112 research outputs found
Thermodynamics of Thermoplastic Polymers and their Solutions
Glassy organic polymers are technologically important across the gamut of materials
applications from structural (hyperbaric windows) to electronic (ionic conductors,
surface coatings for printed circuit boards) to environmental (membranes for industrial
gas separation). A formal description and understanding of the glass transition
temperature is necessary in order to determine the configurational state and hence
physical properties of the glass. Moreover, the non-equilibrium glassy state appears
to be unstable: volume-relaxation studies of glassy materials have revealed that they
undergo slow processes, which attempt to establish equilibrium. These types of retardation/
relaxation phenomena are called physical ageing. As well as pressure and
temperature, sorption of a plasticizer may affect in several ways the membrane physical
properties. Generally speaking structural rearrangement of the chains is enhanced
and, consequently, the glass transition temperature decreases, physical ageing is usually
speed up, the membrane is affected by swelling and/or plasticization and even crystallization
can be activated.
The research work focuses on the investigation of industrial polymers’ glassy
– rubbery behaviour due to thermodynamic state variables change (e.g. temperature
T, mechanical pressure P and solvent content
) within the polymer matrix. The goal
is to obtain a fundamental insight of the sorption process on both macroscopic and
microscopic levels. As a result several polymer—penetrant systems have been studied.
Different techniques have been implemented to achieve this goal: dilatometry,
MTDSC, gravimetry, manometry and in situ FTIR. The instruments used are: a PVT
apparatus from GNOMIX®; a MDSC from TA Instruments®; four different handmade
systems consisting of a CAHN microbalance from Thermo Fisher Scientific®, a QSM
from RUSKA Co.®, a pressure decay system from MKS® and finally a FTIR from
Perkin-Elmer®. All data have been modelled with statistical thermodynamic theories
and empirical approaches .
The study is divided as follows: the first chapter introduces the research goal and
fields of application along with the theoretical background for membrane science; the
second chapter reports the study conducted on the system PEI—CO2; the third chapter
describes the results obtained on the PS—Toluene system; finally in the fourth chapter
the results for the PPO—benzene system are given. The order in which these systems
are presented is related to the increase of structural modifications as a result of polymer—
penetrant interactions
Bearing-only formation control with auxiliary distance measurements, leaders, and collision avoidance
We address the controller synthesis problem for distributed formation control. Our solution requires only relative bearing measurements (as opposed to full translations), and is based on the exact gradient of a Lyapunov function with only global minimizers (independently from the formation topology). These properties allow a simple proof of global asymptotic convergence, and extensions for including distance measurements, leaders and collision avoidance. We validate our approach through simulations and comparison with other stateof-the-art algorithms.ARL grant W911NF-08-2-0004, ARO grant W911NF-13-1-0350, ONR grants N00014-07-1-0829, N00014-14-1-0510, N00014-15-1-2115, NSF grant IIS-1426840, CNS-1521617 and United Technologies
A distributed optimization framework for localization and formation control: applications to vision-based measurements
Multiagent systems have been a major area of research for the last 15 years. This interest has been motivated by tasks that can be executed more rapidly in a collaborative manner or that are nearly impossible to carry out otherwise. To be effective, the agents need to have the notion of a common goal shared by the entire network (for instance, a desired formation) and individual control laws to realize the goal. The common goal is typically centralized, in the sense that it involves the state of all the agents at the same time. On the other hand, it is often desirable to have individual control laws that are distributed, in the sense that the desired action of an agent depends only on the measurements and states available at the node and at a small number of neighbors. This is an attractive quality because it implies an overall system that is modular and intrinsically more robust to communication delays and node failures
Geometric Fault-Tolerant Control of Quadrotors in Case of Rotor Failures: An Attitude Based Comparative Study
The ability of aerial robots to operate in the presence of failures is
crucial in various applications that demand continuous operations, such as
surveillance, monitoring, and inspection. In this paper, we propose a
fault-tolerant control strategy for quadrotors that can adapt to single and
dual complete rotor failures. Our approach augments a classic geometric
tracking controller on to accommodate the effects of
rotor failures. We provide an in-depth analysis of several attitude error
metrics to identify the most appropriate design choice for fault-tolerant
control strategies. To assess the effectiveness of these metrics, we evaluate
trajectory tracking accuracies. Simulation results demonstrate the performance
of the proposed approach.Comment: Accepted for publication in IROS 202
Visual Geo-localization with Self-supervised Representation Learning
Visual Geo-localization (VG) has emerged as a significant research area,
aiming to identify geolocation based on visual features. Most VG approaches use
learnable feature extractors for representation learning. Recently,
Self-Supervised Learning (SSL) methods have also demonstrated comparable
performance to supervised methods by using numerous unlabeled images for
representation learning. In this work, we present a novel unified VG-SSL
framework with the goal to enhance performance and training efficiency on a
large VG dataset by SSL methods. Our work incorporates multiple SSL methods
tailored for VG: SimCLR, MoCov2, BYOL, SimSiam, Barlow Twins, and VICReg. We
systematically analyze the performance of different training strategies and
study the optimal parameter settings for the adaptation of SSL methods for the
VG task. The results demonstrate that our method, without the significant
computation and memory usage associated with Hard Negative Mining (HNM), can
match or even surpass the VG performance of the baseline that employs HNM. The
code is available at https://github.com/arplaboratory/VG_SSL.Comment: 15 pages (including appendix, references), 2 figures, 9 tables (5
tables in appendix
BackpropTools: A Fast, Portable Deep Reinforcement Learning Library for Continuous Control
Deep Reinforcement Learning (RL) has been demonstrated to yield capable
agents and control policies in several domains but is commonly plagued by
prohibitively long training times. Additionally, in the case of continuous
control problems, the applicability of learned policies on real-world embedded
devices is limited due to the lack of real-time guarantees and portability of
existing deep learning libraries. To address these challenges, we present
BackpropTools, a dependency-free, header-only, pure C++ library for deep
supervised and reinforcement learning. Leveraging the template meta-programming
capabilities of recent C++ standards, we provide composable components that can
be tightly integrated by the compiler. Its novel architecture allows
BackpropTools to be used seamlessly on a heterogeneous set of platforms, from
HPC clusters over workstations and laptops to smartphones, smartwatches, and
microcontrollers. Specifically, due to the tight integration of the RL
algorithms with simulation environments, BackpropTools can solve popular RL
problems like the Pendulum-v1 swing-up about 7 to 15 times faster in terms of
wall-clock training time compared to other popular RL frameworks when using
TD3. We also provide a low-overhead and parallelized interface to the MuJoCo
simulator, showing that our PPO implementation achieves state of the art
returns in the Ant-v4 environment while achieving a 25 to 30 percent faster
wall-clock training time. Finally, we also benchmark the policy inference on a
diverse set of microcontrollers and show that in most cases our optimized
inference implementation is much faster than even the manufacturer's DSP
libraries. To the best of our knowledge, BackpropTools enables the first-ever
demonstration of training a deep RL algorithm directly on a microcontroller,
giving rise to the field of Tiny Reinforcement Learning (TinyRL). Project page:
https://backprop.toolsComment: Project page: https://backprop.tool
Physics-Inspired Temporal Learning of Quadrotor Dynamics for Accurate Model Predictive Trajectory Tracking
Accurately modeling quadrotor's system dynamics is critical for guaranteeing
agile, safe, and stable navigation. The model needs to capture the system
behavior in multiple flight regimes and operating conditions, including those
producing highly nonlinear effects such as aerodynamic forces and torques,
rotor interactions, or possible system configuration modifications. Classical
approaches rely on handcrafted models and struggle to generalize and scale to
capture these effects. In this paper, we present a novel Physics-Inspired
Temporal Convolutional Network (PI-TCN) approach to learning quadrotor's system
dynamics purely from robot experience. Our approach combines the expressive
power of sparse temporal convolutions and dense feed-forward connections to
make accurate system predictions. In addition, physics constraints are embedded
in the training process to facilitate the network's generalization capabilities
to data outside the training distribution. Finally, we design a model
predictive control approach that incorporates the learned dynamics for accurate
closed-loop trajectory tracking fully exploiting the learned model predictions
in a receding horizon fashion. Experimental results demonstrate that our
approach accurately extracts the structure of the quadrotor's dynamics from
data, capturing effects that would remain hidden to classical approaches. To
the best of our knowledge, this is the first time physics-inspired deep
learning is successfully applied to temporal convolutional networks and to the
system identification task, while concurrently enabling predictive control.Comment: Video: https://youtu.be/dsOtKfuRjE
Safety-Aware Human-Robot Collaborative Transportation and Manipulation with Multiple MAVs
Human-robot interaction will play an essential role in various industries and
daily tasks, enabling robots to effectively collaborate with humans and reduce
their physical workload. Most of the existing approaches for physical
human-robot interaction focus on collaboration between a human and a single
ground robot. In recent years, very little progress has been made in this
research area when considering aerial robots, which offer increased versatility
and mobility compared to their grounded counterparts. This paper proposes a
novel approach for safe human-robot collaborative transportation and
manipulation of a cable-suspended payload with multiple aerial robots. We
leverage the proposed method to enable smooth and intuitive interaction between
the transported objects and a human worker while considering safety constraints
during operations by exploiting the redundancy of the internal transportation
system. The key elements of our system are (a) a distributed payload external
wrench estimator that does not rely on any force sensor; (b) a 6D admittance
controller for human-aerial-robot collaborative transportation and
manipulation; (c) a safety-aware controller that exploits the internal system
redundancy to guarantee the execution of additional tasks devoted to preserving
the human or robot safety without affecting the payload trajectory tracking or
quality of interaction. We validate the approach through extensive simulation
and real-world experiments. These include as well the robot team assisting the
human in transporting and manipulating a load or the human helping the robot
team navigate the environment. To the best of our knowledge, this work is the
first to create an interactive and safety-aware approach for quadrotor teams
that physically collaborate with a human operator during transportation and
manipulation tasks.Comment: Guanrui Li and Xinyang Liu contributed equally to this pape
The Role of Vision Algorithms for Micro Aerial Vehicles
This work investigates the research topics related to visual aerial navigation in loosely structured and cluttered environments. During the inspection of the desired infrastructure the robot is required to fly in an environment which is uncertain and only partially structured because, usually, no reliable layouts and drawings of the surroundings are available.
To support these features, advanced cognitive capabilities are required, and
in particular the role played by vision is of paramount importance. The use
of vision and other onboard sensors such as IMU and GPS play a fundamental to
provide high level degree of autonomy to flying vehicles. In detail, the outline of
this thesis is organized as follows
• Chapter 1 is a general introduction of the aerial robotic field, the quadrotor
platform, the use of onboard sensors like cameras and IMU for autonomous
navigation. A discussion about camera modeling, current state of art on vision
based control, navigation, environment reconstruction and sensor fusion
is presented.
• Chapter 2 presents vision based control algorithms useful for reactive control
like collision avoidance, perching and grasping tasks. Two main contributions
are presented based on relative depth map and image based visual
servoing respectively.
• Chapter 3 discusses the use of vision algorithms for localization and mapping.
Compared to the previous chapter, the vision algorithm is more complex
involving vehicle’s poses estimation and environment reconstruction. An algorithm
based on RGB-D sensors for localization, extendable to localization
of multiple vehicles, is presented. Moreover, an environment representation
for planning purposes, applied to industrial environments, is introduced.
• Chapter 4 introduces the possibility to combine vision measurements and
IMU to estimate the motion of the vehicle. A new contribution based on Pareto Optimization, which overcome classical Kalman filtering techniques,
is presented.
• Chapter 5 contains conclusion, remarks and proposals for possible developments
Visual Servoing of Quadrotors for Perching by Hanging From Cylindrical Objects
This paper addresses vision-based localization and servoing for quadrotors to enable autonomous perching by hanging from cylindrical structures using only a monocular camera. We focus on the problems of relative pose estimation, control, and trajectory planning for maneuvering a robot relative to cylinders with unknown orientations. We first develop a geometric model that describes the pose of the robot relative to a cylinder. Then, we derive the dynamics of the system, expressed in terms of the image features. Based on the dynamics, we present a controller which guarantees asymptotic convergence to the desired image space coordinates. Finally, we develop an effective method to plan dynamically-feasible trajectories in the image space, and we provide experimental results to demonstrate the proposed method under different operating conditions such as hovering, trajectory tracking, and perching
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