Formation control of a group of micro aerial vehicles (MAVs)

Coordinated motion of Unmanned Aerial Vehicles (UAVs) has been a growing research interest in the last decade. In this paper we propose a coordination model that makes use of virtual springs and dampers to generate reference trajectories for a group of quadrotors. Virtual forces exerted on each vehicle are produced by using projected distances between the quadrotors. Several coordinated task scenarios are presented and the performance of the proposed method is verified by simulations

Dynamics of Flapping Micro-Aerial Vehicles

[[abstract]]A dynamic-link rule base (DLRB) is introduced to the fuzzy inference systems for the purpose of speeding up and simplifying the fuzzy reasoning. This paper proposes a new reasoning mechanism by adding a dynamic-link rule base between the original rule base and the inference engine. The fuzzy inference system with a dynamic-link rule base is called a dynamic-link-rule-base-fuzzy-inference-system (DLRB-FIS). In the DLRB-FIS, only the fired rules, whose firing strengths are not equal to zero, are included for inference. The mathematical foundations, theorems and architecture of the DLRB-FIS are presented. A numeric example is also given for verifying the practicability of DLRB-FIS. The DLRB-FIS proposed has a general-purpose architecture. Therefore, it can be applied to many kinds of fields, such as fuzzy control, fuzzy image processing, fuzzy decision making, and fuzzy pattern recognition, etc[[conferencetype]]國際[[conferencedate]]20090610~20090612[[iscallforpapers]]Y[[conferencelocation]]St. Louis, US

Motion Planning For Micro Aerial Vehicles

A Micro Aerial Vehicle (MAV) is capable of agile motion in 3D making it an ideal platform for developments of planning and control algorithms. For fully autonomous MAV systems, it is essential to plan motions that are both dynamically feasible and collision-free in cluttered environments. Recent work demonstrates precise control of MAVs using time-parameterized trajectories that satisfy feasibility and safety requirements. However, planning such trajectories is non-trivial, especially when considering constraints, such as optimality and completeness. For navigating in unknown environments, the capability for fast re-planning is also critical. Considering all of these requirements, motion planning for MAVs is a challenging problem. In this thesis, we examine trajectory planning algorithms for MAVs and present methodologies that solve a wide range of planning problems. We first introduce path planning and geometric control methods, which produce spatial paths that are inadequate for high speed flight, but can be used to guide trajectory optimization. We then describe optimization-based trajectory planning and demonstrate this method for solving navigation problems in complex 3D environments. When the initial state is not fixed, an optimization-based method is prone to generate sub-optimal trajectories. To address this challenge, we propose a search-based approach using motion primitives to plan resolution complete and sub-optimal trajectories. This algorithm can also be used to solve planning problems with constraints such as motion uncertainty, limited field-of-view and moving obstacles. The proposed methods can run in real time and are applicable for real-world autonomous navigation, even with limited on-board computational resources. This thesis includes a carefully analysis of the strengths and weaknesses of our planning paradigm and algorithms, and demonstration of their performance through simulation and experiments

Aquatic escape for micro-aerial vehicles

As our world is experiencing climate changes, we are in need of better monitoring technologies. Most of our planet is covered with water and robots will need to move in aquatic environments. A mobile robotic platform that possesses efficient locomotion and is capable of operating in diverse scenarios would give us an advantage in data collection that can validate climate models, emergency relief and experimental biological research. This field of application is the driving vector of this robotics research which aims to understand, produce and demonstrate solutions of aerial-aquatic autonomous vehicles. However, small robots face major challenges in operating both in water and in air, as well as transition between those fluids, mainly due to the difference of density of the media. This thesis presents the developments of new aquatic locomotion strategies at small scales that further enlarge the operational domain of conventional platforms. This comprises flight, shallow water locomotion and the transition in-between. Their operating principles, manufacturing methods and control methods are discussed and evaluated in detail. I present multiple unique aerial-aquatic robots with various water escape mechanisms, spanning over different scales. The five robotic platforms showcased share similarities that are compared. The take-off methods are analysed carefully and the underlying physics principles put into light. While all presented research fulfils a similar locomotion objective - i.e aerial and aquatic motion - their relevance depends on the environmental conditions and supposed mission. As such, the performance of each vehicle is discussed and characterised in real, relevant conditions. A novel water-reactive fuel thruster is developed for impulsive take-off, allowing consecutive and multiple jump-gliding from the water surface in rough conditions. At a smaller scale, the escape of a milligram robotic bee is achieved. In addition, a new robot class is demonstrated, that employs the same wings for flying as for passive surface sailing. This unique capability allows the flexibility of flight to be combined with long-duration surface missions, enabling autonomous prolonged aquatic monitoring.Open Acces

PAC: A Novel Self-Adaptive Neuro-Fuzzy Controller for Micro Aerial Vehicles

There exists an increasing demand for a flexible and computationally efficient controller for micro aerial vehicles (MAVs) due to a high degree of environmental perturbations. In this work, an evolving neuro-fuzzy controller, namely Parsimonious Controller (PAC) is proposed. It features fewer network parameters than conventional approaches due to the absence of rule premise parameters. PAC is built upon a recently developed evolving neuro-fuzzy system known as parsimonious learning machine (PALM) and adopts new rule growing and pruning modules derived from the approximation of bias and variance. These rule adaptation methods have no reliance on user-defined thresholds, thereby increasing the PAC's autonomy for real-time deployment. PAC adapts the consequent parameters with the sliding mode control (SMC) theory in the single-pass fashion. The boundedness and convergence of the closed-loop control system's tracking error and the controller's consequent parameters are confirmed by utilizing the LaSalle-Yoshizawa theorem. Lastly, the controller's efficacy is evaluated by observing various trajectory tracking performance from a bio-inspired flapping-wing micro aerial vehicle (BI-FWMAV) and a rotary wing micro aerial vehicle called hexacopter. Furthermore, it is compared to three distinctive controllers. Our PAC outperforms the linear PID controller and feed-forward neural network (FFNN) based nonlinear adaptive controller. Compared to its predecessor, G-controller, the tracking accuracy is comparable, but the PAC incurs significantly fewer parameters to attain similar or better performance than the G-controller.Comment: This paper has been accepted for publication in Information Science Journal 201

Real-time model-based video stabilization for microaerial vehicles

The emerging branch of micro aerial vehicles (MAVs) has attracted a great interest for their indoor navigation capabilities, but they require a high quality video for tele-operated or autonomous tasks. A common problem of on-board video quality is the effect of undesired movements, so different approaches solve it with both mechanical stabilizers or video stabilizer software. Very few video stabilizer algorithms in the literature can be applied in real-time but they do not discriminate at all between intentional movements of the tele-operator and undesired ones. In this paper, a novel technique is introduced for real-time video stabilization with low computational cost, without generating false movements or decreasing the performance of the stabilized video sequence. Our proposal uses a combination of geometric transformations and outliers rejection to obtain a robust inter-frame motion estimation, and a Kalman filter based on an ANN learned model of the MAV that includes the control action for motion intention estimation.Peer ReviewedPostprint (author's final draft

Micro Aerial Vehicle (MAV): An Aerodynamics Optimized Design

Unmanned Aerial Vehicles (UAV) have played more and more important roles, not only in military, but also in civil applications nowadays. UAV differs than normal aircraft due to different aerodynamic characteristics. Studying UAV aerodynamic leads to an optimized successful design. Micro Aerial Vehicles (MAV) are smaller versions of UAV, with lower Reynolds number and lower weight. The purpose of this project is to perform a technical study on aerodynamic characteristic of Micro Aerial Vehicles (MAV) at different Angles of Attack (AOA) and with varying wing shapes and areas if necessary. The background study consists of understanding of Unmanned Aerial Vehicles (UAV), Micro Aerial Vehicles (MAV), forces of flight, lift and drag coefficient. The literature review summarizes research of UAV problems since the invention, optimization of aerodynamic characteristic and numerical testing carried out on aircraft. The methodology planned consists of numerical testing, which is performed by using Computational Fluid Dynamic simulation using the commercial code FLUENT. MAV Design 2 shows improved CL, Lift Coefficient compared to MAV Design 1. The finer meshing shows more accurate result as compared to coarser and uniform meshing. For MAV Design 2 and MAV Design 3, as Angle of Attack increases, CL increases. This shows the same CL versus Angles of Attack relation as the published data. Hence the results show reasonable predictions of lift and drag coefficients