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КартаВръзкиТемиБългарияНощта (K-TRIO)Мария Склодовска-КюриЗа ученициЗа учителиМетодологияИзненадай ме
H2020Обмен на изследователи2016–2021

STEP2DYNA · Spatial-temporal information processing for collision detection in dynamic environments

„Хоризонт 2020“ — Действия „Мария Склодовска-Кюри“

Период
2016-07-01 → 2021-12-31
Финансиране от ЕС
1 008 000 €
Участници
11
Схема
MSCA-RISE

Линиите свързват координатора с партньорите.

Накратко на български

Биовдъхновени сензори за откриване на сблъсъци в динамична среда се разработват за използване в автономни дронове. Тези системи целят да бъдат по-надеждни, по-компактни и по-енергоспестяващи от сегашните радари и лазери, за да се намалят тежките катастрофи.

Този кратък обзор е генериран от изкуствен интелект

Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.

Резултати накратко

Spatial-temporal information processing for collision detection in dynamic environments

In the real world, collisions happen at every second - often resulting in serious accidents and fatalities. For example, more than 3560 people die from vehicle collision per day worldwide. Autonomous unmanned aerial vehicles (UAVs) have demonstrated great potential in serving human society such as delivering goods to households and precision farming but are restricted due to lack of collision detection capability. The current approaches for collision detection such as radar, laser based Lidar, and GPS are far from acceptable in terms of reliability, energy consumption and size. The STEP2DYNA consortium proposed an innovative bio-inspired solution for collision detection in dynamic environments at low cost and low energy consumption. The methodologies employed by the consortium take advantage of low cost spatial-temporal and parallel computing capacity of visual neural systems, and realise it as a compact vision module specifically for collision detection in dynamic environments. The multidisciplinary teams across Europe, Asia, and South America have carried out neurophysiological experiments, computational biological system modelling, circuits, and embedded system design, robotics, and UAV experiments, to verify the proposed collision detection sensor system in various conditions. 25 journal papers and 39 conference papers have been published during the project, with more being prepared for submission and publication. The database (e.g. for collision detection) has been uploaded to Github for free public access, along with the published papers. These research outcomes are the result of the close collaboration of the consortium partners supported by this project via secondments, workshops, and training seminars. The publication areas also span cross to neurobiology, neural system modelling, electronic hardware design, robotics, and UAVs. The research teams in Europe have significantly strengthened their capacity with newly obtained skills through the project activities while transferring knowledge to partners. Through this project, the partners have built strong expertise in this exciting multidisciplinary area and the European SME has gained a leading position to exploit the market potential further after the project.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз

Цел на проекта

In the real world, collision happens at every second - often results in serious accidents and fatalities. For example, there are more than 3560 people died from vehicle collision per day worldwide. On the other sector, autonomous unmanned aerial vehicles (UAVs) have demonstrated great potential in serving human society such as delivering goods to households and precision farming, but are restricted due to lacking of collision detection capability. The current approaches for collision detection such as radar, laser based Ladar and GPS are far from acceptable in terms of reliability, energy consumption and size. A new type of low cost, low energy consumption and miniaturized collision detection sensors are badly needed to not only save millions of people’s lives but also make autonomous UAVs and robots safe to serve human society. STEP2DYNA consortium proposes an innovative bio-inspired solution for collision detection in dynamic environments. It takes the advantages of low cost spatial-temporal and parallel computing capacity of visual neural systems and realized it in chip specifically for collision detection in dynamic environments.Realizing visual neural systems in chips demands multidisciplinary expertise in biological system modelling, computer vision, chip design and robotics. This breadth of expertise is not readily possessed within one institution. Secondly, the market potential of the collision detection system could not be well exploited, unless by a dedicated partner from industry. Therefore, this consortium is designed to bring neurobiologists, neural system modelers, chip designers, robotics researchers and engineers from Europe and East of Asia together and complement each others’ research strengths via staff secondments, jointly organised workshops and conferences. Through this project, the partners will build up strong expertise in this exciting multidisciplinary area and the European SME will position well as a market leader in collision detection.

Оригинален текст от CORDIS (на английски).

Участници

Връзки

Данни: CORDIS, © Европейски съюз