Exploratory Path Planning for Mobile Robots in Dynamic Environments with Ant Colony Optimization

In the path planning task for autonomous mobile robots, robots should be able to plan their trajectory to leave the start position and reach the goal, safely. There are several path planning approaches for mobile robots in the literature. Ant Colony Optimization algorithms have been investigated for this problem, giving promising results. In this paper, we propose the Max-Min Ant System for Dynamic Path Planning algorithm for the exploratory path planning task for autonomous mobile robots based on topological maps. A topological map is an environment representation whose focus is the main reference points of the environment and their connections. Based on this representation, the path can be composed by a sequence of state/actions pairs, which facilitates the navigability of the path, with no need to have the information of the complete map. The proposed algorithm was evaluated in static and dynamic envi- ronments, showing promising results in both of them. Experiments in dynamic environments show the adaptability of our proposal

Fast-BoW: Scaling Bag-of-Visual-Words Generation

The bag-of-visual-words (BoW) generation is a widely used unsupervised feature extraction method for the variety of computer vision applications. However, space and computational complexity of bag-of-visual-words generation increase with an increase in the size of the dataset because of computational complexities involved in underlying algorithms. In this paper, we present Fast-BoW, a scalable method for BoW generation for both hard and soft vector-quantization with time complexities O(|h| log2 k) and O(|h|k), respectively1. We replace the process of finding the closest cluster center with a softmax classifier which improves the cluster boundaries over k-means and also can be used for both hard and soft BoW encoding. To make the model compact and faster, we quantize the real weights into integer weights which can be represented using few bits (2−8) only. Also, on the quantized weights, we apply the hashing to reduce the number of multiplications which makes the process further faster. We evaluated the proposed approach on several public benchmark datasets. The experimental results outperform the existing hierarchical clustering tree-based approach by ≈ 12 times

Analytics-based decomposition of a class of bilevel problems

This paper proposes a new class of multi-follower bilevel problems. In this class the followers may be nonlinear, do not share constraints or variables, and are at most weakly constrained. This allows the leader variables to be partitioned among the followers. The new class is formalised and compared with existing problems in the literature. We show that approaches currently in use for solving multi-follower problems are unsuitable for this class. Evolutionary algorithms can be used, but these are computationally intensive and do not scale up well. Instead we propose an analytics-based decomposition approach. Two example problems are solved using our approach and two evolutionary algorithms, and the decomposition approach produces much better and faster results as the problem size increases

Optimal generation scheduling in hydro-power plants with the Coral Reefs Optimization algorithm

Hydro-power plants are able to produce electrical energy in a sustainable way. A known format for producing energy is through generation scheduling, which is a task usually established as a Unit Commitment problem. The challenge in this process is to define the amount of energy that each turbine-generator needs to deliver to the plant, to fulfill the requested electrical dispatch commitment, while coping with the operational restrictions. An optimal generation scheduling for turbine-generators in hydro-power plants can offer a larger amount of energy to be generated with respect to non-optimized schedules, with significantly less water consumption. This work presents an efficient mathematical modelling for generation scheduling in a real hydro-power plant in Brazil. An optimization method based on different versions of the Coral Reefs Optimization algorithm with Substrate Layers (CRO) is proposed as an effective method to tackle this problem.This approach uses different search operators in a single population to refine the search for an optimal scheduling for this problem. We have shown that the solution obtained with the CRO using Gaussian search in exploration is able to produce competitive solutions in terms of energy production. The results obtained show a huge savings of 13.98 billion (liters of water) monthly projected versus the non-optimized scheduling.European CommissionMinisterio de Economía y CompetitividadComunidad de Madri

An analytics-based heuristic decomposition of a bilevel multiple-follower cutting stock problem

This paper presents a new class of multiple-follower bilevel problems and a heuristic approach to solving them. In this new class of problems, the followers may be nonlinear, do not share constraints or variables, and are at most weakly constrained. This allows the leader variables to be partitioned among the followers. We show that current approaches for solving multiple-follower problems are unsuitable for our new class of problems and instead we propose a novel analytics-based heuristic decomposition approach. This approach uses Monte Carlo simulation and k-medoids clustering to reduce the bilevel problem to a single level, which can then be solved using integer programming techniques. The examples presented show that our approach produces better solutions and scales up better than the other approaches in the literature. Furthermore, for large problems, we combine our approach with the use of self-organising maps in place of k-medoids clustering, which significantly reduces the clustering times. Finally, we apply our approach to a real-life cutting stock problem. Here a forest harvesting problem is reformulated as a multiple-follower bilevel problem and solved using our approachThis publication has emanated from research conducted with the financial support of Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/228

Внедрение микрогенерации и энергосберегающих технологий в рамках концепции зеленой экономики: зарубежный опыт и Россия

The aim of this work is a comparative analysis of the level of development of microgeneration and energy-saving technologies in the framework of the national economy of Russia and the world. The relevance is predetermined by the rapid growth of the investment policy of microgrids and energy-saving technologies based on renewable energy sources (2.6 trillion dollars). Basic information research provided analytical reviews, reports and analytical materials, specialized international departments and agencies, the Ministry of Energy of the Russian Federation, as well as the work of Russian and foreign scientists. Understanding the large-scale tasks related to the development, as well as the development of national and international relations, are an incentive for the pursuit of cleaner, primarily technologies. By 2030, provided that the current course on sustainable development is maintained, the green economy should grow to 10 % of the gross world product. Microenergy is an energy-efficient energy source in the restructuring of Russia's energy sector - the transition from a centralized system, the use of large sources of electricity production, the use of various energy sources that are most suitable for these environmental conditions and the characteristics of natural consumers. Reducing the negative impact of pollution on health and the environment can significantly reduce the burden on the economy, thereby freeing up resources for its growth. The transition of the global economy to a model of green growth will require significant efforts to expand international cooperation. This will require consistent government policies over many years. It is advisable for Russia to join in the development of methodologies and the creation of tools for implementing green initiatives.Целью настоящей работы является сравнительный анализ уровня развития микрогенерации и энергосберегающих технологий в рамках концепции зеленой экономики в России и в мире. Актуальность предопределена стремительным ростом инвестиционной привлекательности микросетей и энергосберегающих технологий на основе возобновляемой энергии (2,6 трлн долл.). Информационной базой исследования послужили аналитические обзоры, отчеты и аналитические материалы профильных международных ведомств и агентств, Министерства энергетики Российской Федерации, а также работы российских и зарубежных ученых. Понимание масштабов задач устойчивого развития, а также развивающееся национальное и международное регулирование становятся стимулами стремительного развития более чистых, в первую очередь низкоуглеродных, технологий во многих отраслях. К 2030 г. при условии сохранения нынешнего курса на устойчивое развитие зеленая экономика должна вырасти до 10 % валового мирового продукта. Микроэнергетика является энергоэффективным инструментом в структурной перестройке энергетики России - переходу от централизованной системы, использующей крупные источники производства электроэнергии, к использованию разнообразных типов источников энергии, наиболее подходящих к данным природным условиям и особенностям конкретных потребителей. Снижение негативного воздействия загрязнения на здоровье и окружающую среду способно существенно уменьшить нагрузку на экономику, тем самым высвобождая ресурсы для ее роста. Вне всякого сомнения, переход мировой экономики на модель зеленого роста потребует значительных усилий по расширению международного сотрудничества. При этом потребуется последовательное проведение правительствами соответствующей политики на протяжении многих лет. России целесообразно подключиться к разработке методик и созданию инструментария для внедрения зеленых инициатив