1,308 research outputs found
A method for the assessment and compensation of positioning errors in industrial robots
Industrial Robots (IR) are currently employed in several production areas as they enable flexible automation and high productivity on a wide range of operations. The IR low positioning performance, however, has limited their use in high precision applications, namely where positioning errors assume importance for the process and directly affect the quality of the final products. Common approaches to increase the IR accuracy rely on empirical relations which are valid for a single IR model. Also, existing works show no uniformity regarding the experimental procedures followed during the IR performance assessment and identification phases. With the aim to overcome these restrictions and further extend the IR usability, this paper presents a general method for the evaluation of IR pose and path accuracy, primarily focusing on instrumentation and testing procedures. After a detailed description of the experimental campaign carried out on a KUKA KR210 R2700 Prime robot under different operating conditions (speed, payload and temperature state), a novel online compensation approach is presented and validated. The position corrections are processed with an industrial PC by means of a purposely developed application which receives as input the position feedback from a laser tracker. Experiments conducted on straight paths confirmed the validity of the proposed approach, which allows remarkable reductions (in the order of 90%) of the orthogonal deviations and in-line errors during the robot movements
Characterisation and State Estimation of Magnetic Soft Continuum Robots
Minimally invasive surgery has become more popular as it leads to less bleeding, scarring, pain, and shorter recovery time. However, this has come with counter-intuitive devices and steep surgeon learning curves. Magnetically actuated Soft Continuum Robots (SCR) have the potential to replace these devices, providing high dexterity together with the ability to conform to complex environments and safe human interactions without the cognitive burden for the clinician. Despite considerable progress in the past decade in their development, several challenges still plague SCR hindering their full realisation. This thesis aims at improving magnetically actuated SCR by addressing some of these challenges, such as material characterisation and modelling, and sensing feedback and localisation.
Material characterisation for SCR is essential for understanding their behaviour and designing effective modelling and simulation strategies. In this work, the material properties of commonly employed materials in magnetically actuated SCR, such as elastic modulus, hyper-elastic model parameters, and magnetic moment were determined. Additionally, the effect these parameters have on modelling and simulating these devices was investigated.
Due to the nature of magnetic actuation, localisation is of utmost importance to ensure accurate control and delivery of functionality. As such, two localisation strategies for magnetically actuated SCR were developed, one capable of estimating the full 6 degrees of freedom (DOFs) pose without any prior pose information, and another capable of accurately tracking the full 6-DOFs in real-time with positional errors lower than 4~mm. These will contribute to the development of autonomous navigation and closed-loop control of magnetically actuated SCR
Gaussian Control Barrier Functions : A Gaussian Process based Approach to Safety for Robots
In recent years, the need for safety of autonomous and intelligent robots has increased. Today, as robots are being increasingly deployed in closer proximity to humans, there is an exigency for safety since human lives may be at risk, e.g., self-driving vehicles or surgical robots. The objective of this thesis is to present a safety framework for dynamical systems that leverages tools from control theory and machine learning. More formally, the thesis presents a data-driven framework for designing safety function candidates which ensure properties of forward invariance. The potential benefits of the results presented in this thesis are expected to help applications such as safe exploration, collision avoidance problems, manipulation tasks, and planning, to name some.
We utilize Gaussian processes (GP) to place a prior on the desired safety function candidate, which is to be utilized as a control barrier function (CBF). The resultant formulation is called Gaussian CBFs and they reside in a reproducing kernel Hilbert space. A key concept behind Gaussian CBFs is the incorporation of both safety belief as well as safety uncertainty, which former barrier function formulations did not consider. This is achieved by using robust posterior estimates from a GP where the posterior mean and variance serve as surrogates for the safety belief and uncertainty respectively. We synthesize safe controllers by framing a convex optimization problem where the kernel-based representation of GPs allows computing the derivatives in closed-form analytically.
Finally, in addition to the theoretical and algorithmic frameworks in this thesis, we rigorously test our methods in hardware on a quadrotor platform. The platform used is a Crazyflie 2.1 which is a versatile palm-sized quadrotor. We provide our insights and detailed discussions on the hardware implementations which will be useful for large-scale deployment of the techniques presented in this dissertation.Ph.D
Application of Elliptic Jerk Motion Profile to Cartesian Space Position Control of a Serial Robot
The paper discusses the application of a motion profile with an elliptic jerk to Cartesian space position control of serial robots. This motion profile is obtained by means of a kinematic approach, starting from the jerk profile and then calculating acceleration, velocity and position by successive integrations. Until now, this profile has been compared to other motion laws (trapezoidal velocity, trapezoidal acceleration, cycloidal, sinusoidal jerk, modified sinusoidal jerk) considering single-input single-output systems. In this work, the comparison is extended to nonlinear multi-input
multi-output systems, investigating the application to Cartesian space position control of serial robots. As case study, a 4-DOF SCARA-like architecture with elastic balancing is considered; both an integer-order and a fractional-order controller are applied. Multibody simulation results show that, independently of the controller, the behavior of the robot using the elliptic jerk profile is similar to the case of adopting the sinusoidal jerk and modified sinusoidal jerk laws, but with a slight reduction in the position error (â3.8% with respect to the sinusoidal jerk law and â0.8% with respect to the modified sinusoidal jerk law in terms of Integral Square Error) and of the control effort (â8.2% with respect to the sinusoidal jerk law and â1.3% with respect to the modified sinusoidal jerk law in terms of Integral Control Effort)
Set-based state estimation and fault diagnosis using constrained zonotopes and applications
This doctoral thesis develops new methods for set-based state estimation and
active fault diagnosis (AFD) of (i) nonlinear discrete-time systems, (ii)
discrete-time nonlinear systems whose trajectories satisfy nonlinear equality
constraints (called invariants), (iii) linear descriptor systems, and (iv)
joint state and parameter estimation of nonlinear descriptor systems. Set-based
estimation aims to compute tight enclosures of the possible system states in
each time step subject to unknown-but-bounded uncertainties. To address this
issue, the present doctoral thesis proposes new methods for efficiently
propagating constrained zonotopes (CZs) through nonlinear mappings. Besides,
this thesis improves the standard prediction-update framework for systems with
invariants using new algorithms for refining CZs based on nonlinear
constraints. In addition, this thesis introduces a new approach for set-based
AFD of a class of nonlinear discrete-time systems. An affine parametrization of
the reachable sets is obtained for the design of an optimal input for set-based
AFD. In addition, this thesis presents new methods based on CZs for set-valued
state estimation and AFD of linear descriptor systems. Linear static
constraints on the state variables can be directly incorporated into CZs.
Moreover, this thesis proposes a new representation for unbounded sets based on
zonotopes, which allows to develop methods for state estimation and AFD also of
unstable linear descriptor systems, without the knowledge of an enclosure of
all the trajectories of the system. This thesis also develops a new method for
set-based joint state and parameter estimation of nonlinear descriptor systems
using CZs in a unified framework. Lastly, this manuscript applies the proposed
set-based state estimation and AFD methods using CZs to unmanned aerial
vehicles, water distribution networks, and a lithium-ion cell.Comment: My PhD Thesis from Federal University of Minas Gerais, Brazil. Most
of the research work has already been published in DOIs
10.1109/CDC.2018.8618678, 10.23919/ECC.2018.8550353,
10.1016/j.automatica.2019.108614, 10.1016/j.ifacol.2020.12.2484,
10.1016/j.ifacol.2021.08.308, 10.1016/j.automatica.2021.109638,
10.1109/TCST.2021.3130534, 10.1016/j.automatica.2022.11042
Evaluating EEGâEMG Fusion-Based Classification as a Method for Improving Control of Wearable Robotic Devices for Upper-Limb Rehabilitation
Musculoskeletal disorders are the biggest cause of disability worldwide, and wearable mechatronic rehabilitation devices have been proposed for treatment. However, before widespread adoption, improvements in user control and system adaptability are required. User intention should be detected intuitively, and user-induced changes in system dynamics should be unobtrusively identified and corrected. Developments often focus on model-dependent nonlinear control theory, which is challenging to implement for wearable devices.
One alternative is to incorporate bioelectrical signal-based machine learning into the system, allowing for simpler controller designs to be augmented by supplemental brain (electroencephalography/EEG) and muscle (electromyography/EMG) information. To extract user intention better, sensor fusion techniques have been proposed to combine EEG and EMG; however, further development is required to enhance the capabilities of EEGâEMG fusion beyond basic motion classification. To this end, the goals of this thesis were to investigate expanded methods of EEGâEMG fusion and to develop a novel control system based on the incorporation of EEGâEMG fusion classifiers.
A dataset of EEG and EMG signals were collected during dynamic elbow flexionâextension motions and used to develop EEGâEMG fusion models to classify task weight, as well as motion intention. A variety of fusion methods were investigated, such as a Weighted Average decision-level fusion (83.01 ± 6.04% accuracy) and Convolutional Neural Network-based input-level fusion (81.57 ± 7.11% accuracy), demonstrating that EEGâEMG fusion can classify more indirect tasks.
A novel control system, referred to as a Task Weight Selective Controller (TWSC), was implemented using a Gain Scheduling-based approach, dictated by external load estimations from an EEGâEMG fusion classifier. To improve system stability, classifier prediction debouncing was also proposed to reduce misclassifications through filtering. Performance of the TWSC was evaluated using a developed upper-limb brace simulator. Due to simulator limitations, no significant difference in error was observed between the TWSC and PID control. However, results did demonstrate the feasibility of prediction debouncing, showing it provided smoother device motion. Continued development of the TWSC, and EEGâEMG fusion techniques will ultimately result in wearable devices that are able to adapt to changing loads more effectively, serving to improve the user experience during operation
Improving Automated Operations of Heavy-Duty Manipulators with Modular Model-Based Control Design
The rapid development of robotization and automation in mobile working machines aims to increase productivity and safety in many industrial sectors. In heavy-duty applications, hydraulically actuated manipulators are the common solution due to their large power-to-weight ratio. As hydraulic systems can exhibit nonlinear dynamic behavior, automated operations with closed-loop control become challenging. In industrial applications, the dexterity of operations for manipulators is ensured by providing interfaces to equip product variants with diïŹerent tool attachments. By considering these domain-speciïŹc tool attachments for heavy-duty hydraulic manipulators (HHMs), the autonomous robotic operating development for all product variants might be a time-consuming process.
This thesis aims to develop a modular nonlinear model-based (NMB) control method for HHMs to enable systematic NMB model reuse and control system modularity across diïŹerent HHM product variants with actuators and tool attachments. Equally importantly, the properties of NMB control are used to improve the high-performance control for multi degrees-of-freedom robotic HHMs, as rigorously stability-guaranteed control systems have been shown to provide superior performance. To achieve these objectives, four research problems (RPs) on HHM controls are addressed. The RPs are focused on damping control methods in underactuated tool attachments, compensating for static actuator nonlinearities, and, equally signiïŹcantly, improving overall control performance. The fourth RP is introduced for hydraulic series elastic actuators (HSEAs) in HHM applications, which can be regarded as supplementing NMB control with the aim of improving force controllability.
Six publications are presented to investigate the RPs in this thesis. The control development focus was on modular NMB control design for HHMs equipped with diïŹerent actuators and tool attachments consisting of passive and actuated joints. The designed control methods were demonstrated on a full-size HHM and a novel HSEA concept in a heavy-duty experimental setup. The results veriïŹed that modular control design for HHM systems can be used to decrease the modiïŹcations required to use the manipulator with diïŹerent tool attachments and ïŹoating-base environments
Development of Motion Control Systems for Hydraulically Actuated Cranes with Hanging Loads
Automation has been used in industrial processes for several decades to increase efficiency and safety. Tasks that are either dull, dangerous, or dirty can often be performed by machines in a reliable manner. This may provide a reduced risk to human life, and will typically give a lower economic cost. Industrial robots are a prime example of this, and have seen extensive use in the automotive industry and manufacturing plants. While these machines have been employed in a wide variety of industries, heavy duty lifting and handling equipment such as hydraulic cranes have typically been manually operated. This provides an opportunity to investigate and develop control systems to push lifting equipment towards the same level of automation found in the aforementioned industries. The use of winches and hanging loads on cranes give a set of challenges not typically found on robots, which requires careful consideration of both the safety aspect and precision of the pendulum-like motion. Another difference from industrial robots is the type of actuation systems used. While robots use electric motors, the cranes discussed in this thesis use hydraulic cylinders. As such, the dynamics of the machines and the control system designmay differ significantly. In addition, hydraulic cranes may experience significant deflection when lifting heavy loads, arising from both structural flexibility and the compressibility of the hydraulic fluid.
The work presented in this thesis focuses on motion control of hydraulically actuated cranes. Motion control is an important topic when developing automation systems, as moving from one position to another is a common requirement for automated lifting operations. A novel path controller operating in actuator space is developed, which takes advantage of the load-independent flow control valves typically found on hydraulically actuated cranes. By operating in actuator space the motion of each cylinder is inherently minimized. To counteract the pendulum-like motion of the hanging payload, a novel anti-swing controller is developed and experimentally verified. The anti-swing controller is able to suppress the motion from the hanging load to increase safety and precision. To tackle the challenges associated with the flexibility of the crane, a deflection compensator is developed and experimentally verified. The deflection compensator is able to counteract both the static deflection due to gravity and dynamic de ection due to motion. Further, the topic of adaptive feedforward control of pressure compensated cylinders has been investigated.
A novel adaptive differential controller has been developed and experimentally verified, which adapts to system uncertainties in both directions of motion. Finally, the use of electro-hydrostatic actuators for motion control of cranes has been investigated using numerical time domain simulations. A novel concept is proposed and investigated using simulations.publishedVersio
Engineering for a changing world: 60th Ilmenau Scientific Colloquium, Technische UniversitÀt Ilmenau, September 04-08, 2023 : programme
In 2023, the Ilmenau Scientific Colloquium is once more organised by the Department of Mechanical Engineering. The title of this yearâs conference âEngineering for a Changing Worldâ refers to limited natural resources of our planet, to massive changes in cooperation between continents, countries, institutions and people â enabled by the increased implementation of information technology as the probably most dominant driver in many fields. The Colloquium, supplemented by workshops, is characterised but not limited to the following topics: â Precision engineering and measurement technology Nanofabrication â Industry 4.0 and digitalisation in mechanical engineering â Mechatronics, biomechatronics and mechanism technology â Systems engineering â Productive teaming - Human-machine collaboration in the production environment The topics are oriented on key strategic aspects of research and teaching in Mechanical Engineering at our university
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