Метод оцінки часу затримки в процесі потокового мовлення

Розглянута задача мінімізації затримки медіаконтенту при онлайн-трансляції. Об’єктом дослідження є медіасерверні платформи, що використовуються для організації онлайн-трансляцій медіаконтенту. Метою роботи є дослідження часу затримки при доставці медіаконтенту в процесі онлайн-трансляції. В процесі проведення експериментів встановлено, що найбільші витрати часу на доставку зумовлені процесом обробки потоку в медіасервері. Затримка в медіасервері виникає за рахунок перетворень сигналу. Проаналізовано найбільш поширені на ринку медіапослуг медасервери, які дозволяють організувати онлайнтрансляцію на регіональному рівні. Це Ant Media Server 1.7.2, MistServer 2.14.1, Nimble Streamer Server 3.5.4, Red5 1.1.1,Wowza Streaming Engine 4.7. Запропоновано методику оцінки часу затримки доставки медіаконтенту в мережах потокового мовлення. Розроблена методика надає змогу визначити як загальний час затримки, так і його складові на кожному з етапів доставки

Sequential Neural Barriers for Scalable Dynamic Obstacle Avoidance

There are two major challenges for scaling up robot navigation around dynamic obstacles: the complex interaction dynamics of the obstacles can be hard to model analytically, and the complexity of planning and control grows exponentially in the number of obstacles. Data-driven and learning-based methods are thus particularly valuable in this context. However, data-driven methods are sensitive to distribution drift, making it hard to train and generalize learned models across different obstacle densities. We propose a novel method for compositional learning of Sequential Neural Control Barrier models (SNCBFs) to achieve scalability. Our approach exploits an important observation: the spatial interaction patterns of multiple dynamic obstacles can be decomposed and predicted through temporal sequences of states for each obstacle. Through decomposition, we can generalize control policies trained only with a small number of obstacles, to environments where the obstacle density can be 100x higher. We demonstrate the benefits of the proposed methods in improving dynamic collision avoidance in comparison with existing methods including potential fields, end-to-end reinforcement learning, and model-predictive control. We also perform hardware experiments and show the practical effectiveness of the approach in the supplementary video.Comment: To be published in IROS 202

An Omnidirectional Platform for Education and Research in Cooperative Robotics

In this paper we present a new, affordable, omnidirectional robot platform which is suitable for research and education in cooperative robotics. We design and implement the platform for the purpose of multi-agent object manipulation and transportation. The design consists of three omnidirectional wheels with two additional traction wheels, making multirobot object manipulation possible. It is validated by performing simple experiments using a setup with one robot and one target object. The execution flow of a simple task (Approach–Press–Lift–Hold–Set) is studied. In addition, we experiment to find the limits of the applied pressure and object orientation under certain conditions. The experiments demonstrate the significance of our inexpensive platform for research and education by proving its feasibility of use in topics such as collaborative robotics, physical interaction, and mobile manipulation

Optimal propagating fronts using Hamilton-Jacobi equations

The optimal handling of level sets associated to the solution of Hamilton-Jacobi equations such as the normal flow equation is investigated. The goal is to find the normal velocity minimizing a suitable cost functional that accounts for a desired behavior of level sets over time. Sufficient conditions of optimality are derived that require the solution of a system of nonlinear Hamilton-Jacobi equations. Since finding analytic solutions is difficult in general, the use of numerical methods to obtain approximate solutions is addressed by dealing with some case studies in two and three dimensions

Model Predictive Control of a Road Junction

This paper presents a model predictive control (MPC) approach for optimally managing the traffic light (TL) signals at a road junction. The objective is to improve queue balancing compared to traditional control strategies where TL signals are periodic. The resulting MPC optimization problem is of quadratic mixed-integer nature. The proposed approach is validated via simulations based on a real scenario