3,095 research outputs found
Streaming Scene Maps for Co-Robotic Exploration in Bandwidth Limited Environments
This paper proposes a bandwidth tunable technique for real-time probabilistic
scene modeling and mapping to enable co-robotic exploration in communication
constrained environments such as the deep sea. The parameters of the system
enable the user to characterize the scene complexity represented by the map,
which in turn determines the bandwidth requirements. The approach is
demonstrated using an underwater robot that learns an unsupervised scene model
of the environment and then uses this scene model to communicate the spatial
distribution of various high-level semantic scene constructs to a human
operator. Preliminary experiments in an artificially constructed tank
environment as well as simulated missions over a 10m10m coral reef
using real data show the tunability of the maps to different bandwidth
constraints and science interests. To our knowledge this is the first paper to
quantify how the free parameters of the unsupervised scene model impact both
the scientific utility of and bandwidth required to communicate the resulting
scene model.Comment: 8 pages, 6 figures, accepted for presentation in IEEE Int. Conf. on
Robotics and Automation, ICRA '19, Montreal, Canada, May 201
Machine Learning-Aided Operations and Communications of Unmanned Aerial Vehicles: A Contemporary Survey
The ongoing amalgamation of UAV and ML techniques is creating a significant
synergy and empowering UAVs with unprecedented intelligence and autonomy. This
survey aims to provide a timely and comprehensive overview of ML techniques
used in UAV operations and communications and identify the potential growth
areas and research gaps. We emphasise the four key components of UAV operations
and communications to which ML can significantly contribute, namely, perception
and feature extraction, feature interpretation and regeneration, trajectory and
mission planning, and aerodynamic control and operation. We classify the latest
popular ML tools based on their applications to the four components and conduct
gap analyses. This survey also takes a step forward by pointing out significant
challenges in the upcoming realm of ML-aided automated UAV operations and
communications. It is revealed that different ML techniques dominate the
applications to the four key modules of UAV operations and communications.
While there is an increasing trend of cross-module designs, little effort has
been devoted to an end-to-end ML framework, from perception and feature
extraction to aerodynamic control and operation. It is also unveiled that the
reliability and trust of ML in UAV operations and applications require
significant attention before full automation of UAVs and potential cooperation
between UAVs and humans come to fruition.Comment: 36 pages, 304 references, 19 Figure
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Artificial Intelligence in Radiotherapy Treatment Planning: Present and Future.
Treatment planning is an essential step of the radiotherapy workflow. It has become more sophisticated over the past couple of decades with the help of computer science, enabling planners to design highly complex radiotherapy plans to minimize the normal tissue damage while persevering sufficient tumor control. As a result, treatment planning has become more labor intensive, requiring hours or even days of planner effort to optimize an individual patient case in a trial-and-error fashion. More recently, artificial intelligence has been utilized to automate and improve various aspects of medical science. For radiotherapy treatment planning, many algorithms have been developed to better support planners. These algorithms focus on automating the planning process and/or optimizing dosimetric trade-offs, and they have already made great impact on improving treatment planning efficiency and plan quality consistency. In this review, the smart planning tools in current clinical use are summarized in 3 main categories: automated rule implementation and reasoning, modeling of prior knowledge in clinical practice, and multicriteria optimization. Novel artificial intelligence-based treatment planning applications, such as deep learning-based algorithms and emerging research directions, are also reviewed. Finally, the challenges of artificial intelligence-based treatment planning are discussed for future works
RITA: Boost Autonomous Driving Simulators with Realistic Interactive Traffic Flow
High-quality traffic flow generation is the core module in building
simulators for autonomous driving. However, the majority of available
simulators are incapable of replicating traffic patterns that accurately
reflect the various features of real-world data while also simulating
human-like reactive responses to the tested autopilot driving strategies.
Taking one step forward to addressing such a problem, we propose Realistic
Interactive TrAffic flow (RITA) as an integrated component of existing driving
simulators to provide high-quality traffic flow for the evaluation and
optimization of the tested driving strategies. RITA is developed with
consideration of three key features, i.e., fidelity, diversity, and
controllability, and consists of two core modules called RITABackend and
RITAKit. RITABackend is built to support vehicle-wise control and provide
traffic generation models from real-world datasets, while RITAKit is developed
with easy-to-use interfaces for controllable traffic generation via
RITABackend. We demonstrate RITA's capacity to create diversified and
high-fidelity traffic simulations in several highly interactive highway
scenarios. The experimental findings demonstrate that our produced RITA traffic
flows exhibit all three key features, hence enhancing the completeness of
driving strategy evaluation. Moreover, we showcase the possibility for further
improvement of baseline strategies through online fine-tuning with RITA traffic
flows.Comment: 8 pages, 5 figures, 3 table
Multi-objective optimisation for battery electric vehicle powertrain topologies
Electric vehicles are becoming more popular in the market. To be competitive, manufacturers need to produce vehicles with a low energy consumption, a good range and an acceptable driving performance. These are dependent on the choice of components and the topology in which they are used. In a conventional gasoline vehicle, the powertrain topology is constrained to a few well-understood layouts; these typically consist of a single engine driving one axle or both axles through a multi-ratio gearbox. With electric vehicles, there is more flexibility, and the design space is relatively unexplored. In this paper, we evaluate several different topologies as follows: a traditional topology using a single electric motor driving a single axle with a fixed gear ratio; a topology using separate motors for the front axle and the rear axle, each with its own fixed gear ratio; a topology using in-wheel motors on a single axle; a four-wheel-drive topology using in-wheel motors on both axes. Multi-objective optimisation techniques are used to find the optimal component sizing for a given requirement set and to investigate the trade-offs between the energy consumption, the powertrain cost and the acceleration performance. The paper concludes with a discussion of the relative merits of the different topologies and their applicability to real-world passenger cars
Acoustic Lens Design Using Machine Learning
This thesis aims to contribute to the development of a novel approach and efficient method for the inverse design of acoustic metamaterial lenses using machine learning, specifically, deep learning, generative modeling, and reinforcement learning. Acoustic lenses can focus incident plane waves at the focal point, enabling them to detect structures non-intrusively. These lenses can be utilized in biomedical engineering, medical devices, structural engineering, ultrasound imaging, health monitoring, etc. Finding the global optimum through a traditional iterative optimization process for designing the acoustic lens is challenging. It may become infeasible due to high dimensional parameter space and the compute resources needed. Machine learning techniques have been shown promising for finding the global optimum. Generative modeling is a powerful technique enabling recent advancements in drug discoveries, organic molecule development, and photonics. We combined generative modeling with global optimization and an analytical form of gradients computed by means of multiple scattering theory. In addition, reinforcement learning can potentially outperform traditional optimization algorithms. Thus, in this thesis, the acoustic lens is modeled using two machine learning techniques, such as generative modeling, using 2D-Global Topology Optimization Networks (2D-GLOnets), and reinforcement learning using the Deep Deterministic Policy Gradient (DDPG) algorithm. Results from the aforementioned methods are compared with traditional optimization algorithms
Microstructure Design of Multifunctional Particulate Composite Materials using Conditional Diffusion Models
This paper presents a novel modeling framework to generate an optimal
microstructure having ultimate multifunctionality using a diffusion-based
generative model. In computational material science, generating microstructure
is a crucial step in understanding the relationship between the microstructure
and properties. However, using finite element (FE)-based direct numerical
simulation (DNS) of microstructure for multiscale analysis is extremely
resource-intensive, particularly in iterative calculations. To address this
time-consuming issue, this study employs a diffusion-based generative model as
a replacement for computational analysis in design optimization. The model
learns the geometry of microstructure and corresponding stress contours,
allowing for the prediction of microstructural behavior based solely on
geometry, without the need for additional analysis. The focus on this work is
on mechanoluminescence (ML) particulate composites made with europium ions and
dysprosium ions. Multi-objective optimization is conducted based on the
generative diffusion model to improve light sensitivity and fracture toughness.
The results show multiple candidates of microstructure that meet the design
requirements. Furthermore, the designed microstructure is not present in the
training data but generates new morphology following the characteristics of
particulate composites. The proposed approach provides a new way to
characterize a performance-based microstructure of composite materials
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