Manipulator Trajectory Research Articles

Joint Control Simulation of A 6-DOF Upper Limb Rehabilitation Manipulator

In order to realize the accurate control of each joint trajectory of the upper limb rehabilitation manipulator, a simple model of the six degree of freedom upper limb rehabilitation manipulator was established by using SolidWorks. The correctness of the kinematics equation was verified by using MATLAB robot toolbox, and the model was imported into ADAMS software for dynamic analysis to verify the rationality of the mechanism; In view of the poor tracking effect of some joints in the dynamic simulation results, the fuzzy PID control algorithm is applied to the joint control module of MATLAB and Adams. The simulation results show that the fuzzy PID control can effectively improve the control performance of joint trajectory, which proves the controllability of the structure design, and provides the basis for further research.

Sliding Mode with Adaptive Control of Robot Manipulator Trajectory Tracking using Neural Network Approximation

This paper briefly discusses about the Robust Controller based on Adaptive Sliding Mode Technique with RBF Neural Network (ASMCNN) for Robotic Manipulator tracking control in presence of uncertainities and disturbances. The aim is to design an effective trajectory tracking controller without any modelling information. The ASMCNN is designed to have robust trajectory tracking of Robot Manipulator, which combines Neural Network Estimation with Adaptive Sliding Mode Control. The RBF model is utilised to construct a Lyapunov function-based adaptive control approach. Simulation of the tracking control of a 2dof Robotic Manipulator in the presence of unpredictability and external disruption demonstrates the usefulness of the planned ASMCNN.

Polynomial-based smooth trajectory planning for fruit-picking robot manipulator

Fruit-picking robot is an important form of intelligent agricultural machinery and has great impact in smart agriculture. Motion planning for its multi-Degree of Freedoms (DOFs) manipulator is significant to picking efficiency and fruit quality. However, it is difficult to plan appropriate trajectory for such a complex system. The purpose of this article is to generate smooth trajectories for fruit-picking robot manipulators using shortcuts that are constrained in velocity, acceleration and jerk. The proposed algorithm smooths the robot motion as a post process to trajectory planning. Given an input jerky path, the algorithm will first convert it into a fully synchronized trajectory, and then attempt to reduce the execution time of this trajectory as much as possible while retaining the kinematic motion constraints. The idea of the proposed method is to build shorter and collision-free shortcuts that replace the intermediate motion between two randomly-picked points on the trajectory. Experiments carried on a mobile fruit-picking robot demonstrate that this technique is able to generate smooth and collision-free trajectories for robot manipulators.

Measurement of the acoustic streaming pattern in a standing surface acoustic wave field

The application of standing surface acoustic waves (sSAW) has enabled the development of many flexible and easily scalable concepts for the fractionation of particle solutions in the field of microfluidic lab-ona-chip devices. In this context, the acoustic radiation force (ARF) is often employed for the targeted manipulation of particle trajectories, whereas acoustically induced flows complicate efficient fractionation in many systems [Sehgal and Kirby (2017)]. Therefore, a characterization of the superimposed fluid motion is essential for the design of such devices. The present work focuses on a structural analysis of the acousticallyexcited flow, both in the center and in the outer regions of the standing wave field. For this, experimental flow measurements were conducted using astigmatism particle tracking velocimetry (APTV) [Cierpka et al. (2010)]. Through multiple approaches, we address the specific challenges for reliable velocity measurements in sSAW due to limited optical access, the influence of the ARF on particle motion, and regions of particle depletion caused by multiple pressure nodes along the channel width and height. Variations in frequency, channel geometry, and electrical power allow for conclusions to be drawn on the formation of a complex, three-dimensional vortex structure at the beginning and end of the sSAW.

Robust adaptive dual layer sliding mode controller: Methodology and application of uncertain robot manipulator

This paper presents a novel robust adaptive dual layer sliding mode control (ADLSMC) for the problem of high accuracy tracking trajectory of robot manipulator in the presence of uncertainties and external disturbances. This new control scheme has a dual layer structure. The first layer drives the robot manipulator system reaches the global nonlinear sliding surface in finite time, and the second layer tackles the values of the two control gains overestimation problem. Moreover, compared with the traditional super-twisting with time delay estimation algorithm, the proposed controller can realize not only set-point tracking but also dynamic tracking, which is very widely used in practice. The stability of the closed–loop system and the finite time convergence are analyzed using Lyapunov techniques. The effectiveness of the proposed method is demonstrated by simulations and experimental studies.

Electrostatic footpads enable agile insect-scale soft robots with trajectory control.

Agility and trajectory control are two desirable features for robotics, but they become very challenging for soft robots without rigid structures to support rapid manipulations. Here, a curved piezoelectric thin film driven at its structural resonant frequency is used as the main body of an insect-scale soft robot for its fast translational movements, and two electrostatic footpads are used for its swift rotational motions. These two schemes are simultaneously executed during operations through a simple two-wire connection arrangement. A high relative centripetal acceleration of 28 body length per square second compared with existing robots is realized on a 65-milligram tethered prototype, which is better than those of common insects, including the cockroach. The trajectory manipulation demonstration is accomplished by navigating the robot to pass through a 120-centimeter-long track in a maze within 5.6 seconds. One potential application is presented by carrying a 180-milligram on-board sensor to record a gas concentration route map and to identify the location of the leakage source. The radically simplified analog motion adjustment technique enables the scale-up construction of a 240-milligram untethered robot. Equipped with a payload of 1660 milligrams to include the control circuit, a battery, and photoresistors, the untethered prototype can follow a designated, 27.9-centimeter-long "S"-shaped path in 36.9 seconds. These results validate key performance attributes in achieving both high mobility and agility to emulate living agile insects for the advancements of soft robots.

Intelligent Spacecraft Visual GNC Architecture With the State-Of-the-Art AI Components for On-Orbit Manipulation.

Conventional spacecraft Guidance, Navigation, and Control (GNC) architectures have been designed to receive and execute commands from ground control with minimal automation and autonomy onboard spacecraft. In contrast, Artificial Intelligence (AI)-based systems can allow real-time decision-making by considering system information that is difficult to model and incorporate in the conventional decision-making process involving ground control or human operators. With growing interests in on-orbit services with manipulation, the conventional GNC faces numerous challenges in adapting to a wide range of possible scenarios, such as removing unknown debris, potentially addressed using emerging AI-enabled robotic technologies. However, a complete paradigm shift may need years' efforts. As an intermediate solution, we introduce a novel visual GNC system with two state-of-the-art AI modules to replace the corresponding functions in the conventional GNC system for on-orbit manipulation. The AI components are as follows: (i) A Deep Learning (DL)-based pose estimation algorithm that can estimate a target's pose from two-dimensional images using a pre-trained neural network without requiring any prior information on the dynamics or state of the target. (ii) A technique for modeling and controlling space robot manipulator trajectories using probabilistic modeling and reproduction to previously unseen situations to avoid complex trajectory optimizations on board. This also minimizes the attitude disturbances of spacecraft induced on it due to the motion of the robot arm. This architecture uses a centralized camera network as the main sensor, and the trajectory learning module of the 7 degrees of freedom robotic arm is integrated into the GNC system. The intelligent visual GNC system is demonstrated by simulation of a conceptual mission—AISAT. The mission is a micro-satellite to carry out on-orbit manipulation around a non-cooperative CubeSat. The simulation shows how the GNC system works in discrete-time simulation with the control and trajectory planning are generated in Matlab/Simulink. The physics rendering engine, Eevee, renders the whole simulation to provide a graphic realism for the DL pose estimation. In the end, the testbeds developed to evaluate and demonstrate the GNC system are also introduced. The novel intelligent GNC system can be a stepping stone toward future fully autonomous orbital robot systems.

Adaptive nonsingular fixed-time sliding mode control for uncertain robotic manipulators under actuator saturation

This paper describes an adaptive nonsingular fixed-time sliding mode control (ANFSMC) scheme under actuator saturation that can track the trajectory of a robotic manipulator under external disturbances and inertia uncertainties. First, a novel NFSMC that offers rapid convergence and avoids singularities is proposed for ensuring robotic manipulators global approximate fixed-time convergence. An ANFSMC is then developed for which the bound of the coupling uncertainty is not necessary to know in advance. The controller exhibits small absolute tracking errors and consumes little energy. An actuator saturation compensator is designed and shown to minimize the chattering of the system while accelerating the trajectory tracking. The proposed schemes are analyzed using Lyapunov stability theory, and their effectiveness and superiority are demonstrated through numerical simulations.

Demonstration of the EMPATHIC Framework for Task Learning from Implicit Human Feedback

Reactions such as gestures, facial expressions, and vocalizations are an abundant, naturally occurring channel of information that humans provide during interactions. An agent could leverage an understanding of such implicit human feedback to improve its task performance at no cost to the human. This approach contrasts with common agent teaching methods based on demonstrations, critiques, or other guidance that need to be attentively and intentionally provided. In this work, we demonstrate a novel data-driven framework for learning from implicit human feedback, EMPATHIC. This two-stage method consists of (1) mapping implicit human feedback to relevant task statistics such as reward, optimality, and advantage; and (2) using such a mapping to learn a task. We instantiate the first stage and three second-stage evaluations of the learned mapping. To do so, we collect a dataset of human facial reactions while participants observe an agent execute a sub-optimal policy for a prescribed training task. We train a deep neural network on this data and demonstrate its ability to (1) infer relative reward ranking of events in the training task from prerecorded human facial reactions; (2) improve the policy of an agent in the training task using live human facial reactions; and (3) transfer to a novel domain in which it evaluates robot manipulation trajectories. In the video, we focus on demonstrating the online learning capability of our instantiation of EMPATHIC.

Accelerating trajectory manipulation of symmetric Pearcey Gaussian beam in a uniformly moving parabolic potential.

We derive analytical solutions that describe the one-dimensional displaced and chirped symmetric Pearcey Gaussian beam in a uniformly moving parabolic potential. The multiple effective manipulations of the beam, which are originated from the diverse configurations of the dynamic parabolic potential, are demonstrated. On the whole, the accelerating trajectory can transform into a linear superposition form of the oblique straight line and the simple harmonic motion. Meanwhile, we discuss the further modulation of the accelerating trajectory characteristics such as slope, amplitude and phase shift. Additionally, the extension into a two-dimensional scenario is also proposed. Our results theoretically improve the practical value of the Pearcey beam, and lead to potential applications in trajectory manipulation and particle manipulation.

Robust Controller for Pursuing Trajectory and Force Estimations of a Bilateral Tele-Operated Hydraulic Manipulator

In hazardous/emergency situations, public safety is of the utmost concern. In areas where human access is not possible or is restricted due to hazardous situations, a system or robot that can be distantly controlled is mandatory. There are many applications in which force cannot be applied directly while using physical sensors. Therefore, in this research, a robust controller for pursuing trajectory and force estimations while deprived of any signals or sensors for bilateral tele-operation of a hydraulic manipulator is suggested to handle these hazardous, emergency circumstances. A terminal sliding control with a sliding perturbation observer (TSMCSPO) is considered as the robust controller for a coupled leader and hydraulic follower system. The ultimate use of this controller is as a sliding perturbation observer (SPO) that can estimate the reaction force without any physical force sensors. Robust and perfect position tracking is attained with terminal sliding mode control (TSMC) in addition to control of the hydraulic follower manipulator. The force estimation and pursuing trajectory for the leader–follower system is built upon a bilateral tele-operation control approach. The difference between the reaction forces (caused by the remote environment) and the operating forces (applied by the human operator) required the involvement of an impedance model. The impedance model is implemented in the leader manipulator to provide human operators with an actual sense of the reaction force while the manipulator connects with the remote environment. A camera is used to ensure the safety of the workplace through visual feedback. The experimental results showed that the controller was robust at pursuing trajectory and force estimations for the bilateral tele-operation control of a hydraulic manipulator.

Shape-Preserving and Reactive Adaptation of Robot End-Effector Trajectories

Variation in task requirements and operation in uncertain environments require robots that can continuously adapt their motions during execution. To do this successfully, the original motion characteristics must be preserved. This paper presents an approach for on-line adaptation of end-effector trajectories for robot manipulators. The trajectory generator aims to preserve the shape of a given model trajectory through its coordinate-invariant shape description. In addition, this trajectory generator is coupled to a task specification and control framework that enables reactive behavior of the robot. Simulation results on the UR10 robot indicate the capability of the developed approach to generalize end-effector trajectories towards new targets while preserving the original trajectory shape. The controller's reactivity is shown towards moving targets and obstacles in the environment. The combination of shape preservation and reactive control may endow robots with better on-line motion generalization capabilities.

A Robotic grinding station based on an industrial manipulator and vision system.

Due to ever increasing precision and automation demands in robotic grinding, the automatic and robust robotic grinding workstation has become a research hot-spot. This work proposes a grinding workstation constituting of machine vision and an industrial manipulator to solve the difficulty of positioning rough metal cast objects and automatic grinding. Faced with the complex characteristics of industrial environment, such as weak contrast, light nonuniformity and scarcity, a coarse-to-fine two-step localization strategy was used for obtaining the object position. The deep neural network and template matching method were employed for determining the object position precisely in the presence of ambient light. Subsequently, edge extraction and contour fitting techniques were used to measure the position of the contour of the object and to locate the main burr on its surface after eliminating the influence of burr. The grid method was employed for detecting the main burrs, and the offline grinding trajectory of the industrial manipulator was planned with the guidance of the coordinate transformation method. The system greatly improves the automaticity through the entire process of loading, grinding and unloading. It can determine the object position and target the robotic grinding trajectory by the shape of the burr on the surface of an object. The measurements indicate that this system can work stably and efficiently, and the experimental results demonstrate the high accuracy and high efficiency of the proposed method. Meanwhile, it could well overcome the influence of the materials of grinding work pieces, scratch and rust.

Method of Supervisory Control of Manipulator Mounted on Underwater Vehicle

The report describes the new supervisory control method for a multi-link manipulator mounted on an underwater vehicle, providing visual control of manipulation operations. The proposed method allows to build complex spatial movement trajectories of a multi-link manipulator working tool along a work object surface, using data obtained from onboard multibeam sonars or technical vision systems. Target designations of the operator determine the trajectory type, its position and orientation in a work area. To form target designations and visual observation of manipulator working tool movements along a given trajectory, a TV camera is used that changes its optical axis spatial orientation. The developed method is implemented in the C++ programming language, as well as numerical simulation of a multi-link underwater manipulator supervisory control in the virtual V-REP environment. To visualize the method, the graphical shell is written using "OpenGL Core".

Loos, a Better Tool to Analyze Molecular Dynamics Simulations

The enormous improvements in computational power in recent years means that we can routinely runs simulations of biologically interesting systems out to the microsecond scale or longer. However, in many cases their scientific impact is limited by an inability to extract useful information from this wealth of data. We created LOOS to unlock this previously unrealized potential. Molecular dynamics is unique among biophysical techniques in that the raw data can be interrogated in a multitude of ways; getting the most out of the data usually requires exploration and iterative refinement. LOOS is designed to make this process easy and efficient, to minimize the friction between a user and her data. LOOS can be viewed in two ways: 1) It is a suite of well-validated, easy-to-use tools for analyzing molecular dynamics simulations. These tools include common tasks, such as trajectory manipulation, as well as more advanced techniques such as analysis of simulation convergence, 3D histograms, and Voronoi decomposition, with a particular focus on membranes and membrane-bound proteins. 2) It is a tool for making tools. At its heart, LOOS is a C++ library covered with a Python interface, for the optimal mix of high performance and ease of development. LOOS is designed for rapid application development, so that most common tasks (reading files, selecting atoms, aligning structures, computing distances, etc) are one-line operations. LOOS is licensed under the GPLv3 license and available via GitHub (https://github.com/GrossfieldLab/loos).

A generalized varying-parameter recurrent neural network for super solution of quadratic programming problem

To obtain superior convergent speed, a generalized varying-parameter recurrent neural network (GVP-RNN) is established and analyzed for solving quadratic programming (QP) problems. Different from the classical fix-parameter neural network, such as Zhang neural network (ZNN) and finite-time convergent differential neural network (FT-CDNN), GVP-RNN dynamics can achieve exponential convergent results and possess high precision. By Lagrange theorem, QP problems can be transformed to a time-varying matrix equation, which is prepared for the GVP-RNN model. Theoretical analysis has shown the GVP-RNN model takes a super-exponential convergence comparing the corresponding simulated results of ZNN and FT-CDNN. Simulation comparisons are presented to evaluate the theoretical performance of the GVP-RNN with three types of activation functions. Furthermore, the proposed GVP-RNN model is analyzed and applied to control kinematic trajectory of redundant manipulators.

Dynamically Feasible Task Space Planning for Underactuated Aerial Manipulators

In this work, we address the problem of planning dynamically feasible trajectories for underactuated aerial manipulators to achieve a desired trajectory for the end effector. We consider a quadrotor equipped with an arbitrary n-joint articulated arm. We show that the combined underactuated system is differentially flat, however the flat outputs do not correspond directly to the motion of the end effector. We therefore develop a method which determines the family of flat output trajectories which will exactly produce any desired task trajectory, even in the case of dynamic maneuvers. We also give criteria on the manipulator geometry which will ensure certain important stability properties, informing hardware design. The entire approach is demonstrated in simulation for systems of varying geometry and number of joints. The simultaneous resolution of the kinematic and dynamic constraints allows these tasks to be performed dynamically without sacrificing accuracy.

Research on Space Motion Trajectory Optimization of the Industrial Robot

In this paper, an online iterative learning planning method is proposed for industrial robot joint space manipulator to capture moving objects. The dynamic mathematical model of the robot is established, and the trajectory of the robot manipulator is tracked and adjusted through the path planning algorithm to change the visual impedance of the robot and optimize the system parameters. It makes the velocity direction of the end effector of the manipulator consistent with the tangent direction of the weld, and realizes the tracking and recognition of the weld trajectory at the end of the manipulator, so as to improve the accuracy and reliability of the robot in capturing moving objects. The simulation results show that the algorithm has good convergence and robustness.

Finite-Time Tracking Controller Design of Perturbed Robotic Manipulator Based on Adaptive Second-Order Sliding Mode Control Method

This article presents an adaptive finite time second-order sliding mode tracking control scheme for robotic manipulators. A new control law is suggested to force the trajectories of the robot manipulator to move from all initial conditions to proportional-integral-derivative switching surface in the finite time and stay on it. Moreover, the adaptation law rejects the requirement of knowledge about upper bound of the external perturbations. It is difficult to exactly determine the upper bound of the perturbations in the practical systems, such as robot manipulators. Unlike the existing methods, the new adaptive finite time second-order sliding mode tracker for $n$ -link robot manipulators enables accurate tracking control, robust performance and parameter tuning. The suggested approach presents the design of a robust controller such that the tracking errors of robot manipulator can reach the equilibrium in the finite time. Through the combination of the finite time tracker and disturbance observer, the position tracking purpose of manipulator joints is accurately performed not only in the nominal environment, but also in the existence of different types of perturbations. The robustness performance and effectiveness of the offered technique are studied in simulation and experimental results.

基于精准碰撞检测算法的机械臂避障轨迹规划

基于精准碰撞检测算法的机械臂避障轨迹规划