49 research outputs found
A 64mW DNN-based Visual Navigation Engine for Autonomous Nano-Drones
Fully-autonomous miniaturized robots (e.g., drones), with artificial
intelligence (AI) based visual navigation capabilities are extremely
challenging drivers of Internet-of-Things edge intelligence capabilities.
Visual navigation based on AI approaches, such as deep neural networks (DNNs)
are becoming pervasive for standard-size drones, but are considered out of
reach for nanodrones with size of a few cm. In this work, we
present the first (to the best of our knowledge) demonstration of a navigation
engine for autonomous nano-drones capable of closed-loop end-to-end DNN-based
visual navigation. To achieve this goal we developed a complete methodology for
parallel execution of complex DNNs directly on-bard of resource-constrained
milliwatt-scale nodes. Our system is based on GAP8, a novel parallel
ultra-low-power computing platform, and a 27 g commercial, open-source
CrazyFlie 2.0 nano-quadrotor. As part of our general methodology we discuss the
software mapping techniques that enable the state-of-the-art deep convolutional
neural network presented in [1] to be fully executed on-board within a strict 6
fps real-time constraint with no compromise in terms of flight results, while
all processing is done with only 64 mW on average. Our navigation engine is
flexible and can be used to span a wide performance range: at its peak
performance corner it achieves 18 fps while still consuming on average just
3.5% of the power envelope of the deployed nano-aircraft.Comment: 15 pages, 13 figures, 5 tables, 2 listings, accepted for publication
in the IEEE Internet of Things Journal (IEEE IOTJ
Adaptive trajectory tracking control for quadrotors with disturbances by using generalized regression neural networks
In this document, the development and experimental validation of a nonlinear controller with an adaptive disturbance compensation system applied on a quadrotor are presented. The introduced scheme relies on a generalized regression neural network (GRNN). The proposed scheme has a structure consisting of an inner control loop inaccessible to the user (i.e., an embedded controller) and an outer control loop which generates commands for the inner control loop. The adaptive GRNN is applied in the outer control loop. The proposed approach lies in the aptitude of the GRNN to estimate the disturbances and unmodeled dynamic effects without requiring accurate knowledge of the quadrotor parameters. The adaptation laws are deduced from a rigorous convergence analysis ensuring asymptotic trajectory tracking. The proposed control scheme is implemented on the QBall 2 quadrotor. Comparisons with respect to a PD-based control, an adaptive model regressor-based scheme, and an adaptive neural-network controller are carried out. The experimental results validate the functionality of the novel control scheme and show a performance improvement since smaller tracking error values are produced.Fil: Lopez Sanchez, Ivan. INSTITUTO POLITÉCNICO NACIONAL (IPN);Fil: Rossomando, Francisco Guido. Consejo Nacional de Investigaciones CientÃficas y Técnicas. Centro CientÃfico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de IngenierÃa. Instituto de Automática; ArgentinaFil: Pérez Alcocer, Ricardo. INSTITUTO POLITÉCNICO NACIONAL (IPN);Fil: Soria, Carlos Miguel. Consejo Nacional de Investigaciones CientÃficas y Técnicas. Centro CientÃfico Tecnológico Conicet - San Juan. Instituto de Automática. Universidad Nacional de San Juan. Facultad de IngenierÃa. Instituto de Automática; ArgentinaFil: Carelli, Ricardo. Universidad Nacional de San Juan. Facultad de IngenierÃa. Instituto de Automática; ArgentinaFil: Moreno Valenzuela, Javier. INSTITUTO POLITÉCNICO NACIONAL (IPN)
Modeling and Robust Control of Flying Robots Using Intelligent Approaches Modélisation et commande robuste des robots volants en utilisant des approches intelligentes
This thesis aims to modeling and robust controlling of a flying robot of quadrotor type. Where
we focused in this thesis on quadrotor unmanned Aerial Vehicle (QUAV). Intelligent
nonlinear controllers and intelligent fractional-order nonlinear controllers are designed to
control. The QUAV system is considered as MIMO large-scale system that can be divided on
six interconnected single-input–single-output (SISO) subsystems, which define one DOF, i.e.,
three-angle subsystems with three position subsystems. In addition, nonlinear models is
considered and assumed to suffer from the incidence of parameter uncertainty. Every
parameters such as mass, inertia of the system are assumed completely unknown and change
over time without prior information. Next, basing on nonlinear, Fractional-Order nonlinear
and the intelligent adaptive approximate techniques a control law is established for all
subsystems. The stability is performed by Lyapunov method and getting the desired output
with respect to the desired input. The modeling and control is done using
MATLAB/Simulink. At the end, the simulation tests are performed to that, the designed
controller is able to maintain best performance of the QUAV even in the presence of unknown
dynamics, parametric uncertainties and external disturbance
A low computational cost, prioritized, multi-objective optimization procedure for predictive control towards cyber physical systems
Cyber physical systems consist of heterogeneous elements with multiple dynamic features. Consequently, multiple objectives in the optimality of the overall system may be relevant at various times or during certain context conditions. Low cost, efficient implementations of such multi-objective optimization procedures are necessary when dealing with complex systems with interactions. This work proposes a sequential implementation of a multi-objective optimization procedure suitable for industrial settings and cyber physical systems with strong interaction dynamics. The methodology is used in the context of an Extended Prediction self-adaptive Control (EPSAC) strategy with prioritized objectives. The analysis indicates that the proposed algorithm is significantly lighter in terms of computational time. The combination with an input-output formulation for predictive control makes these algorithms suitable for implementation with standardized process control units. Three simulation examples from different application fields indicate the relevance and feasibility of the proposed algorithm
Optimization of non-linear control aerodynamic systems using metaheuristic algorithm Optimisation des commandes non linéaires des systèmes aérodynamiques par les méthodes méta-heuristiques
This thesis is part of the project "modelisation and control dynamic systems"
carried by the laboratory of LMSE. This project aims to develop and optimize
new control approaches for the UAV quadrotor tracking control. This thesis
consisted of the modelling of the quadrotor, and then analysing, designing and
implementing new optimal control strategies based on the model-free concept.
In this context, the aim of the thesis is to propose new control strategies based on
the model-free concept. The proposed strategies help to compensate the
disturbances and model uncertainties. Regarding our work, we have proposed
different control techniques for quadrotor control. First, an optimal model-free
backstepping control law applied to a quadrotor UAV has been proposed. In
addition to this work, the dynamic system has been estimated through a new
proposed fuzzy strategy and merged with the BC under the model-free concept.
Finally, an optimal fuzzy model-free control has been designed based on
decentralized fuzzy control. The objective of these control strategies is to achieve
the best tracking with unknown nonlinear dynamics and external disturbances.
These proposed approaches are validated through analytical and experimental
procedures and the effectiveness checked and compared with regard to the
related controllers in the presence of disturbances and model uncertainties
The 1st International Conference on Computational Engineering and Intelligent Systems
Computational engineering, artificial intelligence and smart systems constitute a hot multidisciplinary topic contrasting computer science, engineering and applied mathematics that created a variety of fascinating intelligent systems. Computational engineering encloses fundamental engineering and science blended with the advanced knowledge of mathematics, algorithms and computer languages. It is concerned with the modeling and simulation of complex systems and data processing methods. Computing and artificial intelligence lead to smart systems that are advanced machines designed to fulfill certain specifications. This proceedings book is a collection of papers presented at the first International Conference on Computational Engineering and Intelligent Systems (ICCEIS2021), held online in the period December 10-12, 2021. The collection offers a wide scope of engineering topics, including smart grids, intelligent control, artificial intelligence, optimization, microelectronics and telecommunication systems. The contributions included in this book are of high quality, present details concerning the topics in a succinct way, and can be used as excellent reference and support for readers regarding the field of computational engineering, artificial intelligence and smart system
Survey of computer vision algorithms and applications for unmanned aerial vehicles
This paper presents a complete review of computer vision algorithms and vision-based intelligent applications, that are developed in the field of the Unmanned Aerial Vehicles (UAVs) in the latest decade. During this time, the evolution of relevant technologies for UAVs; such as component miniaturization, the increase of computational capabilities, and the evolution of computer vision techniques have allowed an important advance in the development of UAVs technologies and applications. Particularly, computer vision technologies integrated in UAVs allow to develop cutting-edge technologies to cope with aerial perception difficulties; such as visual navigation algorithms, obstacle detection and avoidance and aerial decision-making. All these expert technologies have developed a wide spectrum of application for UAVs, beyond the classic military and defense purposes. Unmanned Aerial Vehicles and Computer Vision are common topics in expert systems, so thanks to the recent advances in perception technologies, modern intelligent applications are developed to enhance autonomous UAV positioning, or automatic algorithms to avoid aerial collisions, among others. Then, the presented survey is based on artificial perception applications that represent important advances in the latest years in the expert system field related to the Unmanned Aerial Vehicles. In this paper, the most significant advances in this field are presented, able to solve fundamental technical limitations; such as visual odometry, obstacle detection, mapping and localization, et cetera. Besides, they have been analyzed based on their capabilities and potential utility. Moreover, the applications and UAVs are divided and categorized according to different criteria.This research is supported by the Spanish Government through the CICYT projects (TRA2015-63708-R and TRA2013-48314-C3-1-R)