1,583 research outputs found
Robot skill learning system of multi-space fusion based on dynamic movement primitives and adaptive neural network control
This article develops a robot skill learning system with multi-space fusion, simultaneously considering motion/stiffness generation and trajectory tracking. To begin with, surface electromyography (sEMG) signals from the human arm is captured based on the MYO armband to estimate endpoint stiffness. Gaussian Process Regression (GPR) is combined with dynamic movement primitive (DMP) to extract more skills features from multi-demonstrations. Then, the traditional DMP formulation is improved based on the Riemannian metric to encode the robot's quaternions with non-Euclidean properties. Furthermore, an adaptive neural network (NN)-based finite-time admittance controller is designed to track the trajectory generated by the motion model and to reflect the learned stiffness characteristics. In this controller, a radial basis function neural network (RBFNN) is employed to compensate for the uncertainty of the robot dynamics. Finally, experimental validation is conducted using the ROKAE collaborative robot, confirming the effectiveness of the proposed approach. In summary, the presented framework is suitable for human-robot skill transfer method that require simultaneous consideration of position and stiffness in Euclidean space, as well as orientation on Riemannian manifolds
Design and Quantitative Assessment of Teleoperation-Based Human–Robot Collaboration Method for Robot-Assisted Sonography
Tele-echography has emerged as a promising and effective solution, leveraging the expertise of sonographers and the autonomy of robots to perform ultrasound scanning for patients residing in remote areas, without the need for in-person visits by the sonographer. Designing effective and natural human-robot interfaces for tele-echography remains challenging, with patient safety being a critical concern. In this article, we develop a teleoperation system for robot-assisted sonography with two different interfaces, a haptic device-based interface and a low-cost 3D Mouse-based interface, which can achieve continuous and intuitive telemanipulation by a leader device with a small workspace. To achieve compliant interaction with patients, we design impedance controllers in Cartesian space to track the desired position and orientation for these two teleoperation interfaces. We also propose comprehensive evaluation metrics of robot-assisted sonography, including subjective and objective evaluation, to evaluate tele-echography interfaces and control performance. We evaluate the ergonomic performance based on the estimated muscle fatigue and the acquired ultrasound image quality. We conduct user studies based on the NASA Task Load Index to evaluate the performance of these two human-robot interfaces. The tracking performance and the quantitative comparison of these two teleoperation interfaces are conducted by the Franka Emika Panda robot. The results and findings provide guidance on human-robot collaboration design and implementation for robot-assisted sonography. Note to Practitioners —Robot-assisted sonography has demonstrated efficacy in medical diagnosis during clinical trials. However, deploying fully autonomous robots for ultrasound scanning remains challenging due to various constraints in practice, such as patient safety, dynamic tasks, and environmental uncertainties. Semi-autonomous or teleoperation-based robot sonography represents a promising approach for practical deployment. Previous work has produced various expensive teleoperation interfaces but lacks user studies to guide teleoperation interface selection. In this article, we present two typical teleoperation interfaces and implement a continuous and intuitive teleoperation control system. We also propose a comprehensive evaluation metric for assessing their performance. Our findings show that the haptic device outperforms the 3D Mouse, based on operators’ feedback and acquired image quality. However, the haptic device requires more learning time and effort in the training stage. Furthermore, the developed teleoperation system offers a solution for shared control and human-robot skill transfer. Our results provide valuable guidance for designing and implementing human-robot interfaces for robot-assisted sonography in practice
Application of reinforcement learning in robotic disassembly operations
Disassembly is a key step in remanufacturing. To increase the level of automation in disassembly, it is necessary to use robots that can learn to perform new tasks by themselves rather than having to be manually reprogrammed every time there is a different job. Reinforcement Learning (RL) is a machine learning technique that enables the robots to learn by trial and error rather than being explicitly programmed.
In this thesis, the application of RL to robotic disassembly operations has been studied. Firstly, a literature review on robotic disassembly and the application of RL in contact-rich tasks has been conducted in Chapter 2.
To physically implement RL in robotic disassembly, the task of removing a bolt from a door chain lock has been selected as a case study, and a robotic training platform has been built for this implementation in Chapter 3. This task is chosen because it can demonstrate the capabilities of RL to pathfinding and dealing with reaction forces without explicitly specifying the target coordinates or building a force feedback controller. The robustness of the learned policies against the imprecision of the robot is studied by a proposed method to actively lower the precision of the robots. It has been found that the robot can learn successfully even when the precision is lowered to as low as ±0.5mm. This work also investigates whether learned policies can be transferred among robots with different precisions. Experiments have been performed by training a robot with a certain precision on a task and replaying the learned skills on a robot with different precision. It has been found that skills learned by a low-precision robot can perform better on a robot with higher precision, and skills learned by a high-precision robot have worse performance on robots with lower precision, as it is suspected that the policies trained on high-precision robots have been overfitted to the precise robots.
In Chapter 4, the approach of using a digital-twin-assisted simulation-to-reality transfer to accelerate the learning performance of the RL has been investigated. To address the issue of identifying the system parameters, such as the stiffness and damping of the contact models, that are difficult to measure directly but are critical for building the digital twins of the environments, system identification method is used to minimise the discrepancy between the response generated from the physical and digital environments by using the Bees Algorithm. It is found that the proposed method effectively increases RL's learning performance. It is also found that it is possible to have worse performance with the sim-to-real transfer if the reality gap is not effectively addressed. However, increasing the size of the dataset and optimisation cycles have been demonstrated to reduce the reality gap and lead to successful sim-to-real transfers.
Based on the training task described in Chapters 4 and 5, a full factorial study has been conducted to identify patterns when selecting the appropriate hyper-parameters when applying the Deep Deterministic Policy Gradient (DDPG) algorithm to the robotic disassembly task. Four hyper-parameters that directly influence the decision-making Artificial Neural Network (ANN) update have been chosen for the study, with three levels assigned to each hyper-parameter. After running 241 simulations, it is found that for this particular task, the learning rates of the actor and critic networks are the most influential hyper-parameters, while the batch size and soft update rate have relatively limited influence.
Finally, the thesis is concluded in Chapter 6 with a summary of findings and suggested future research directions
Model learning for trajectory tracking of robot manipulators
Abstract
Model based controllers have drastically improved robot performance, increasing task accuracy while reducing control effort. Nevertheless, all this was realized with a very strong assumption: the exact knowledge of the physical properties of both the robot and the environment that surrounds it. This assertion is often misleading: in fact modern robots are modeled in a very approximate way and, more important, the environment is almost never static and completely known. Also for systems very simple, such as robot manipulators, these assumptions are still too strong and must be relaxed. Many methods were developed which, exploiting previous experiences, are able to refine the nominal model: from classic identification techniques to more modern machine learning based approaches. Indeed, the topic of this thesis is the investigation of these data driven techniques in the context of robot control for trajectory tracking. In the first two chapters, preliminary knowledge is provided on both model based controllers, used in robotics to assure precise trajectory tracking, and model learning techniques. In the following three chapters, are presented the novelties introduced by the author in this context with respect to the state of the art: three works with the same premise (an inaccurate system modeling), an identical goal (accurate trajectory tracking control) but with small differences according to the specific platform of application (fully actuated, underactuated, redundant robots). In all the considered architectures, an online learning scheme has been introduced to correct the nominal feedback linearization control law. Indeed, the method has been primarily introduced in the literature to cope with fully actuated systems, showing its efficacy in the accurate tracking of joint space trajectories also with an inaccurate dynamic model. The main novelty of the technique was the use of only
kinematics information, instead of torque measurements (in general very noisy), to online retrieve and compensate the dynamic mismatches. After that the method has
been extended to underactuated robots. This new architecture was composed by an online learning correction of the controller, acting on the actuated part of the system
(the nominal partial feedback linearization), and an offline planning phase, required to realize a dynamically feasible trajectory also for the zero dynamics of the system.
The scheme was iterative: after each trial, according to the collected information, both the phases were improved and then repeated until the task achievement. Also in this case the method showed its capability, both in numerical simulations and on real experiments on a robotics platform. Eventually the method has been applied to redundant systems: differently from before, in this context the task consisted in the accurate tracking of a Cartesian end effector trajectory. In principle very similar to the fully actuated case, the presence of redundancy slowed down drastically the learning machinery convergence, worsening the performance. In order to cope with this, a redundancy resolution was proposed that, exploiting an approximation of the learning algorithm (Gaussian process regression), allowed to locally maximize the information and so select the most convenient self motion for the system; moreover, all of this was realized with just the resolution of a quadratic programming problem. Also in this case the method showed its performance, realizing an accurate online tracking while reducing both the control effort and the joints velocity, obtaining so a natural behaviour. The thesis concludes with summary considerations on the proposed approach and with possible future directions of research
ABC: Adaptive, Biomimetic, Configurable Robots for Smart Farms - From Cereal Phenotyping to Soft Fruit Harvesting
Currently, numerous factors, such as demographics, migration patterns, and economics, are leading to the critical labour shortage in low-skilled and physically demanding parts of agriculture. Thus, robotics can be developed for the agricultural sector to address these shortages. This study aims to develop an adaptive, biomimetic, and configurable modular robotics architecture that can be applied to multiple tasks (e.g., phenotyping, cutting, and picking), various crop varieties (e.g., wheat, strawberry, and tomato) and growing conditions. These robotic solutions cover the entire perception–action–decision-making loop targeting the phenotyping of cereals and harvesting fruits in a natural environment.
The primary contributions of this thesis are as follows. a) A high-throughput method for imaging field-grown wheat in three dimensions, along with an accompanying unsupervised measuring method for obtaining individual wheat spike data are presented. The unsupervised method analyses the 3D point cloud of each trial plot, containing hundreds of wheat spikes, and calculates the average size of the wheat spike and total spike volume per plot. Experimental results reveal that the proposed algorithm can effectively identify spikes from wheat crops and individual spikes. b) Unlike cereal, soft fruit is typically harvested by manual selection and picking. To enable robotic harvesting, the initial perception system uses conditional generative adversarial networks to identify ripe fruits using synthetic data. To determine whether the strawberry is surrounded by obstacles, a cluster complexity-based perception system is further developed to classify the harvesting complexity of ripe strawberries. c) Once the harvest-ready fruit is localised using point cloud data generated by a stereo camera, the platform’s action system can coordinate the arm to reach/cut the stem using the passive motion paradigm framework, as inspired by studies on neural control of movement in the brain. Results from field trials for strawberry detection, reaching/cutting the stem of the fruit with a mean error of less than 3 mm, and extension to analysing complex canopy structures/bimanual coordination (searching/picking) are presented.
Although this thesis focuses on strawberry harvesting, ongoing research is heading toward adapting the architecture to other crops. The agricultural food industry remains a labour-intensive sector with a low margin, and cost- and time-efficiency business model. The concepts presented herein can serve as a reference for future agricultural robots that are adaptive, biomimetic, and configurable
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 (−3.8% with respect to the sinusoidal jerk law and −0.8% with respect to the modified sinusoidal jerk law in terms of Integral Square Error) and of the control effort (−8.2% with respect to the sinusoidal jerk law and −1.3% 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
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