2,262 research outputs found
Dynamic modeling, property investigation, and adaptive controller design of serial robotic manipulators modeled with structural compliance
Research results on general serial robotic manipulators modeled with structural compliances are presented. Two compliant manipulator modeling approaches, distributed and lumped parameter models, are used in this study. System dynamic equations for both compliant models are derived by using the first and second order influence coefficients. Also, the properties of compliant manipulator system dynamics are investigated. One of the properties, which is defined as inaccessibility of vibratory modes, is shown to display a distinct character associated with compliant manipulators. This property indicates the impact of robot geometry on the control of structural oscillations. Example studies are provided to illustrate the physical interpretation of inaccessibility of vibratory modes. Two types of controllers are designed for compliant manipulators modeled by either lumped or distributed parameter techniques. In order to maintain the generality of the results, neither linearization is introduced. Example simulations are given to demonstrate the controller performance. The second type controller is also built for general serial robot arms and is adaptive in nature which can estimate uncertain payload parameters on-line and simultaneously maintain trajectory tracking properties. The relation between manipulator motion tracking capability and convergence of parameter estimation properties is discussed through example case studies. The effect of control input update delays on adaptive controller performance is also studied
Identification of robotic manipulators' inverse dynamics coefficients via model-based adaptive networks
The values of a given manipulator's dynamics coefficients need to be accurately
identified in order to employ model-based algorithms in the control of its motion. This
thesis details the development of a novel form of adaptive network which is capable of
accurately learning the coefficients of systems, such as manipulator inverse dynamics,
where the algebraic form is known but the coefficients' values are not. Empirical motion
data from a pair of PUMA 560s has been processed by the Context-Sensitive Linear
Combiner (CSLC) network developed, and the coefficients of their inverse dynamics
identified. The resultant precision of control is shown to be superior to that achieved from
employing dynamics coefficients derived from direct measurement.
As part of the development of the CSLC network, the process of network learning is
examined. This analysis reveals that current network architectures for processing analogue
output systems with high input order are highly unlikely to produce solutions that are
good estimates throughout the entire problem space. In contrast, the CSLC network is
shown to generalise intrinsically as a result of its structure, whilst its training is greatly
simplified by the presence of only one minima in the network's error hypersurface.
Furthermore, a fine-tuning algorithm for network training is presented which takes
advantage of the CSLC network's single adaptive layer structure and does not rely upon
gradient descent of the network error hypersurface, which commonly slows the later
stages of network training
Biomimetic Manipulator Control Design for Bimanual Tasks in the Natural Environment
As robots become more prolific in the human environment, it is important that safe operational
procedures are introduced at the same time; typical robot control methods are
often very stiff to maintain good positional tracking, but this makes contact (purposeful
or accidental) with the robot dangerous. In addition, if robots are to work cooperatively
with humans, natural interaction between agents will make tasks easier to perform with
less effort and learning time. Stability of the robot is particularly important in this
situation, especially as outside forces are likely to affect the manipulator when in a close
working environment; for example, a user leaning on the arm, or task-related disturbance
at the end-effector.
Recent research has discovered the mechanisms of how humans adapt the applied force
and impedance during tasks. Studies have been performed to apply this adaptation to
robots, with promising results showing an improvement in tracking and effort reduction
over other adaptive methods. The basic algorithm is straightforward to implement,
and allows the robot to be compliant most of the time and only stiff when required by
the task. This allows the robot to work in an environment close to humans, but also
suggests that it could create a natural work interaction with a human. In addition, no
force sensor is needed, which means the algorithm can be implemented on almost any
robot.
This work develops a stable control method for bimanual robot tasks, which could also
be applied to robot-human interactive tasks. A dynamic model of the Baxter robot is
created and verified, which is then used for controller simulations. The biomimetic control
algorithm forms the basis of the controller, which is developed into a hybrid control
system to improve both task-space and joint-space control when the manipulator is disturbed
in the natural environment. Fuzzy systems are implemented to remove the need
for repetitive and time consuming parameter tuning, and also allows the controller to
actively improve performance during the task. Experimental simulations are performed,
and demonstrate how the hybrid task/joint-space controller performs better than either
of the component parts under the same conditions. The fuzzy tuning method is then applied
to the hybrid controller, which is shown to slightly improve performance as well as
automating the gain tuning process. In summary, a novel biomimetic hybrid controller
is presented, with a fuzzy mechanism to avoid the gain tuning process, finalised with a
demonstration of task-suitability in a bimanual-type situation.EPSR
Robust iterative feedback tuning control of a compliant rehabilitation robot for repetitive ankle training
Robot-assisted rehabilitation offers benefits, such as repetitive, intensive, and task-specific training, as compared to traditional manual manipulation performed by physiotherapists. In this paper, a robust iterative feedback tuning (IFT) technique for repetitive training control of a compliant parallel ankle rehabilitation robot is presented. The robot employs four parallel intrinsically compliant pneumatic muscle actuators that mimic skeletal muscles for ankle's motion training. A multiple degrees-of-freedom normalized IFT technique is proposed to increase the controller robustness by obtaining an optimal value for the weighting factor and offering a method with learning capacity to achieve an optimum of the controller parameters. Experiments with human participants were conducted to investigate the robustness as well as to validate the performance of the proposed IFT technique. Results show that the normalized IFT scheme will achieve a better and better tracking performance during the robot repetitive control and provides more robustness to the system by adapting to various situations in robotic rehabilitation
Benchmarking Cerebellar Control
Cerebellar models have long been advocated as viable models
for robot dynamics control. Building on an increasing insight
in and knowledge of the biological cerebellum, many models have been
greatly refined, of which some computational models have emerged
with useful properties with respect to robot dynamics control.
Looking at the application side, however, there is a totally different
picture. Not only is there not one robot on the market which uses
anything remotely connected with cerebellar control, but even in
research labs most testbeds for cerebellar models are restricted to
toy problems. Such applications hardly ever exceed the complexity of
a 2 DoF simulated robot arm; a task which is hardly representative for
the field of robotics, or relates to realistic applications.
In order to bring the amalgamation of the two fields forwards, we
advocate the use of a set of robotics benchmarks, on which existing
and new computational cerebellar models can be comparatively tested.
It is clear that the traditional approach to solve robotics dynamics
loses ground with the advancing complexity of robotic structures;
there is a desire for adaptive methods which can compete as traditional
control methods do for traditional robots.
In this paper we try to lay down the successes and problems in the
fields of cerebellar modelling as well as robot dynamics control.
By analyzing the common ground, a set of benchmarks is suggested
which may serve as typical robot applications for cerebellar models
Bidirectional recurrent learning of inverse dynamic models for robots with elastic joints: a real-time real-world implementation
Collaborative robots, or cobots, are designed to work alongside humans and to alleviate their physical burdens, such as lifting heavy objects or performing tedious tasks. Ensuring the safety of human–robot interaction (HRI) is paramount for effective collaboration. To achieve this, it is essential to have a reliable dynamic model of the cobot that enables the implementation of torque control strategies. These strategies aim to achieve accurate motion while minimizing the amount of torque exerted by the robot. However, modeling the complex non-linear dynamics of cobots with elastic actuators poses a challenge for traditional analytical modeling techniques. Instead, cobot dynamic modeling needs to be learned through data-driven approaches, rather than analytical equation-driven modeling. In this study, we propose and evaluate three machine learning (ML) approaches based on bidirectional recurrent neural networks (BRNNs) for learning the inverse dynamic model of a cobot equipped with elastic actuators. We also provide our ML approaches with a representative training dataset of the cobot's joint positions, velocities, and corresponding torque values. The first ML approach uses a non-parametric configuration, while the other two implement semi-parametric configurations. All three ML approaches outperform the rigid-bodied dynamic model provided by the cobot's manufacturer in terms of torque precision while maintaining their generalization capabilities and real-time operation due to the optimized sample dataset size and network dimensions. Despite the similarity in torque estimation of these three configurations, the non-parametric configuration was specifically designed for worst-case scenarios where the robot dynamics are completely unknown. Finally, we validate the applicability of our ML approaches by integrating the worst-case non-parametric configuration as a controller within a feedforward loop. We verify the accuracy of the learned inverse dynamic model by comparing it to the actual cobot performance. Our non-parametric architecture outperforms the robot's default factory position controller in terms of accuracy.IMOCOe4.0 [EU H2020RIA-101007311]Spanish national funding [PCI2021-121925INTSENSO [MICINN-FEDER-PID2019-
109991GB-I00]INTARE (TED2021-131466B-I00)
projects funded by MCIN/AEI/10.13039/501100011033EU
NextGenerationEU/PRTR to ERThe SPIKEAGE [MICINN629PID2020-113422GAI00]DLROB
[TED2021 131294B-I00]Spanish Ministry
of Science and Innovation MCIN/AEI/10.13039/501100011033
and European Union NextGenerationEU/PRT
Learning Deep Nets for Gravitational Dynamics with Unknown Disturbance through Physical Knowledge Distillation: Initial Feasibility Study
Learning high-performance deep neural networks for dynamic modeling of high
Degree-Of-Freedom (DOF) robots remains challenging due to the sampling
complexity. Typical unknown system disturbance caused by unmodeled dynamics
(such as internal compliance, cables) further exacerbates the problem. In this
paper, a novel framework characterized by both high data efficiency and
disturbance-adapting capability is proposed to address the problem of modeling
gravitational dynamics using deep nets in feedforward gravity compensation
control for high-DOF master manipulators with unknown disturbance. In
particular, Feedforward Deep Neural Networks (FDNNs) are learned from both
prior knowledge of an existing analytical model and observation of the robot
system by Knowledge Distillation (KD). Through extensive experiments in
high-DOF master manipulators with significant disturbance, we show that our
method surpasses a standard Learning-from-Scratch (LfS) approach in terms of
data efficiency and disturbance adaptation. Our initial feasibility study has
demonstrated the potential of outperforming the analytical teacher model as the
training data increases
Modelling and control of lightweight underwater vehicle-manipulator systems
This thesis studies the mathematical description and the low-level control structures for
underwater robotic systems performing motion and interaction tasks. The main focus is
on the study of lightweight underwater-vehicle manipulator systems. A description of
the dynamic and hydrodynamic modelling of the underwater vehicle-manipulator system
(UVMS) is presented and a study of the coupling effects between the vehicle and manipulator
is given. Through simulation results it is shown that the vehicle’s capabilities are
degraded by the motion of the manipulator, when it has a considerable mass with respect to
the vehicle. Understanding the interaction effects between the two subsystems is beneficial
in developing new control architectures that can improve the performance of the system.
A control strategy is proposed for reducing the coupling effects between the two subsystems
when motion tasks are required. The method is developed based on the mathematical
model of the UVMS and the estimated interaction effects. Simulation results show the validity
of the proposed control structure even in the presence of uncertainties in the dynamic
model. The problem of autonomous interaction with the underwater environment is further
addressed. The thesis proposes a parallel position/force control structure for lightweight underwater
vehicle-manipulator systems. Two different strategies for integrating this control
law on the vehicle-manipulator structure are proposed. The first strategy uses the parallel
control law for the manipulator while a different control law, the Proportional Integral
Limited control structure, is used for the vehicle. The second strategy treats the underwater
vehicle-manipulator system as a single system and the parallel position/force law is
used for the overall system. The low level parallel position/force control law is validated
through practical experiments using the HDT-MK3-M electric manipulator. The Proportional
Integral Limited control structure is tested using a 5 degrees-of-freedom underwater
vehicle in a wave-tank facility. Furthermore, an adaptive tuning method based on interaction
theory is proposed for adjusting the gains of the controller. The experimental results
show that the method is advantageous as it decreases the complexity of the manual tuning
otherwise required and reduces the energy consumption. The main objectives of this
thesis are to understand and accurately represent the behaviour of an underwater vehiclemanipulator
system, to evaluate this system when in contact with the environment and to
design informed low-level control structures based on the observations made through the
mathematical study of the system. The concepts presented in this thesis are not restricted
to only vehicle-manipulator systems but can be applied to different other multibody robotic
systems
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