DESIGN OF A CANARD-WING UAV

In this project, we intend to design a Canard wing-based Unmanned Aerial Vehicle (UAV), which can carry a wide range of missions, providing capabilities to handle our challenges with sophisticated care. Canard-based UAV is the latest trend in aviation technology designed for the use case of providing better maneuverability, which in result gives the UAV new capabilities, such as increased time for data gathering, transferring, and autonomous behavior. The basic disciplines like Aerodynamics, Engineering design, Flight dynamics, Propulsion, and Performance are carried out during the UAV designing process. The proposed methodology applied in this project is weight estimation, initial sizing, aerofoil and wing geometry, fuselage sizing, tail sizing, T/W ratio, aerodynamics, and performance analysis. The design of Canard Based UAV leads to a deeper understanding of the trade-off studies of the UAV and is demonstrated by optimizing for designed missions like surveillance. A drafted sketch is presented at the end of the design phase featuring the selected configurations of major components.

Dialogue Control of Collaborative Robots Based on Artifi cial Neural Networks

Collaborative robotics progress is based on the possibility to apply robots to the wide range activity of peoples. Now the user can control the robot without any special knowledge in robotics and safe. The price of such possibilities is complication of control system of robot which now has to aquire an opportunity of autonomous behavior under human’s control, using the necessary sensors and elements of artificial intelligence. In our research we suppose the collaborative robot as mobile robotic device possible to fulfil some work under the human’s speech demands not only in the same space with the human. We also suppose the necessity of bilateral dialogue human-robot to make it clear the task, the current situation, the state as robot as human. The complex task of control, or may be the collaboration of human with his artificial partner need new means of control, situation recognition, speech dialogue management. As a mean to solve the whole complex of problems we propose the combination of different artificial neural networks. Such as convolution networks for image recognition, deep networks for speech recognition, LSTM networks for autonomous movement of robot control in current situation. Investigations in the field of mobile and manipulation robots including the human-robot control have been proceeded for some years in the department "Robotic systems and mechatronics" BMSTU celebrating now it 70th years Jubilee. The reader may find some of the works in the bibliography. In result of all these investigations we obtain the service robot model which may find a wide application.

Framework proposal for adaptive mobile intelligent agents

Intelligent agents are computational entities which have elements that provide them with the ability to perceive and manipulate their environment: sensors and actuators. These are characterized by displaying various properties that adapt and achieve their objectives. Autonomy, learning, collaboration and reasoning are examples of these properties which together make them intelligent artificial entities. This article shows the development of a framework that has made it possible to speed-up the construction of a system of adaptive mobile intelligent agents, called SySAge. The system agents have integrated search and learning techniques for the execution of automated processes focused on solving search, classification and optimization problems. It has been found that through learning, the agents were able to estimate input parameters and apply them in estimating the shortest route in a graph, considering cost and penalty aspects. To determine the choice of search technique, a probabilistic selection was used. The autonomous behavior of each agent was appreciated through the various attempts to solve the search problem and not to focus the information acquired individually on a single agent.

Recognition Method of Vehicle Cluster Situation Based on Set Pair Logic considering Driver’s Cognition

The recognition of vehicle cluster situations is one of the critical technologies of advanced driving, such as intelligent driving and automated driving. The accurate recognition of vehicle cluster situations is helpful for behavior decision-making safe and efficient. In order to accurately and objectively identify the vehicle cluster situation, a vehicle cluster situation model is proposed based on the interval number of set pair logic. The proposed model can express the traffic environment’s knowledge considering each vehicle’s characteristics, grouping relationships, and traffic flow characteristics in the target vehicle’s interest region. A recognition method of vehicle cluster situation is designed to infer the traffic environment and driving conditions based on the connection number of set pair logic. In the proposed model, the uncertainty of the driver’s cognition is fully considered. In the recognition method, the relative uncertainty and relative certainty of driver’s cognition, traffic information, and vehicle cluster situation are fully considered. The verification results show that the proposed recognition method of vehicle cluster situations can realize accurate and objective recognition. The proposed anthropomorphic recognition method could provide a basis for vehicle autonomous behavior decision-making.

A realistic locomotory model of Drosophila larva for behavioral simulations.

The Drosophila larva is extensively used as model species in experiments where behavior is recorded via tracking equipment and evaluated via population-level metrics. Although larva locomotion neuromechanics have been studied in detail, no comprehensive model has been proposed for realistic simulations of foraging experiments directly comparable to tracked recordings. Here we present a virtual larva for simulating autonomous behavior, fitting empirical observations of spatial and temporal kinematics. We propose a trilayer behavior-based control architecture for larva foraging, allowing to accommodate increasingly complex behaviors. At the basic level, forward crawling and lateral bending are generated via coupled, interfering oscillatory processes under the control of an intermittency module, alternating between crawling bouts and pauses. Next, navigation in olfactory environments is achieved via active sensing and top-down modulation of bending dynamics by concentration changes. Finally, adaptation at the highest level entails associative learning. We could accurately reproduce behavioral experiments on autonomous free exploration, chemotaxis, and odor preference testing. Inter-individual variability is preserved across virtual larva populations allowing for single animal and population studies. Our model is ideally suited to interface with neural circuit models of sensation, memory formation and retrieval, and spatial navigation.

How Could We Know When a Robot was a Moral Patient?

There is growing interest in machine ethics in the question of whether and under what circumstances an artificial intelligence would deserve moral consideration. This paper explores a particular type of moral status that the author terms psychological moral patiency, focusing on the epistemological question of what sort of evidence might lead us to reasonably conclude that a given artificial system qualified as having this status. The paper surveys five possible criteria that might be applied: intuitive judgments, assessments of intelligence, the presence of desires and autonomous behavior, evidence of sentience, and behavioral equivalence. The author suggests that despite its limitations, the latter approach offers the best way forward, and defends a variant of that, termed the cognitive equivalence strategy. In short, this holds that an artificial system should be considered a psychological moral patient to the extent that it possesses cognitive mechanisms shared with other beings such as nonhuman animals whom we also consider to be psychological moral patients.

Continuous learning of emergent behavior in robotic matter

One of the main challenges in robotics is the development of systems that can adapt to their environment and achieve autonomous behavior. Current approaches typically aim to achieve this by increasing the complexity of the centralized controller by, e.g., direct modeling of their behavior, or implementing machine learning. In contrast, we simplify the controller using a decentralized and modular approach, with the aim of finding specific requirements needed for a robust and scalable learning strategy in robots. To achieve this, we conducted experiments and simulations on a specific robotic platform assembled from identical autonomous units that continuously sense their environment and react to it. By letting each unit adapt its behavior independently using a basic Monte Carlo scheme, the assembled system is able to learn and maintain optimal behavior in a dynamic environment as long as its memory is representative of the current environment, even when incurring damage. We show that the physical connection between the units is enough to achieve learning, and no additional communication or centralized information is required. As a result, such a distributed learning approach can be easily scaled to larger assemblies, blurring the boundaries between materials and robots, paving the way for a new class of modular “robotic matter” that can autonomously learn to thrive in dynamic or unfamiliar situations, for example, encountered by soft robots or self-assembled (micro)robots in various environments spanning from the medical realm to space explorations.