Development of c-means Clustering Based Adaptive Fuzzy Controller for A Flapping Wing Micro Air Vehicle

Advanced and accurate modelling of a Flapping Wing Micro Air Vehicle (FW MAV) and its control is one of the recent research topics related to the field of autonomous Unmanned Aerial Vehicles (UAVs). In this work, a four wing Natureinspired (NI) FW MAV is modeled and controlled inspiring by its advanced features like quick flight, vertical take-off and landing, hovering, and fast turn, and enhanced manoeuvrability when contrasted with comparable-sized fixed and rotary wing UAVs. The Fuzzy C-Means (FCM) clustering algorithm is utilized to demonstrate the NIFW MAV model, which has points of interest over first principle based modelling since it does not depend on the system dynamics, rather based on data and can incorporate various uncertainties like sensor error. The same clustering strategy is used to develop an adaptive fuzzy controller. The controller is then utilized to control the altitude of the NIFW MAV, that can adapt with environmental disturbances by tuning the antecedent and consequent parameters of the fuzzy system.Comment: this paper is currently under review in Journal of Artificial Intelligence and Soft Computing Researc

Ornithopter Trajectory Optimization with Neural Networks and Random Forest

Trajectory optimization has recently been addressed to compute energy-efficient routes for ornithopter navigation, but its online application remains a challenge. To overcome the high computation time of traditional approaches, this paper proposes algorithms that recursively generate trajectories based on the output of neural networks and random forest. To this end, we create a large data set composed by energy-efficient trajectories obtained by running a competitive planner. To the best of our knowledge our proposed data set is the first one with a high number of pseudo-optimal paths for ornithopter trajectory optimization. We compare the performance of three methods to compute low-cost trajectories: two classification approaches to learn maneuvers and an alternative regression method that predicts new states. The algorithms are tested in several scenarios, including the landing case. The effectiveness and efficiency of the proposed algorithms are demonstrated through simulation, which show that the machine learning techniques can be used to compute the flight path of the ornithopter in real time, even under uncertainties such as wrong sensor readings or re-positioning of the target. Random Forest obtains the higher performance with more than 99% and 97% of accuracy in a landing and a mid-range scenario, respectively.Ministerio de Economía y Competitividad MTM2016-76272- R AEI/FEDER,UEMinisterio de Ciencia e Innovación PID2020-114154RB-I00Unión Europea, Horizon 2020, Marie Sklodowska-Curie grant agreement #73492

An Analytical Investigation of Flapping Wing Structures for Micro Air Vehicles

An analytical model of flapping wing structures for bio-inspired micro air vehicles is presented in this dissertation. Bio-inspired micro air vehicles (MAVs) are based on insects and hummingbirds. These animals have lightweight, flexible wings that undergo large deformations while flapping. Engineering studies have confirmed that deformations can increase the lift of flapping wings. Wing flexibility has been studied through experimental construction-and-evaluation methods and through computational numerical models. Between experimental and numerical methods there is a need for a simple method to model and evaluate the structural dynamics of flexible flapping wings. This dissertation's analytical model addresses this need. A time-periodic assumed-modes beam analysis of a flapping, flexible wing undergoing linear deformations is developed from a beam analysis of a helicopter blade. The resultant structural model includes bending and torsion degrees of freedom. The model is non-dimensionalized. The ratio of the system's structural natural frequency to wingbeat frequency characterizes its constant stiffness, and the amplitude of flapping motion characterizes its time-periodic stiffness. Current flapping mechanisms and MAVs are compared to biological fliers on the basis of the characteristic parameters. The beam analysis is extended to develop an plate model of a flapping wing. The time-periodic stability of the flapping wing model is assessed with Floquet analysis. A flapping-wing stability diagram is developed as a function of the characteristic parameters. The analysis indicates that time-periodic instabilities are more likely for large-amplitude, high-frequency flapping motion. Instabilities associated with the first bending mode dominate the stability diagram. Due to current limitations of flapping mechanisms, instabilities are not likely in current experiments but become more likely at the operating conditions of biological fliers. The effect of structural design parameters, including wing planform and material stiffness, are assessed with an assumed-modes aeroelastic model. Wing planforms are developed from an empirical model of biological planforms. Non-linearities are described in the effect of membrane thickness on lift generation. Structural couplings due to time-periodic stiffness are identified that can decrease lift generation at certain wingbeat frequencies

Proceedings of the International Micro Air Vehicles Conference and Flight Competition 2017 (IMAV 2017)

The IMAV 2017 conference has been held at ISAE-SUPAERO, Toulouse, France from Sept. 18 to Sept. 21, 2017. More than 250 participants coming from 30 different countries worldwide have presented their latest research activities in the field of drones. 38 papers have been presented during the conference including various topics such as Aerodynamics, Aeroacoustics, Propulsion, Autopilots, Sensors, Communication systems, Mission planning techniques, Artificial Intelligence, Human-machine cooperation as applied to drones

Integration of aerial and terrestrial locomotion modes in a bioinspired robotic system

In robotics, locomotion is a fundamental task for the development of high-level activities such as navigation. For a robotic system, the challenge of evading environmental obstacles depends both on its physical capabilities and on the strategies followed to achieve it. Thus, a robot with the ability to develop several modes of locomotion (walking, flying or swimming) has a greater probability of success in achieving its goal than a robot that develops only one. In nature, Hymenoptera insects use terrestrial and aerial modes of locomotion to carry out their activities. Mimicry the physical capabilities of these insects opens the possibility of improvements in the area of robotic locomotion. Therefore, this work seeks to generate a bio-inspired robotic system that integrates the terrestrial and aerial modes of locomotion. The methodology used in this research project has considered the anatomical study and characterization of Hymenoptera insects locomotion, the proposal of conceptual models that integrate terrestrial and aerial modes locomotion, the construction of a physical platform and experimental testing of the system. In addition, a gait generation approach based on an artificial nervous system of coupled nonlinear oscillators has been proposed. This approach has resulted in the generation of a coherent and functional gait pattern that, in combination with the flight capabilities of the system, has constituted an aero-terrestrial robot. The results obtained in this work include the construction of a bioinspired physical platform, the generation of the gait process using an artificial nervous system and the experimental tests on the integration of aero-terrestrial locomotion.Conacyt - Becario Naciona