Physical Simulation of Inarticulate Robots

In this note we study the structure and the behavior of inarticulate robots. We introduce a robot that moves by successive revolvings. The robot's structure is analyzed, simulated and discussed in detail

Various robust search methods in a Hungarian speech recognition system

This work focuses on the search aspect of speech recognition. We describe some standard algorithms such as stack decoding, multi-stack decoding, the Viterbi beam search and an A* heuristic, then present improvements on these search methods. Finally we compare the performance of each algorithm, grading them according to their performance. We will show that our improvements can outperform the standard methods

A* Orthogonal Matching Pursuit: Best-First Search for Compressed Sensing Signal Recovery

Compressed sensing is a developing field aiming at reconstruction of sparse signals acquired in reduced dimensions, which make the recovery process under-determined. The required solution is the one with minimum $\ell_0$ norm due to sparsity, however it is not practical to solve the $\ell_0$ minimization problem. Commonly used techniques include $\ell_1$ minimization, such as Basis Pursuit (BP) and greedy pursuit algorithms such as Orthogonal Matching Pursuit (OMP) and Subspace Pursuit (SP). This manuscript proposes a novel semi-greedy recovery approach, namely A* Orthogonal Matching Pursuit (A*OMP). A*OMP performs A* search to look for the sparsest solution on a tree whose paths grow similar to the Orthogonal Matching Pursuit (OMP) algorithm. Paths on the tree are evaluated according to a cost function, which should compensate for different path lengths. For this purpose, three different auxiliary structures are defined, including novel dynamic ones. A*OMP also incorporates pruning techniques which enable practical applications of the algorithm. Moreover, the adjustable search parameters provide means for a complexity-accuracy trade-off. We demonstrate the reconstruction ability of the proposed scheme on both synthetically generated data and images using Gaussian and Bernoulli observation matrices, where A*OMP yields less reconstruction error and higher exact recovery frequency than BP, OMP and SP. Results also indicate that novel dynamic cost functions provide improved results as compared to a conventional choice.Comment: accepted for publication in Digital Signal Processin

A new bidirectional algorithm for shortest paths

For finding a shortest path in a network the bidirectional A* algorithm is a widely known algorithm.An A* instance requires a heuristic estimate, a real-valued function on the set of nodes.The version of bidirectional~A* that is considered the most appropriate in literature hitherto,uses so-called balanced heuristic estimates.This means that the two estimates of the two directions are in balance, i.e., their sum is a constant value.In this paper, we do not restrict ourselves any longer to balanced heuristics.A generalized version of bidirectional A* is proposed, where the heuristic estimate does not need to be balanced.This new version turns out to be faster than the one with the balanced heuristic.shortest path;bidirectional search;road network search

Использование графоаналитических методов для формирования траектории группы подвижных объектов в двумерной среде

The problem of movement trajectories formation of a vehicle robots group, functioning in the two-dimensional environment with motionless obstacles, is considered. For the solution of this task, it is possible to use graphic-analytical methods. These methods are based on Dijkstra's algorithms, Bellman-Ford and A *. We carry out experiment including 100 iterations of computer modeling. The results of modeling are data on time of vehicle robots group movement on trajectories developed by means of the algorithms. On the basis of the modeling results was made a comparison of methods. This comparison has allowed revealing the most optimum of methods.Рассматривается задача формирования траекторий движения группы подвижных объектов, функционирующих в двумерной среде с неподвижными препятствиями. Эта задача решалась графоаналитическими методами, основанными на алгоритмах Дейкстры, Беллмана-Форда и A*. Поставлен эксперимент, включающий компьютерное моделирование, результатами которого явились данные о времени движения группы подвижных объектов по траекториям. На основании данных результатов моделирования произведено их сравнение, которое позволило сделать вывод об эффективности различных методов решения задачи, и помогло выявить наиболее оптимальный

Human-Machine Interface for Remote Training of Robot Tasks

Regardless of their industrial or research application, the streamlining of robot operations is limited by the proximity of experienced users to the actual hardware. Be it massive open online robotics courses, crowd-sourcing of robot task training, or remote research on massive robot farms for machine learning, the need to create an apt remote Human-Machine Interface is quite prevalent. The paper at hand proposes a novel solution to the programming/training of remote robots employing an intuitive and accurate user-interface which offers all the benefits of working with real robots without imposing delays and inefficiency. The system includes: a vision-based 3D hand detection and gesture recognition subsystem, a simulated digital twin of a robot as visual feedback, and the "remote" robot learning/executing trajectories using dynamic motion primitives. Our results indicate that the system is a promising solution to the problem of remote training of robot tasks.Comment: Accepted in IEEE International Conference on Imaging Systems and Techniques - IST201