1,722,393 research outputs found
Performance evaluation of concurrency control techniques for database management systems
No full textSIGLELD:D50290/84 / BLDSC - British Library Document Supply CentreGBUnited Kingdo
Dynamic evolution of transverse energy flow in focused asymmetric optical vector-vortex beams
No full textWe present here controlled generation of asymmetric optical vector-vortex beams using a two-mode optical fiber and study the dynamic evolution of the transverse energy flow (TEF) when focused through a spherical lens. The dependence of the TEF on various factors such as the vortex charge, vortex anisotropy and polarization structure around the vortex core is explored. It is found that the TEF is directly proportional to the phase gradient and its direction is governed by the vortex charge. The presence of C-point polarization singularity in the beam and the polarization structure around it results in vibrational phase gradient which is the major factor deciding the TEF in vector-vortex beams
Planning Natural Locomotion for Articulated Soft Quadrupeds
No full textEmbedding elastic elements into legged robots through mechanical design enables highly efficient oscillating patterns that resemble natural gaits. However, current trajectory planning techniques miss the opportunity of taking advantage of these natural motions. This work proposes a locomotion planning method that aims to unify traditional trajectory generation with modal oscillations. Our method utilizes task-space linearized modes for generating center of mass trajectories on the sagittal plane. We then use nonlinear optimization to find the gait timings that match these trajectories within the Divergent Component of Motion planning framework. This way, we can robustly translate the modes-aware centroidal motions into joint coordinates. We validate our approach with promising results and insights through experiments on a compliant quadrupedal robot.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Learning & Autonomous Contro
Regulations Aware Motion Planning for Autonomous Surface Vessels in Urban Canals
No full textIn unstructured urban canals, regulation-aware interactions with other vessels are essential for collision avoidance and social compliance. In this paper, we propose a regulations aware motion planning framework for Autonomous Surface Vessels (ASVs) that accounts for dynamic and static obstacles. Our method builds upon local model predictive contouring control (LMPCC) to generate motion plans satisfying kino-dynamic and collision constraints in real-time while including regulation awareness. To incorporate regulations in the planning stage, we propose a cost function encouraging compliance with rules describing interactions with other vessels similar to COLlision avoidance REGulations at sea (COLREGs). These regulations are essential to make an ASV behave in a predictable and socially compliant manner with regard to other vessels. We compare the framework against baseline methods and show more effective regulation-compliant avoidance of moving obstacles with our motion planner. Additionally, we present experimental results in an outdoor environment.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Learning & Autonomous Contro
Where to Look Next: Learning Viewpoint Recommendations for Informative Trajectory Planning
No full textSearch missions require motion planning and navigation methods for information gathering that continuously replan based on new observations of the robot's surroundings. Current methods for information gathering, such as Monte Carlo Tree Search, are capable of reasoning over long horizons, but they are computationally expensive. An alternative for fast online execution is to train, offline, an information gathering policy, which indirectly reasons about the information value of new observations. However, these policies lack safety guarantees and do not account for the robot dynamics. To overcome these limitations we train an information-aware policy via deep reinforcement learning, that guides a receding-horizon trajectory optimization planner. In particular, the policy continuously recommends a reference viewpoint to the local planner, such that the resulting dynamically feasible and collision-free trajectories lead to observations that maximize the information gain and reduce the uncertainty about the environment. In simulation tests in previously unseen environments, our method consistently outperforms greedy next-best-view policies and achieves competitive performance compared to Monte Carlo Tree Search, in terms of information gains and coverage time, with a reduction in execution time by three orders of magnitude.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Learning & Autonomous Contro
An Experimental Study of Wind Resistance and Power Consumption in MAVs with a Low-Speed Multi-Fan Wind System
No full textThis paper discusses a low-cost, open-source and open-hardware design and performance evaluation of a low-speed, multi-fan wind system dedicated to micro air vehicle (MAV) testing. In addition, a set of experiments with a flapping wing MAV and rotorcraft is presented, demonstrating the capabilities of the system and the properties of these different types of drones in response to various types of wind. We performed two sets of experiments where a MAV is flying into the wake of the fan system, gathering data about states, battery voltage and current. Firstly, we focus on steady wind conditions with wind speeds ranging from 0.5 m S-1 to 3.4 m S-1. During the second set of experiments, we introduce wind gusts, by periodically modulating the wind speed from 1.3 m S−1 to 3.4 m S−1 with wind gust oscillations of 0.5 Hz, 0.25 Hz and 0.125 Hz. The “Flapper” flapping wing MAV requires much larger pitch angles to counter wind than the “CrazyFlie” quadrotor. This is due to the Flapper's larger wing surface. In forward flight, its wings do provide extra lift, considerably reducing the power consumption. In contrast, the CrazyFlie's power consumption stays more constant for different wind speeds. The experiments with the varying wind show a quicker gust response by the CrazyFlie compared with the Flapper drone, but both their responses could be further improved. We expect that the proposed wind gust system will provide a useful tool to the community to achieve such improvements.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Control & Simulatio
Self-supervised Monocular Multi-robot Relative Localization with Efficient Deep Neural Networks
No full textRelative localization is an important ability for multiple robots to perform cooperative tasks in GPS-denied environments. This paper presents a novel autonomous positioning framework for monocular relative localization of multiple tiny flying robots. This approach does not require any groundtruth data from external systems or manual labeling. Instead, the proposed framework is able to label real-world images with 3D relative positions between robots based on another onboard relative estimation technology, using ultra-wideband (UWB). After training in this self-supervised manner, the proposed deep neural network (DNN) can predict relative positions of peer robots by purely using a monocular camera. This deep learning-based visual relative localization is scalable, distributed, and autonomous. We also built an open-source and lightweight simulation pipeline by using Blender for 3D rendering, which allows synthetic image generation of other robots, and generalized training of the neural network. The proposed localization framework is tested on two real-world Crazyflie2 quadrotors by running the DNN on the onboard AIdeck (a tiny AI chip and monocular camera). All results demonstrate the effectiveness of the self-supervised multi-robot localization method. Video: https://youtu.be/7arkaIblPpsGreen Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Control & Simulatio
Barrier Function-based Safe Reinforcement Learning for Formation Control of Mobile Robots
No full textDistributed model predictive control (DMPC) concerns how to online control multiple robotic systems with constraints effectively. However, the nonlinearity, nonconvexity, and strong interconnections of dynamic system models and constraints can make the real-time and real-world DMPC implementations nontrivial. Reinforcement learning (RL) algorithms are promising for control policy design. However, how to ensure safety in terms of state constraints in RL remains a significant issue. This paper proposes a barrier function-based safe reinforcement learning algorithm for DMPC of nonlinear multi-robot systems under state constraints. The proposed approach is composed of several local learning-based MPC regulators. Each regulator, associated with a local system, learns and deploys the local control policy using a safe reinforcement learning algorithm in a distributed manner, i.e., with state information only among the neighbor agents. As a prominent feature of the proposed algorithm, we present a novel barrier-based policy structure to ensure safety, which has a clear mechanistic interpretation. Both simulated and real-world experiments on the formation control of mobile robots with collision avoidance show the effectiveness of the proposed safe reinforcement learning algorithm for DMPC.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Robot Dynamic
Freedom and constraints in the K3 landscape
Full text linkWe consider ``magnetized brane'' compactifications of the type I/heterotic string on K3 with U(1) background fluxes. The gauge group and matter content of the resulting six-dimensional vacua are parameterized by a matrix encoding a lattice contained within the even, self-dual lattice Γ[superscript 3,19]. Mathematical results of Nikulin on lattice embeddings make possible a simple classification of all such solutions. We find that every six-dimensional theory parameterized in this way by a negative semi-definite matrix whose trace satisfies a simple tadpole constraint can be realized as a K3 compactification. This approach makes it possible to explicitly and efficiently construct all models in this class with any particular allowed gauge group and matter content, so that one can immediately ``dial-a-model'' with desired properties
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