. This paper studies the dexterous workspace maximization problem of a six-degree-of-freedom (DOF) parallel manipulator. First, a constrained optimization model is formulated, in which, the objective is to maximize the dexterous workspace, and the constraints include several kinematic and dynamic indicators, and some general structural limitations. To address the highly constrained, computationally-expensive problem, a surrogate-assisted two-phase constrained differential evolution method is proposed. Its effectiveness and efficiency are verified by the superior performance over two surrogate-free evolutionary methods: differential evolution (DE) and genetic algorithm (GA), and two surrogate-assisted state-of-the-art evolutionary methods: modified constrained expected improvement (CEI) and the lower confidence bounding approach (PCLCB), on ten benchmark functions and the established real-word 6-DOF manipulator design optimization problem.

Spring-balanced 3-DoF serial planar manipulators for constant forces in arbitrary directions

AbstractWith the use of springs, a method to balance the constant forces in arbitrary directions on a planar serial manipulator is developed in this study. Gravity balancing has been discussed a lot in the past. However, manipulators usually bear forces from various directions rather than only a fixed one as gravity. For instance, an industrial manipulator would bear forces from everywhere during the working process. Therefore, a method to balance these forces in arbitrary directions with springs is proposed. Based on the representation of energy, spring energy is the function of springs’ attachment points. Two spring systems with different attachment angles are needed to balance respectively forces in arbitrary directions and gravity. The spring installations of the above systems on 3-DoF manipulators are proposed. Finally, a resistive force-balanced manipulator with/without gravity balance in the grinding process is shown. In sum, this paper for the first time develops the balancing method for forces in arbitrary directions, expanding the spring balance theory to a broader application.

A Microrobot With an Attached Microforce Sensor for Transurethral Access to the Bladder Interior Wall

AbstractWe present the conceptual design and limited functionality prototype and characterization of a system for application in transurethral palpation of any targeted area of the bladder interior wall tissue consisting of a robotic manipulator and a microforce sensor attached at its tip all less than 3.5 mm in diameter. A hyper-redundant ten-joint six degrees-of-freedom (6DOF) manipulator (5DOF rigid and five-joint continuum segments) is presented along with the forward and inverse kinematics analyses based on a Jacobian formulation to prevent configuration singularities. Simulated motion studies demonstrate the ability of the proposed manipulator to attain a desired pose (normal to the tissue) with any area in the bladder including the difficult to reach trigone area. A strain gauge-based microforce sensor is designed using finite element analysis (safety factor > 3), prototyped using additive manufacturing, and characterized. The characterized sensor was used to acquire in vivo measurements to evaluate human palm tissue viscoelastic properties. A single module of the continuum segment is designed and prototyped using additive manufacturing, and used to characterize its tension-bend angle behavior. Finite element analysis is used to improve structurally weak regions of the vertebra. A three-joint four-vertebrae prototype was successfully actuated to reach a bend state using tendons. The developed robot and sensor prototypes demonstrate capabilities of the proposed concept which could be a possible solution to quantitatively evaluate localized biomechanical properties of the bladder tissue to improve treatment and provide better patient care.

Adaptive Notch Filter in a Two-Link Flexible Manipulator for the Compensation of Vibration and Gravity-Induced Distortion

This paper presents a 2-link, 2-DOF flexible manipulator control using an inverse feedforward controller and an adaptive notch filter with a direct strain feedback controller. In the flexible manipulator, transient and residue vibrations inhibit the full potential of the manipulator. Vibrations caused by abrupt changes in the direction of the links are referred to as transient vibrations, whereas residual vibrations occur when the arm takes too long to settle after engaging in the intended task. The feedforward adaptive notch filter will reduce transient vibration caused by the manipulator arm beginning and halting suddenly, while the strain feedback will assure the quick decay of leftover vibrations. Maple, Maplesim, and MATLAB tools were used to model the manipulator and create the inverse controller and adaptive notch filter. The experiments took place in the dSPACE control desk environment. The experimental results of the spectral power of strain resulting from the two strategies are compared. From the results, the adaptive notch filter control had over an 80% improvement in the reduction in resonant frequencies that contribute to vibration. The results confirmed the feasibility of the approach, characterized by very minimal transient vibrations and a quick settling of the end effector.

Nonlinear Controller with Dynamic Compensation for 6-DOF Manipulator in Practice

This paper presents a nonlinear controller with dynamic compensation for six degrees of freedom (6-DOF) manipulator Denso VS-6556 in practice. The manipulator is a complex nonlinear system with some limitations when applying a linear controller to control it. The nonlinear controller combines proportional derivative, and dynamic compensation is established to deal with this problem. The PD control is a linear controller, and dynamic compensation can cancel the nonlinear parts in the system. Therefore, the tracking problem is expected to result in a good performance. The stability and resilience of the entire system are examined using a Lyapunov technique. The test bench is built to ensure the conditions for experimentation; it includes card PCI QUAD04s, NI PCI-6713, industrial computer 610H Advantage, and MATLAB/Simulink in practice. Finally, the suggested control is subjected to the 6-DOF manipulator Denso 6556 and compared with the PD control to verify the effectiveness of the controller.

Design, Kinematics and Workspace Analysis of a Novel 4-DOF Kinematically Redundant Planar Parallel Grasping Manipulator

This article presents a model of a novel 4-DOF kinematically redundant planar parallel grasping manipulator. As distinct from the traditional 4-DOF manipulator, the proposed design includes an extensible platform, which provides kinematic redundancy. This constructive feature is used for grasping. The article discusses the inverse and forward kinematics of the proposed manipulator. The inverse kinematics algorithm provides the analytical relations between the platform coordinates and the driven (controlled) coordinates. The forward kinematics algorithm allows defining different assembly modes of the manipulator. Both algorithms are demonstrated using numerical examples. The article discusses different designs of the manipulator in which its links are placed in one, two, or three layers. Based on these designs, we performed their workspace analyses.

Design of PID, FLC and Sliding Mode Controller for 2-DOF Robotic Manipulator: A Comparative Study

Controlling the manipulators in a precise manner is a challenging task. To overcome this difficulty around the world, many researchers have developed various control algorithms but are not providing optimal results. To obtain the optimal results in the current research the authors designed a proportional, integral, and derivative (PID) controller, fuzzy logic controller (FLC), and sliding mode controller (SMC) for a 2-DOF manipulator. The concept of forward and inverse kinematics was initially solved after assigning the D-H parameters for each joint. The purpose of forward or direct kinematics is to obtain the position and orientation of the end effector. Further, the concept of inverse kinematics is used to estimate the joint angles. Later on, the Lagrange-Euler formulation was used to calculate the dynamics of the 2-DOF manipulator, which is required to estimate the torque required for each joint of the robotics arm. The main goal of this research problem is to optimize the angular error between the two successive events. Finally, the developed algorithm is compared with the existing algorithms such as PID and Fuzzy logic controller.

Global Model Learning for Large Deformation Control of Elastic Deformable Linear Objects: An Efficient and Adaptive Approach

Robotic manipulation of deformable linear objects (DLOs) has broad application prospects in many fields. However, a key issue is to obtain the exact deformation models (i.e., how robot motion affects DLO deformation), which are hard to theoretically calculate and vary among different DLOs. Thus, shape control of DLOs is challenging, especially for large deformation control which requires global and more accurate models. In this paper, we propose a coupled offline and online data-driven method for efficiently learning a global deformation model, allowing for both accurate modeling through offline learning and further updating for new DLOs via online adaptation. Specifically, the model approximated by a neural network is first trained offline on random data, then seamlessly migrated to the online phase, and further updated online during actual manipulation. Several strategies are introduced to improve the model's efficiency and generalization ability. We propose a convex-optimization-based controller, and analyze the system's stability using the Lyapunov method. Detailed simulations and real-world experiments demonstrate that our method can efficiently and precisely estimate the deformation model, and achieve large deformation control of untrained DLOs in 2D and 3D dual-arm manipulation tasks better than the existing methods. It accomplishes all 24 tasks with different desired shapes on different DLOs in the real world, using only simulation data for the offline learning.

An inverse dynamics based fuzzy adaptive state-feedback controller for a nonlinear 3DOF manipulator

ABSTRACT This work presents a novel fuzzy adaptive controller for position regulation of the joint variables for a nonlinear 3DOF Revolute-Prismatic-Prismatic (RPP) manipulator. To reach this goal, the forward kinematic equations of the considered series robot are obtained, and the workspace of the end-effector is investigated. Next, the dynamical equations of the robot are found, and a state-feedback controller is designed for stabilizing via the inverse dynamics approach. Through sliding mode surfaces related to the system states, the control parameters are adaptively calculated to enhance the performance of the introduced strategy. Finally, in order to increase the efficiency of the proposed approach, the gains of the designed controller are revealed by employing the fuzzy-logic-based systems. Simulation results for joint variables over time obtained by the suggested technique and recently published methods show the superiority of the fuzzy adaptive control scheme for stabilization of the robot manipulator.

Combinatorial Drug Screening Based on Massive 3D Tumor Cultures Using Micropatterned Array Chips.

The establishment and application of a generalizable three-dimensional (3D) tumor device for high-throughput screening plays an important role in drug discovery and cancer therapeutics. In this study, we introduce a facile microplatform for considerable 3D tumor generation and combinatorial drug screening evaluation. High fidelity of chip fabrication was achieved depending on the simple and well-improved microcontact printing. We demonstrated the high stability and repeatability of the established tumor-on-a-chip system for controllable and massive production of 3D tumors with high size uniformity. Importantly, we accomplished the screening-like chemotherapy investigation involving individual and combinatorial drugs and validated the high accessibility and applicability of the system in 3D tumor-based manipulation and analysis on a large scale. This achievement in tumor-on-a-chip has potential applications in plenty of biomedical fields such as tumor biology, pharmacology, and tissue microengineering. It offers an insight into the development of the popularized microplatform with easy-to-fabricate and easy-to-operate properties for cancer exploration and therapy.

Adaptive Fractional-Order Fast-Terminal-Type Sliding Mode Control for Underwater Vehicle-Manipulator Systems

Abstract This article proposes an adaptive fractional-order fast-terminal-type sliding mode control method to solve the trajectory tracking problem of the underwater vehicle-manipulator system in the presence of large dynamic uncertainties and strong external disturbances. For improving the control continuity and accuracy of the conventional sliding mode control method, a modified fractional-order sliding mode control scheme with a novel fractional-order sliding manifold and a fast-terminal-type reaching law is proposed. In addition, an auto-tuning law of switching gains is presented and combined with the fractional-order sliding mode control scheme to overcome the problem that the fixed switching gain may lead to obvious tracking performance degradation under complicated lumped disturbances. Finally, the superiorities of the proposed adaptive fractional-order fast-terminal-type sliding mode control method are verified through a series of comparative simulations of a 6-degrees-of-freedom (DOF) UVMS carrying a 6DOF manipulator.

Obstacle avoidance method for fixed trajectory of a seven-degree-of-freedom manipulator

AbstractThis paper, based on the idea of redundancy angle discretisation, proposes an obstacle avoidance method for the fixed tip pose trajectory of a seven degrees-of-freedom (7-DOF) modular manipulator. First, for the case in which a specific redundancy angle is given, the analytical solutions of the redundant manipulator left 6-DOF subchain are found. Then, through the discretisation of the redundancy angle, the concept of the self-motion space of the tip pose is proposed and is extended to the concept of the self-motion space of the trajectory. Based on this discrete space, a path-planning algorithm is proposed to help select the appropriate redundancy angles to obtain the collision-free solution set of the fixed Cartesian trajectory. However, due to the large fluctuation of the obtained path, a path optimisation method based on the path cost is proposed to smooth the path, and the continuous and collision-free solution set of the manipulator tip’s trajectory is obtained. The method proposed in this paper provides a new thought for the problem of collision-free solution set planning for the Cartesian trajectory of a 7-DOF manipulator and it has great application potential in working environments with high accuracy requirements for the trajectory.

A novel trajectory tracking control approach for uncertain 6-DOF manipulators based on fuzzy sliding mode of radial basis function neural network

Due to the influence of uncertain external disturbance and model error, the trajectory tracking of manipulators has some problems such as inaccuracy, low speed and severe chattering. To solve the problems mentioned, a sliding mode control method combined fuzzy controller and Radial Basis Function Neural Network (RBFNN) was proposed. The RBFNN was used to approximate the equivalent system model of manipulators, and the weights of their hidden layers were updated adaptively online. The robust item with the form of sliding mode had been set to compensate the uncertain external disturbances or the approximation errors of the RBFNN. A fuzzy controller was used to adjust the switching gain online adaptively and to solve the chattering of the sliding mode control. The stability and finite time convergence of the closed-loop system had been analyzed by using Lyapunov method. Finally, simulation experiments about trajectory tracking were implemented by MATLAB/SIMULINK, which shows that the proposed method can improve the position-tracking accuracy by more than 40%, the speed-tracking accuracy by more than 70%, and the cumulative-error accuracy by more than 40% compared with the comparison method. Moreover, there is no chattering. The proposed method has been proved to be efficient.

A magnetic particle imaging in anisotropic magnetic manipulation platform

Magnetic particle imaging (MPI) has been demonstrated as the most promising imaging technology for monitoring distribution of magnetic nanoparticles (MNPs) at high sensitivity, high resolution, and fast imaging speed. Up to now, most published researches use the symmetric setups with multiple orthogonal pairs of coils. However, this poses a size and controllable degrees-of-freedom limitation for 3D manipulation tasks. The difficulties of field-free point (FFP) estimation and interferences of the core materials limit the MPI integration into other types of magnetic configuration. Thus, this paper presents a feasibility study of the integration of the MPI function in the anisotropic magnetic platform. A FFP generation method in anisotropic structures with various magnetic sources is derived and numerically investigated. The tracking performances are then demonstrated in both in-vitro and in-vivo experiments using one of the selected anisotropic structures. To date, the function of the proposed system is limited to tracking the particle. The experimental results on tracking the MNPs show a tracking accuracy of around 1 mm. Our proposed methodology allows the current magnetic actuation systems to upgrade the functionalities of simultaneous manipulation and localization.