965 research outputs found
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
Kinetic energy fluctuation-driven locomotor transitions on potential energy landscapes of beam obstacle traversal and self-righting
Despite contending with constraints imposed by the environment, morphology,
and physiology, animals move well by physically interactingwith the environment
to use and transition between modes such as running, climbing, and
self-righting. By contrast, robots struggle to do so in real world.
Understanding the principles of how locomotor transitions emerge from
constrained physical interaction is necessary for robots to move robustly using
similar strategies. Recent studies discovered that discoid cockroaches use and
transition between diverse locomotor modes to traverse beams and self-right on
ground. For both systems, animals probabilistically transitioned between modes
via multiple pathways, while its self-propulsion created kinetic energy
fluctuation. Here, we seek mechanistic explanations for these observations by
adopting a physics-based approach that integrates biological and robotic
studies.
We discovered that animal and robot locomotor transitions during beam
obstacle traversal and ground self-righting are barrier-crossing transitions on
potential energy landscapes. Whereas animals and robot traversed stiff beams by
rolling their body betweenbeam, they pushed across flimsy beams, suggesting a
concept of terradynamic favorability where modes with easier physical
interaction are more likely to occur. Robotic beam traversal revealed that,
system state either remains in a favorable mode or transitions to one when
energy fluctuation is comparable to the transition barrier. Robotic
self-righting transitions occurred similarly and revealed that changing system
parameters lowers barriers over which comparable fluctuation can induce
transitions. Thetransitionsof animalsin both systems mostly occurred similarly,
but sensory feedback may facilitate its beam traversal. Finally, we developed a
method to measure animal movement across large spatiotemporal scales in a
terrain treadmill.Comment: arXiv admin note: substantial text overlap with arXiv:2006.1271
Extension of the Control Concept for a Mobile Overhead Manipulator to Whole-Body Impedance Control
At present, robots constitute a central component of contemporary factories. The application of traditional ground-based systems, however, may lead to congested floors with minimal space left for new robots or human workers. Overhead manipulators, on the other hand, aim to occupy the unutilized ceiling space, in order to manipulate the workspace located below them. The SwarmRail system is an example of such an overhead manipulator. This concept deploys mobile units driving across a passive railstructure above the ground. Additionally, equipping the mobile units with robotic arms at their bottom side enables this design to provide continuous overhead manipulation while in motion. Although a first demonstrator confirmed the functional capability of said system, the current hardware suffers from complications while traversing rail crossings. Due to uneven rails consecutive rails, said crossing points cause the robot's wheels to collide with the new rail segment it is driving towards. Additionally, the robot experiences an undesired sudden altitude change.
In this thesis, we aim to implement a hierarchical whole-body impedance tracking controller for the robots employed within the SwarmRail system. Our controller combines a kinematically controlled mobile unit with the impedance-based control of a robotic arm through an admittance interface. The focus of this thesis is set on the controller's robustness against the previously mentioned external disturbances. The performance of this controller is validated inside a simulation that incorporates the aforementioned complications. Our findings suggest, that the control strategy presented in this thesis provides a foundation for the development of a controller applicable to the physical demonstrator
Advanced Bionic Attachment Equipment Inspired by the Attachment Performance of Aquatic Organisms: A Review
In nature, aquatic organisms have evolved various attachment systems, and their attachment ability has become a specific and mysterious survival skill for them. Therefore, it is significant to study and use their unique attachment surfaces and outstanding attachment characteristics for reference and develop new attachment equipment with excellent performance. Based on this, in this review, the unique non-smooth surface morphologies of their suction cups are classified and the key roles of these special surface morphologies in the attachment process are introduced in detail. The recent research on the attachment capacity of aquatic suction cups and other related attachment studies are described. Emphatically, the research progress of advanced bionic attachment equipment and technology in recent years, including attachment robots, flexible grasping manipulators, suction cup accessories, micro-suction cup patches, etc., is summarized. Finally, the existing problems and challenges in the field of biomimetic attachment are analyzed, and the focus and direction of biomimetic attachment research in the future are pointed out
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
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
Machine Learning Meets Advanced Robotic Manipulation
Automated industries lead to high quality production, lower manufacturing
cost and better utilization of human resources. Robotic manipulator arms have
major role in the automation process. However, for complex manipulation tasks,
hard coding efficient and safe trajectories is challenging and time consuming.
Machine learning methods have the potential to learn such controllers based on
expert demonstrations. Despite promising advances, better approaches must be
developed to improve safety, reliability, and efficiency of ML methods in both
training and deployment phases. This survey aims to review cutting edge
technologies and recent trends on ML methods applied to real-world manipulation
tasks. After reviewing the related background on ML, the rest of the paper is
devoted to ML applications in different domains such as industry, healthcare,
agriculture, space, military, and search and rescue. The paper is closed with
important research directions for future works
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On the Creation and Use of Forward Models in Robot Motor Control
Advancements in robotics have the potential to aid humans in many realms of exploration as well as daily life: from search and rescue work, to space and deep sea exploration, to in-home assistance to improve the quality of life for those with limited mobility. One of the main milestones that needs to be met for robotics to achieve these ends is a robust ability to manipulate objects and locomote in cluttered and changing environments. A prerequisite to these skills is the ability to understand the current state of the world as well as how actions result in changes to the environment; in short, a robot needs a way to model itself and the world around it. With recent advances in machine learning and access to cheap and fast computation, one of the most promising avenues for creating robust models is to learn a neural network to approximate the dynamics of the system.
Learning a data-driven model that accurately replicates the dynamics of a robot and its environment is an active area of robotics research. This model needs to be accurate, it needs to operate using sensors that are often high dimensional, and it needs to be robust to changes within the system and the surrounding environment. In this thesis, we investigate ways to improve the processof learning data-driven dynamics models as well as ways to reduce the dimensionality of a robot’s state space.
We start by trying to improve the long-term accuracy of neural network based forward models. Learning forward models is more complicated than it appears on the surface. While it is easy to learn a model to predict the change of a system over a short horizon, it is challenging to assure this performance over a long horizon. We investigate the concept of adding temporal information into the loss function of the forward model during training; we demonstrate that this improves the accuracy of a model when it is used to predict over long horizons.
While we are currently working with low dimensional systems, we eventually want to apply our learned models to robots with high dimensional state spaces. To make learning feasible, we need to find ways to learn a lower dimensional representation of the state space (also known as a latent space) to make learning models in the real world computationally feasible. We present a method to improve the usefulness of a learned latent space using a method we call context training: we learn a latent space alongside a forward model to encourage the learned latent space to retain the variables critical to learning the dynamics of the system.
In all of our experiments, we spend significant time in analysis and evaluation. A large portion of literature demonstrating the effectiveness of data-driven forward models in robot control settings often only presents the final controller performance. We were often left curious about what the model was learning independent of the control scenario. We set out to do our own deep dive into exactly what data-driven forward models are predicting. We evaluate all of our models over long horizons. We also look deeper than just the mean and median loss values. We plot the full distribution of loss values over the entire horizon. The literature on data-driven models that do evaluate model prediction accuracy often focuses on the mean and median prediction errors; while these are important metrics, we found that looking at these metrics alone can sometimes obscure subtle but important effects. A high mean loss is often a result of poor performance on only a subset of the test dataset; one model can outperform other models with lower mean error values on a majority of the test set, but it can be skewed to look like the worst performer by having a few highly inaccurate outliers.
We observe that models often have a subset of a test dataset on which they perform best; we seek to limit the use of a model to regions of the test dataset where it has high accuracy by using an ensemble of models. We find that if we train an ensemble of forward models, the accuracy of the models is higher when they all agree on a prediction. Conversely, when the ensemble of models disagrees, the prediction is often poor. We explore this relationship and propose future ways to apply it.
Finally, we look into the application of improved model accuracy and context trained latent spaces. We start by testing the performance of our context training architecture as a method to reduce the state space dimensionality in a model-free reinforcement learning (MFRL) reaching task. We hypothesize that a policy trained with a latent space observation derived using our context trained encoder will outperform a policy trained with a latent space observation derived from a standard autoencoder. Unfortunately, we found no difference in task performance between the policies learned using either method. We end on a bright note by looking at the power of model-based control when we have access to an accurate model. We successfully use model predictive control (MPC) to generate robust locomotion for a simulated snake robot. With access to an accurate model, we are able to generate realistic snake gaits in a variety of environments with very little parameter tuning that are robust to changes in the environment
Learning-Based Control Strategies for Soft Robots: Theory, Achievements, and Future Challenges
In the last few decades, soft robotics technologies have challenged conventional approaches by introducing new, compliant bodies to the world of rigid robots. These technologies and systems may enable a wide range of applications, including human-robot interaction and dealing with complex environments. Soft bodies can adapt their shape to contact surfaces, distribute stress over a larger area, and increase the contact surface area, thus reducing impact forces
Advanced medical micro-robotics for early diagnosis and therapeutic interventions
Recent technological advances in micro-robotics have demonstrated their immense potential for biomedical applications. Emerging micro-robots have versatile sensing systems, flexible locomotion and dexterous manipulation capabilities that can significantly contribute to the healthcare system. Despite the appreciated and tangible benefits of medical micro-robotics, many challenges still remain. Here, we review the major challenges, current trends and significant achievements for developing versatile and intelligent micro-robotics with a focus on applications in early diagnosis and therapeutic interventions. We also consider some recent emerging micro-robotic technologies that employ synthetic biology to support a new generation of living micro-robots. We expect to inspire future development of micro-robots toward clinical translation by identifying the roadblocks that need to be overcome
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