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<summary> <b>"High-precision UAV localization system for landing on a mobile collaborative robot based on an IR marker pattern recognition"</b>, Ivan Kalinov, <ins>Evgenii Safronov</ins>, Ruslan Agishev, Mikhail Kurenkov, Dzmitry Tsetserukou, <i>2019 IEEE 89th Vehicular Technology Conference (VTC2019-Spring)</i><br/>, cited by: <a href="https://scholar.google.com/scholar?oi=bibs&hl=en&cites=7365323438557675703">10</a>, paper at <a href="https://ieeexplore.ieee.org/abstract/document/8746668/">IEEE</a>, read <a href="https://www.researchgate.net/profile/Dzmitry-Tsetserukou/publication/334074171_High-Precision_UAV_Localization_System_for_Landing_on_a_Mobile_Collaborative_Robot_Based_on_an_IR_Marker_Pattern_Recognition/links/5f528df3299bf13a31a064c1/High-Precision-UAV-Localization-System-for-Landing-on-a-Mobile-Collaborative-Robot-Based-on-an-IR-Marker-Pattern-Recognition.pdf">pdf</a>
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We present a novel high-precision UAV localization system for interconnection between two collaborative robots, i.e., unmanned ground robot (UGR) and unmanned aerial vehicle (UAV) capable of autonomous navigation and precise localization in an indoor environment. Based on our localization system we have achieved robust UAV landing on the moving robot using a fusion of 2D LIDAR sensors, camera, and ultrasonic system for localization. In addition, UAV is capable of accurate high-altitude indoor flights (up to 15 m) relative to the ground robot. Localization of UAV is based on the developed adaptive active IR marker system to achieve reliable flight on different altitudes and light conditions. In this paper, we describe the operating principle of the system and present the results of UAV flight experiments. One of promising applications of the developed system is automated inventory management of warehouses.
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<summary> <b>"Asynchronous behavior trees with memory aimed at aerial vehicles with redundancy in flight controller"</b>, <ins>Evgenii Safronov</ins>, Michael Vilzmann, Dzmitry Tsetserukou, Konstantin Kondak, <i>2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)</i><br/>, cited by: <a href="https://scholar.google.com/scholar?oi=bibs&hl=en&cites=16964304501276599688">6</a>, paper at <a href="https://ieeexplore.ieee.org/abstract/document/8967928/">IEEE</a>, read <a href="https://arxiv.org/pdf/1907.00253">pdf</a>
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Complex aircraft systems are becoming a target for automation. For successful operation, they require both efficient and readable mission execution system (MES). Flight control computer (FCC) units, as well as all important subsystems, are often duplicated. Discrete nature of MES does not allow small differences in data flow among redundant FCCs which are acceptable for continuous control algorithms. Therefore, mission state consistency has to be specifically maintained. We present a novel MES which includes FCC state synchronization. To achieve this result we developed the new concept of Asynchronous Behavior Tree with Memory (ABTM) and proposed a state synchronization algorithm. The implemented system was tested and proven to work in a real-time simulation of High Altitude Pseudo Satellite (HAPS) mission.
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<summary> <b>"Task Planning with Belief Behavior Trees"</b>, <ins>Evgenii Safronov</ins>, Michele Colledanchise, Lorenzo Natale, <i>2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)</i><br/>, cited by: <a href="https://scholar.google.com/scholar?oi=bibs&hl=en&cites=15253686761539631670">3</a>, paper at <a href="https://ieeexplore.ieee.org/abstract/document/9341562/">IEEE</a>, read <a href="https://arxiv.org/pdf/2008.09393">pdf</a>
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In this paper, we propose Belief Behavior Trees (BBTs), an extension to Behavior Trees (BTs) that allows to automatically create a policy that controls a robot in partially observable environments. We extend the semantic of BTs to account for the uncertainty that affects both the conditions and action nodes of the BT. The tree gets synthesized following a planning strategy for BTs proposed recently: from a set of goal conditions we iteratively select a goal and find the action, or in general the subtree, that satisfies it. Such action may have preconditions that do not hold. For those preconditions, we find an action or subtree in the same fashion. We extend this approach by including, in the planner, actions that have the purpose to reduce the uncertainty that affects the value of a condition node in the BT (for example, turning on the lights to have better lighting conditions). We demonstrate that BBTs allows task planning with …
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<summary> <b>"Node templates to improve reusability and modularity of behavior trees"</b>, <ins>Evgenii Safronov</ins>, <i>2020, arXiv preprint arXiv:2002.03167</i><br/>, cited by: <a href="https://scholar.google.com/scholar?oi=bibs&hl=en&cites=7009371671915186503">2</a>, paper at <a href="https://arxiv.org/abs/2002.03167">2020, arXiv preprint arXiv:2002.03167</a>, read <a href="https://arxiv.org/pdf/2002.03167">pdf</a>
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Behavior Trees (BTs) got the robotics society attention not least thanks to their modularity and reusability. The subtrees of BTs could be treated as separate behaviors and therefore reused. We address the following research question: do we exploit the full power of BT on these properties? We suggest to generalise the idea of subtree reuse to "node templates" concept, which allows to represent an arbitrary nodes collection. In addition, previously hardcoded behaviors such as Node* and many Decorator nodes could be implemented in a memory-based BT by node templates.
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<summary> <b>"Compact Belief State Representation for Task Planning"</b>, <ins>Evgenii Safronov</ins>, Michele Colledanchise, Lorenzo Natale, <i>2020 IEEE 16th International Conference on Automation Science and Engineering (CASE)</i><br/>, paper at <a href="https://ieeexplore.ieee.org/abstract/document/9216994/">IEEE</a>, read <a href="https://arxiv.org/pdf/2008.10386">pdf</a>
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Task planning in a probabilistic belief space generates complex and robust execution policies in domains affected by state uncertainty. The performance of a task planner relies on the belief space representation of the world. However, such representation becomes easily intractable as the number of variables and execution time grow. To address this problem, we developed a novel belief space representation based on the Cartesian product and union operations over belief substates. These two operations and single variable assignment nodes form And-Or directed acyclic graph of Belief States (AOBSs). We show how to apply actions with probabilistic outcomes and how to measure the probability of conditions holding true over belief states. We evaluated AOBSs performance in simulated forward state space exploration. We compared the size of AOBSs with the size of Binary Decision Diagrams (BDDs) that were …
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