Articulated Robot Research Articles

Electrostatic brakes enable individual joint control of underactuated, highly articulated robots

Highly articulated organisms serve as blueprints for incredibly dexterous mechanisms, but building similarly capable robotic counterparts has been hindered by the difficulties of developing electromechanical actuators with both the high strength and compactness of biological muscle. We develop a stackable electrostatic brake that has comparable specific tension and weight to that of muscles and integrate it into a robotic joint. High degree-of-freedom mechanisms composed of such electrostatic brake enabled joints can then employ established control algorithms to achieve hybrid motor-brake actuated dexterous manipulation. Specifically, our joint design enables a ten degree-of-freedom robot equipped with only one motor to manipulate multiple objects simultaneously. We also show that the use of brakes allows a two-fingered robot to perform in-hand re-positioning of an object 45% more quickly and with 53% lower positioning error than without brakes. Relative to fully actuated robots, robots equipped with such electrostatic brakes will have lower weight, volume, and power consumption yet retain the ability to reach arbitrary joint configurations.

Task Accuracy Enhancement for a Surgical Macro-Micro Manipulator With Probabilistic Neural Networks and Uncertainty Minimization

Accurate robot kinematic modelling is a major component for autonomous robot control to guarantee safety and precision during task execution. In surgical robotics complex robotic structures and actuation mechanisms are generally employed, therefore machine learning techniques can be adopted to build the model of the robot. Probabilistic neural networks are a class of learning approaches that provide information about the uncertainty of the learnt models. In this work we compare two different probabilistic neural networks (Bayesian and Evidential Neural Networks) to model the kinematics of a surgical robotic instrument and propose a control strategy based on Hierarchical Quadratic Programming (HQP) capable of exploiting the model uncertainty to improve the accuracy and safety of the controller. Simulation and real world experiments on different autonomous path tracking tasks show that the model uncertainty highly affects the control performances and prove the effectiveness of the proposed controller in improving task execution. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —The push towards reducing invasiveness and patient’s traumas in surgery has lead to the requirement of miniaturized and highly articulated robots. This however comes at the cost of having systems that are hard to model and control, which is one of the major limitations for autonomy in surgical robotics. Machine learning has become very effective in modelling complex systems and probabilistic approaches additionally allow estimating the confidence of the learnt model. In robotics field where high precision is required to perform an autonomous task, like in minimally invasive surgery, the robot model needs to be very accurate and controllers need to guarantee safety in performing the desired task, while satisfying additional motion constraints imposed by the application scenario. This work proposes the use of probabilistic neural networks to model the complexity of a surgical robotic instrument and a control strategy capable of ensuring safety by maximizing model’s confidence and guaranteeing satisfaction of imposed motion constraints. In this work a macro-micro manipulator setup is employed, consisting of an articulated surgical robotic instrument connected to a serial-link manipulator. The proposed modelling and control approaches can be used in any other field where controllers need to highly rely on the robot model due to limitations in using external sensors and where leveraging information about model’s confidence can be beneficial. Currently, the work focuses only on pure kinematic modelling and control, thus neglecting any possible interaction with the environment. Future work will focus on addressing this limitation in order to ensure proper force control and effective autonomy.

Influence of the velocity on quasi-static deflections of industrial articulated robots

This article presents the measurement and analysis of the influence of velocity on the quasi-static deflections of industrial manipulators of three different manufacturers. Quasi-static deflection refers to the deflection of the end effector position of articulated robots during movement at low velocity along a predefined trajectory. Based on earlier reported observations by the authors, there exists a difference in the static and quasi-static deflections considering the same points along a trajectory. This work investigates this difference to assess the applicability of robotic compliance calibration at low velocities. For this assessment, the deflections of three industrial articulated robots were measured at different speeds and loads. Considering the similarity among the robot models used in this investigation, this work also elaborates on the potential influence of the measurement procedure on the measured deflections and its implications for the compliance calibration of articulated robots. For all industrial articulated robots in this investigation, the quasi-static deflections are significantly larger than the static ones but similar in trend. Additionally, the magnitude of the quasi-static deflections presents a proportional relationship to the Cartesian velocity.

Robust and Decoupled Position and Stiffness Control for Electrically-Driven Articulated Soft Robots

The control of articulated soft robots, i.e. robots with flexible joints and rigid links, presents a challenge due to their intrinsic elastic elements and nonlinear force-deflection dependency. This letter first proposes a discrete-time delayed unknown input-state observer based on a nominal robot model that reconstructs the total torque disturbance vector, resulting from the imperfect knowledge of the elastic torque characteristic, external torques, and other model uncertainties. Then, it introduces a robust controller, that actively compensates for the estimated uncertainty and allows bounded stability for the tracking of independent link position and joint stiffness reference signals. The convergence of the disturbance estimator and the overall system’s stability in closed loop is proven analytically, while the effectiveness of the proposed control design is first evaluated in simulations with respect to large uncertainty conditions, and then demonstrated through experiments on a real multi-degree-of-freedom articulated soft robot.

Model Learning With Backlash Compensation for a Tendon-Driven Surgical Robot

Robots for minimally invasive surgery are becoming more and more complex, due to miniaturization and flexibility requirements. The vast majority of surgical robots are tendon-driven and this, along with the complex design, causes high nonlinearities in the system which are difficult to model analytically. In this work we analyse how incorporating a backlash model and compensation can improve model learning and control. We combine a backlash compensation technique and a Feedforward Artificial Neural Network (ANN) with differential relationships to learn the kinematics at position and velocity level of highly articulated tendon-driven robots. Experimental results show that the proposed backlash compensation is effective in reducing nonlinearities in the system, that compensating for backlash improves model learning and control, and that our proposed ANN outperforms traditional ANN in terms of path tracking accuracy.

Manufacturing and controlling 5-axis ball screw driven industrial robot moving through G codes

These days, in industrial robot applications, servo motors with reducer have been used to move robot manipulators. Due to the weight of the robot parts, it causes undesirable conditions such as backward movement, downward movement and vibration caused by the moments of inertia at the time of movement. In this study, step motor ball screw driven actuators, which use push-pull force instead of servo motor with reducers, were utilized to provide rotational movement in robot arms. The ball screw system reduces the effect of moments of inertia caused by the movements of the robot joints, and restricts the free movements of the robot joints. It also provides an advantage by reducing costs since it uses smaller motors. In articulated robots, the complexity of kinematic equations and the use of high-order polynomials for trajectory planning cause difficulties in controlling. In this study, the information of the points, where the robot went during the manual movement, was obtained by using the macros prepared in program interface without using kinematic and trajectory planning equations. The position lines taken at these points were printed in a text file by adding the code G01 and f feed-rate. The robot was made to move by running this file in the program interface used for control. This method allows the operator to use the robot without advanced kinematics knowledge.

Micrometer Positioning Accuracy With a Planar Parallel Continuum Robot.

Parallel Continuum Robots (PCR) have several advantages over classical articulated robots, notably a large workspace, miniaturization capabilities and safe human-robot interactions. However, their low accuracy is still a serious drawback. Indeed, several conditions have to be met for PCR to reach a high accuracy, namely: a repeatable mechanical structure, a correct kinematic model, and a proper estimation of the model’s parameters. In this article, we propose a methodology that allows reaching a micrometer accuracy with a PCR. This approach emphasizes the importance of using a repeatable continuum mechanism, identifying the most influential parameters of an accurate kinematic model of the robot and precisely measuring them. The experimental results show that the proposed approach allows to reach an accuracy of 3.3 µm in position and 0.5 mrad in orientation over a 10 mm long circular path. These results push the current limits of PCR accuracy and make them good potential candidates for high accuracy automatic positioning tasks.

Efficient Representation of Ducts and Cluttered Spaces for Realistic Motion Planning of Hyper-Redundant Robots through Confined Paths

Application of highly articulated hyper-redundant robots to manoeuvre in narrow and confined spaces is gaining popularity due to their obvious advantages. In this paper, we describe an optimization based approach for motion planning of hyper-redundant robots, which results in a natural motion of the links through ducts and confined spaces. It is shown that for a desired motion of the end-effector or the head of the hyper-redundant robot, the motion of the subsequent links attenuate and all the links avoid collision with the walls of the ducts and any other obstacles in the confined spaces. We discuss several ways to represent ducts in 2D and 3D space and also how the proposed algorithm is applied in these representations. It is shown that the complexity of the algorithm, with m constraints is at most O(m3.5) and in case where the ducts can be modeled with polyhedra, the complexity can be as low as O(m1.5). The proposed approach is also used to determine the largest link length in the hyper-redundant robot which can traverse the confined path. The concepts developed in this paper are demonstrated using simulations conducted on three practical scenarios: 1) hyper-redundant manipulators inspecting an industrial pipeline, 2) motion of an endoscopic robot through gastro-intestinal (GI) tract and 3) motion of hyper-redundant manipulators in search and rescue operations. Analysis on the computational complexity and the simulations shows that the method is feasible for practical implementation.

Evaluation of Joint Driving Torque and Finite Element Analysis in Articulate Robot through the simulation of Multi-Body Dynamics

Interest in high-speed articulated robots is increasing for product productivity expansion. High-speed articulated robots operate with rapid acceleration/deceleration moves, requiring dynamic characteristic analysis in the robot designing process. For this dynamic behavior analysis, simulation software is utilized, which supports product design verification and parts optimization. In analyzing the dynamic characteristics using the software, loading conditions can be obtained from experimental data or parts’ material characteristics. In a special case where data or experimental data on load conditions are hardly obtainable, multibody dynamics software is utilized. However, it is not easy to define an effective load and boundary conditions for systems with kinetically complicated connections. In order to solve such a problem, this present study investigated how to apply to structural analysis software the dynamic load found using dynamics and structural analysis software. In addition, the dynamic characteristics of high-speed articulated robots and robot link were assumed as a rigid body in implementing the dynamics analysis and structural analysis.

Analysis of non-geometric accuracy effects of articulated robots

PurposeArticulated robots are widely used in industrial applications owing to their high repeatability accuracy. In terms of new applications such as robot-based inspection systems, the limitation is a lack of pose accuracy. Mostly, robot calibration approaches are used for the improvement of the pose accuracy. Such approaches however require a profound understanding of the determining effects. This paper aims to provide a non-destructive analysis method for the identification and characterisation of non-geometric accuracy effects in relation to the kinematic structure for the purpose of an accuracy enhancement.Design/methodology/approachThe analysis is realised by a non-destructive method for rotational, uncoupled robot axes with the use of a 3D lasertracker. For each robot axis, the lasertracker position data for multiple reflectors are merged with the joint angles given by the robot controller. Based on this, the joint characteristics are determined. Furthermore, the influence of the kinematic structure is investigated.FindingsThis paper analyses the influence of the kinematic structure and non-geometric effects on the pose accuracy of standard articulated robots. The provided method is shown for two different industrial robots and presented effects incorporate tilting of the robot, torsional joint stiffness, hysteresis, influence of counter balance systems, as well as wear and damage.Practical implicationsBased on these results, an improved robot model for a better match between the mathematical description and the real robot system can be achieved by characterising non-geometric effects. In addition, wear and damages can be identified without a disassembly of the system.Originality/valueThe presented method for the analysis of non-geometric effects can be used in general for rotational, uncoupled robot axes. Furthermore, the investigated accuracy influencing effects can be taken into account to realise high-accuracy applications.

High performance rigid body simulation of modularized robots using constraint-based models

Modularization using self-controlling servo-motors allows for rapid prototyping of highly articulated robots at a much lower cost than custom designs. Microcontrollers within the motor casings allow the internal motor to be controlled in a closed loop running at a much higher frequency than possible with external commands, at the cost of an inability to use directly estimated motor responses, especially when internal components are not known. Although electro-mechanical motor models have been used to simulate systems directly when the module components are known, this requires full knowledge of the system and small steps during discrete time simulation to prevent instabilities in the simulation and control. We propose a rigid body constraint based model for the simulation of internally controlled articulated robot modules that can reduce simulation instabilities at larger time-steps without requiring simulation specific non-physical damping or closed form dynamic solutions. This method uses the properties of rigid body constraints to limit system dynamics when low system inertia or high control gains would otherwise result in physically impossible performance. In practice, the simulation accuracy of this method is comparable to traditional models, but an order of magnitude faster in practice due to larger time-steps and improved range of stability.

Effective Manipulation in Confined Spaces of Highly Articulated Robotic Instruments for Single Access Surgery

The field of robotic surgery increasingly advances towards highly articulated and continuum robots, requiring new kinematic strategies to enable users to perform dexterous manipulation in confined workspaces. This development is driven by surgical interventions accessing the surgical workspace through natural orifices such as the mouth or the anus. Due to the long and narrow nature of these access pathways, external triangulation at the fulcrum point is very limited or absent, which makes introducing multiple degrees of freedom at the distal end of the instrument necessary. Additionally, high force and miniaturization requirements make the control of such instruments particularly challenging. This letter presents the kinematic considerations needed to effectively manipulate these novel instruments and allow us their dexterous control in confined spaces. A nonlinear calibration model is further used to map joint to actuator space and improve significantly the precision of the instrument's motion. The effectiveness of the presented approach is quantified with bench tests, and the usability of the system is assessed by three user studies simulating the requirements of a realistic surgical task.

多関節アームロボットへの電界結合非接触給電

Both power feeding cable and control signal cable in the arm robot to the servo motor are easily cut due to too fast and frequent rotation of each joint. We propose an electrically coupling contactless power transfer system to an articulated arm robot for solve the problem.

Stability and Performance Limits of Latency-Prone Distributed Feedback Controllers

Robotic systems are increasingly relying on distributed feedback controllers to tackle complex sensing and decision problems, such as those found in highly articulated human-centered robots. These demands come at the cost of a growing computational burden and, as a result, larger controller latencies. To maximize robustness to mechanical disturbances by maximizing control feedback gains, this paper emphasizes the necessity for compromise between high- and low-level feedback control efforts in distributed controllers. Specifically, the effect of distributed impedance controllers is studied, where damping feedback effort is executed in close proximity to the control plant and stiffness feedback effort is executed in a latency-prone centralized control process. A central observation is that the stability of high-impedance distributed controllers is very sensitive to damping feedback delay but much less to stiffness feedback delay. This study pursues a detailed analysis of this observation that leads to a physical understanding of the disparity. Then, a practical controller breakdown gain rule is derived to aim at enabling control designers to consider the benefits of implementing their control applications in a distributed fashion. These considerations are further validated through the analysis, simulation, and experimental testing on high-performance actuators and on an omnidirectional mobile base.

A Filtering Approach for Image-Guided Surgery With a Highly Articulated Surgical Snake Robot.

The objective of this paper is to introduce a probabilistic filtering approach to estimate the pose and internal shape of a highly flexible surgical snake robot during minimally invasive surgery. Our approach renders a depiction of the robot that is registered to preoperatively reconstructed organ models to produce a 3-D visualization that can be used for surgical feedback. Our filtering method estimates the robot shape using an extended Kalman filter that fuses magnetic tracker data with kinematic models that define the motion of the robot. Using Lie derivative analysis, we show that this estimation problem is observable, and thus, the shape and configuration of the robot can be successfully recovered with a sufficient number of magnetic tracker measurements. We validate this study with benchtop and in-vivo image-guidance experiments in which the surgical robot was driven along the epicardial surface of a porcine heart. This paper introduces a filtering approach for shape estimation that can be used for image guidance during minimally invasive surgery. The methods being introduced in this paper enable informative image guidance for highly articulated surgical robots, which benefits the advancement of robotic surgery.

Simulation of Spray Painting Using Articulated – Arm Robot

Robot trajectory generation for spray painting have highly demand in modern manufacturing. One of the most common surfaces in spray painting operations is ruled surfaces. In this paper, a geometry based spray paint trajectory generation system for ruled surfaces has been developed to simulate the spray painting process using articulated arm robot. The developed system uses the geometry information of both the required ruled surface to be painted and the paint spot to generate spray painting trajectory for Labvolt RoboCIM 5150 articulated robot. The developed system is designed by using Matlab software, and has been simulated and evaluated using the Labvolt system. The results have shown that the generated trajectory achieves satisfactory performance.

Variability and consistency in mechatronic design

Mechatronic products combine hardware and software; and today, much of software engineering is directly or indirectly involved to support mechatronic design. Due to the high level of standardization, especially among its hardware, the design of mechatronic products is strongly characterized by integrating standardized components and thus seems an ideal environment for product line engineering techniques, which allow dealing with the variability of reusable components if fully definable a priori. However, while many aspects of mechatronic design are standardized, there is also the need for the continuous construction of new components. However, today, it is difficult to seamlessly integrate the reuse of standardized components with the development of new components. This article presents a model-based approach for integrating component variants with user-defined components to better support the mechatronic design process. We present a combination of existing approaches addressing certain needs of the mechatronic design domain, for example, the integration of components is ensured through incremental consistency checking. Our approach is illustrated using the example of a basic articulated robot.

A METHODOLOGY OF PROGRAMMING FOR TAL TR-2 ARTICULATED ROBOT

A METHODOLOGY OF PROGRAMMING FOR TAL TR-2 ARTICULATED ROBOT

Adaptive rapidly-exploring random tree for efficient path planning of high-degree-of-freedom articulated robots

This article proposes a method for the path planning of high-degree-of-freedom articulated robots with adaptive dimensionality. For efficient path planning in a high-dimensional configuration space, we first describe an adaptive body selection that selects the robot bodies depending on the complexity of the path planning. Then, the involved joints of the selected body are included in the planning process. That is, it builds the C-space (configuration space) with adaptive dimensionality for sampling-based path planner. Next, by using adaptive body selection, the adaptive rapidly-exploring random tree (RRT) algorithm is introduced, which incrementally grows RRTs in the adaptive dimensional C-space. We show through several simulation results that the proposed method is more efficient than the basic RRT-based path planner, which requires full-dimensional planning.

일반화된 보로노이 그래프를 이용한 동일 두 링크 로봇의 센서 기반 경로계획

The generalized Voronoi graph (GVG) is a topological map of a constrained environment. This is defined in terms of workspace distance measurements using only sensor-provided information, with a robot having a maximum distance from obstacles, and is the optimum for exploration and obstacle avoidance. This is the safest path for the robot, and is very significant when studying the GVG edges of highly articulated robots. In previous work, the point-GVG edge and Rod-GVG were built with point robot and rod robot using sensor-based control. An attempt was made to use a higher degree of freedom robot to build GVG edges. This paper presents GVG-based a new local roadmap for the two-link robot in the constrained two-dimensional environment. This new local roadmap is called the two-identical-link generalized Voronoi graph (L2-GVG). This is used to explore an unknown planar workspace and build a local roadmap in an unknown configuration space R 2 × T 2 for a planar two-identical-link robot. The two-identical-link GVG also can be constructed using only sensor-provided information. These results show the more complex properties of two-link-GVG, which are very different from point-GVG and rod-GVG. Furthermore, this approach draws on the experience of other highly articulated robots.