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In the present study, we propose a trajectory optimization and replanning algorithm for micro air vehicles (MAVs) in cluttered environments. To generate the path of an MAV in a cluttered environment, we first design an offline global path optimization algorithm. This algorithm generates a global trajectory for safe aerial delivery; this trajectory enables an MAV to avoid static obstacles marked in the navigation map and satisfies the MAV's initial and arrival velocities. The MAV's trajectory is replanned by exploiting dynamic movement primitives (DMPs) and a time adjustment algorithm to enable computationally efficient unknown obstacle avoidance in local path planning. To validate the applicability of the proposed algorithm, we compare simulation results with those obtained using an existing approach based on DMPs. Furthermore, an autonomous flight is demonstrated in an outdoor environment using a custom-made MAV driven by the proposed approach.

The stability of flying of a hummingbird-like flapping-wing micro air vehicle (MAV) has been challenging. In this paper, experimental studies are reported on the tail shapes of hummingbird-like flapping-wing MAVs, since tails play an important role in-flight stability. Dynamics parameters of hummingbird tails are firstly studied and evaluated. Then man-made tails inspired by the natural hummingbirds are designed, manufactured and optimized for experimental tests. The results show that lift generated by the tail is independent of a fan angle, whereas the pitch moment is related to the fan angle. Further, the tail can be applied to stabilising hovering twin-wing flapping wing MAVs.

Recent progress in flapping-wing micro air vehicles has led to the design of the bird-like main body of a flapping-wing micro air vehicle. Inspired by the Calliope hummingbird, the main body of the flapping-wing micro aerial vehicle is designed, and modifications were made to house all the commercially available components inside the main body such as a battery, motor and camera. All the components were placed inside and the center of gravity position of the main body of bio-inspired MAV was determined. Steady and unsteady computational analysis of the main body was carried out at body angles of 8, 14, 20, 28, 38, 50, 70 and 90 degrees. The effect of change in forward velocity on aerodynamics was also studied at the body angle of 28 degrees, where the lift to drag ratio is maximum. Flow physics analysis made at different body angles shows that flow starts to separate after 50 degrees and stall accurs. Bell and semi S-shaped curves are formed when the coefficient of lift and Drag are plotted against the body angle, respectively.

Hoverable Flapping Wing Micro Air Vehicle (FWMAV) is a new concept aircraft which imitates the flight mode of hummingbirds or insects. It has excellent mobility, flexibility, concealment, and can demonstrate hovering, forward flight and other maneuvers, which has attracted wide attention. In this research, a prototype of hoverable FWMAVs is designed and built for the research of control method, and the research on flight attitude estimation and attitude control algorithm are carried out. The hardware devices such as flight control board and remote control are designed and developed independently, also we devised the software control system to realize the communication between software and hardware, then the algorithm is verified on the experimental platform. The prototype of the aircraft weighs 27.24g, and can take off under the flapping frequency of 20Hz. On the experimental platform, relying on the hardware control circuit and software control algorithm, it can realize the attitude control of roll and pitch, and demonstrate hovering flight for two minutes.

To investigate aerodynamic performance of Flapping Wing Micro Air Vehicle (FWMAV), it is significant to design an efficient test system for measuring aerodynamic performance of FWMAV in hovering condition. In this study, a test platform with a simple trigger device, which can synchronously obtain data including forces, wing motion and power consumption under a certain flight status of uncontrolled FWMAV, was introduced. Time-resolved forces generated by wing flapping were measured by an ATI force transducer and the wing kinematics was tracked by a high-speed camera. A customed circuit was designed for power consumption. For simple experimental operation, a low- level signal was used to trigger our system for data acquisition, data processing and data storage. A flapper with 20cm wing span, which weighs 9.37g without batteries, was tested to characterize its aerodynamic performance through this platform. Test results show that the wing flapping frequency of the flapper can almost reach 13Hz at the flapping angle of 180° producing 8.1g maximum mean lift and 0.27g maximum mean thrust, which can provide valuable references for performance improvement and validation of aerodynamic modelling of FWMAV.

This paper presents an online local trajectory planning algorithm for multirotor micro air vehicle(MAV) which applies the motion primitive generator proposed by Meuller et al. in RRT* algorithm to extend new nodes. The obtained results satisfy the dynamic constraints of multirotor MAV and avoid obstacles. Given initial state and final state, the motion primitive generator proposed by Meuller et al. generates a segment of polynomial trajectory to minimize the jerk of the trajectory. If initial state and final state are not feasible, the calculated motion primitive will not satisfy with the multirotor multirotor micro air vehicle dynamics. To resolve this problem, we use the numerical fitting method to obtain a mapping relation which gives the feasible range of the final state of the corresponding initial state. In the operation, such mapping can be called directly. Meanwhile, the trajectory is modified to decrease the velocity and acceleration of the end state of the whole trajectory as much as possible, so as to reduce the risk of colliding with obstacles not considered in the planning. Finally, we verified the effectiveness of the algorithm in the simulation environment.

The basis for properly verified R&D works is to provide reliable prototyping tools at three most important stages: computer simulation, laboratory tests and real-world experiments. In the laboratory-limited conditions, particular importance is attributed to the first two stages, especially in the context of the safety development of autonomous flights of unmanned aerial vehicle (UAV) groups in various missions. The open-source framework Aerostack support those needs and its effectiveness has been proven in the International Micro Air Vehicle Indoor Competitions (IMAV 2013, 2016, 2017) and Mohammed Bin Zayed International Robotics Challenge (MBZIRC 2020). In the paper, the exemplary functionalities for the new version of Aerostack Version 3.0 Distribution Sirocco (Aerial robotics framework for the industry), extended additionally with a library of new behaviors, are presented. The mission of UAVs can be developed fast and effectively in order to conduct test flights with real drones in lab, before one will decide to fly autonomously outdoor. The representative results obtained for low-cost AR.Drone 2.0 UAV models in two missions, are presented. The first mission is autonomous patrolling the area by a pair of UAVs, the second – intercepting the intruder in guarded area by the guard UAV.

Flying birds, bats and insects have always inspired humans to venture into developing bio-inspired flying machines. Such vehicles imitate flapping wing concept are also known as Ornithopters. Efforts to build such vehicles date back to 9th-century work by famous Arab scientist Abbas-Ibn-Firnas. Despite many efforts, design and development of such vehicle remained a challenge. The major limitation in this regard is the limited knowledge of related aerodynamic concepts like, Low-Reynolds number phenomenon, vortex induced lift, non-linear fluid structure interaction, high flapping frequency, low weight, complex mechanics, stabilization and controls algorithms. This review paper is aimed to provide an insight into recent developments in understanding the complex dynamics of flapping wing vehicles. Notable experimental works and numerical investigations have been discussed in details; special emphasis has been laid on computational techniques for analysing complex dynamics and design of flapping wing vehicles. Moreover, the modern trends in design, power requirement, motion control and mathematical modelling are also explored.

The Bioinspired Flapping-wing Micro Air Vehicle (FMAV) has been attracting many research institutions due to the advantages they present, such as greater maneuverability, greater efficiency, and lower energy consumption. Compared with the fixed and rotary airfoils, maneuvering characteristics of FMAV depends much upon efficient control of flight attitude. So, an effective model of flight control is of utmost importance. The developments of bioinspired FMAV in recent years is firstly highlighted in the paper, and we specifically discuss the different mechanisms for bioinspired FMAV flight control as well as model-based control and the model-free control. We conjointly define the challenges related to making associate degree integrated bioinspired FMAV.

In todays world, the applications of Unmanned Aerial Vehicle (UAV) systems are leaping by extending their scope from military applications on to commercial and medical sectors as well. Owing to this commercialization, the necessity to append external hardware with UAV systems becomes inevitable. This external hardware could aid in enabling wireless data transfer between the UAV system and remote Wireless Sensor Networks (WSN) using low powered architecture like Thread, BLE (Bluetooth Low Energy). The data is being transmitted from the flight controller to the ground control station using MAVLink (Micro Air Vehicle Link) protocol. The ideal aim of this research is to address the issues of integrating different hardware with the flight controller of the UAV system using a light-weight protocol called UAVCAN. This approach would result in reduced wiring and would harness the problem of integrating multiple systems to UAV.

In this work a one dimensional micro air vehicle model with flexible wings incorporating spatial hysteresis internal damping is analyzed. The control initial value problem is shown to be well-posed and the first order system operator of the linearized system is shown to generate a strongly continuous contraction semigroup. The importance of a theoretical foundation is generally understated in science and this work provides any numerical results from this model with a rigorous foundation. The theoretical analysis of control design for the model in this work provided motivation for and makes use of a recently published inequality that is easy to understand yet was quite non-trivial to prove.

Increased demand for undetected surveillance along with data sensing and gathering capabilities has encouraged the researchers to explore new avenues in micro and nano air vehicle technology. Successful employment of fixed and rotary wing micro air vehicles in a vast array of roles have encouraged the scientists to develop bio-inspired flapping-wing micro air vehicles hence surpassing them in hovering and sharp turning at low-speed capabilities. These vehicles mimic birds such as hummingbird and many more. Their wings are constrained from the root; flexible nature of their material inhibits deformations and torsion during the flapping motion. Static deflections may lead to buckling or loss of lift due to higher torsion angles. Moreover, the cyclic motion of the wings may lead to material degradation resulting in an increased twist. This effects their aerodynamic performance and in the worst case may lead to fatigue failure. Therefore, it is of paramount importance to design the flexible wing with appropriate stiffness distribution for deformation, which may result in desirable aerodynamic and structural performance. Material selection for the flapping-wing unlike fixed wings is complex in nature due to the anisotropic property of reinforcement members and membranes used in the design. In this research, a comparison of different wing designs has been carried out using commercial finite element analysis software. In the first step, different wings of varying stiffness structure patterns of a Zimmerman planform (mimicking the humming-bird) have been designed. They consist of thin flexible carbon fiber and latex rubber membranes of varying strengths. Wing designs of different membrane materials and structural reinforcement patterns have been analyzed and compared in this work. Four such wings with an aspect ratio of 7.65 (wing length and root chord of 75 mm and 25 mm root, respectively) have been designed with different placement patterns of stiffness battens. This paper focuses on the stress, strain, deformation and modal analysis of the wings of various design configurations as a reaction to static forces applied at leading edge tip of the wing. After the analysis of the obtained results, the thickness of reinforced material has been decreased to three-fifth and one-fifth of the original thickness. The refinement in wing design has enabled the selection of the optimum design for given models and materials. The results show that Spread Batten Wing made of Capran membrane, with batten and membrane thickness of 0.2mm and 0.014mm is better amongst the designed wings to handle a load of 6g at higher operating frequencies (>100Hz). Whereas at lower operating frequencies (

Food security is one of the societal challenge topics. As one-third of all food consumed by humans relies on animal pollination currently, this research provides an emerging solution to food supply reduction caused by population shrinking of natural pollinators, so as to reduce its impact on ecological relationships, ecosystem conservation and stability, genetic variation in the crop plant community, floral diversity, specialisation and evolution. This paper develops a conceptual technical roadmap of autonomous pollination for future farming using robotic micro air vehicle pollinators (MPrs). The research provides new insights into autonomous design and manufacture and into possible ways to increase the production efficiency which shortens the time from lab to market. The autonomous MPrs are realized using artificial intelligence and human expertise in the loop for smart agricultural industry. Further, this work identifies scientific and technological advances that are expected to translate, within proposed regulatory frameworks, into the pervasive use of MPrs for agricultural applications and beyond.

In this work, we wish to improve upon previous models describing the damping mechanisms within composite, flexible wing micro aerial vehicles. We are primarily interested in the integration of spatial hysteresis damping, a non-local internal form of damping into our Euler Bernoulli cantilevered beam model. This internal damping model has been proposed as more accurate within such structures. We then experimentally validate the inclusion of this form of damping into our micro aerial vehicle model and show higher agreement with the collected data.

In view of the shortcomings of the previous mechanism design for the flapping wing micro air vehicle(FMAV),such as small flapping angle and low transmission efficiency, a new type mechanism of FMAV is designed in this paper. Large aerodynamic force is generated by the large amplitude flapping angle and high frequency flapping. By combining the new control mechanism, the maneuverability of the aircraft and the load demand during the mission are improved. Meanwhile, this new type mechanism meets the design requirements of the miniaturization and flexibility, and provide the theoretical basis and application value for the mission of the aircraft to carry out the border patrol, the disaster rescue and the reconnaissance missions.

In this paper, we implement a real-time method for incrementally stitching aerial images over a large area. To collect high-quality aerial images, the camera is mounted on a gimbal. Being isolated from the vibration of the drone, the camera can provide relatively stable and less blurry images. To achieve fast aerial mapping, instead of using traditional feature extraction and matching steps, this algorithm only relies on camera position and orientation for blending. The mosaic is fused and incrementally updated based on the adaptive weight multi-band blending algorithm. The weight matrix and Laplacian pyramid for blending are updated adaptively. The orthoimage can be reconstructed from the Laplacian pyramid incrementally. This method was applied in the aerial mapping task of the ninth International Micro Air Vehicle Conference and Flight Competition, achieving a high quality map that contributed to the team's championship in the outdoor competition.

Morphing or shape changing of aircraft, though attractive from aerodynamics point of view, entails additional weight and power penalty and control complexities that pose research challenges. Researchers generally agree with the aerodynamic benefits of morphing and propose Shape Memory Alloy (SMA) actuators for controlled morphing. However, the methods discussed in the literature, generally uses computer based software and data acquisition systems that are suitable for ground based operation. This paper presents an in-flight configurable morphing Micro Air Vehicle (MAV) with four SMA actuated morphing surfaces. As a proof of concept, NAL’s 300mm MAV airframe was modified to provide deflectable leading edges and trailing edges on both left and right side wing of the MAV. These four morphing surfaces could be actuated independently. The paper also presents the weight reduction methods employed to achieve a weight budget of less than 6g per morphing system. The power optimization using staggered PID-PWM control algorithm has reduced the peak battery current drawn by four SMA actuators (apparently operating together) to that of a single SMA. Typical rate of 4.4°/s for actuating the morphing segment and 2.6°/s for deactivating the same have been achieved at a control accuracy of ±0.5°.

This study presents principle of the camber-flat wing structure conversion mechanism, which is inspired by a dragonfly, and its applicability to MAV. The camber-flat wing structure convert mechanism makes MAV flight using asymmetric flapping pattern through control of angle of attack without complicate structure. This mechanism was inspired from the dragonfly's feature that the camber structure of the wing increases the rigidity of wing structure and makes dragonfly has asymmetric flapping pattern. Experimental results show that MAV has asymmetric flapping pattern that can more stable flight performance when hovering flight with a camber structure and superior performance when the forward flight with a flat structure. The average lift force in the camber wing structure was 0.02N, the average thrust force was 0.02N and the average lift force was 0.011N in the flat wing structure at 20 Hz flapping frequency.

Planning a feasible and flyable path for a micro air vehicle (MAV) operating in a dynamic and cluttered environment is a very canonical and challenging problem in the field of robotics. And there is a variety of exploration in this area. Closed-loop rapidly-exploring random tree (CL-RRT), as a typical randomized algorithm considering the dynamic constraints and instantaneity, has made key contributions to build a real-time planning framework. This paper builds upon these efforts and gives depth discussions on the implementation plans of CL-RRT based motion planning algorithm in a complex closed environment. Simulation examples involving a quadrotor are presented and discussed.

The design of a flight control for a micro air vehicle type quadrotor is presented. The control scheme is based on a smooth differentiator combined with an adaptive sliding mode controller. The observer provides a smooth and accurate estimation of the full state from partial measurements of state in spite of noise. The adaptive controller guarantee tracking of the desired trajectories against external perturbations. Thus, it can get a smooth measurement helping to adequately calculate the control law and adaptive gains of the controller allows to use a minimal gain sufficiently to keep the sliding mode, but large enough to reject perturbation as they appear. The methodology shows the effectiveness and performance via numerical simulations.

In this paper, we present a modular mission management system for micro aerial vehicles (MAVs), with a successful application in real environments with the actual platform. We use the behaviour tree as the main underlying structure to represent and further execute the complex mission plan. The proposed system is highly modular, allowing the user to specify different missions by simply grouping various mission-independent sub-behaviours together. The paper also describes a set of commonly used behaviours for MAVs. The proposed system has been applied to the 2017 International Micro Air Vehicle Competition (IMAV 2017) and has helped the team win both indoor and outdoor championships.

In our current society, Unmanned Aerial Vehicles (UAVs), also known as drones have become common. These vehicles are controlled remotely from a Ground Control Station (GCS) or autonomously by a programmed mission. Data-link between drone and GCS can be established by using a communication protocol. The Micro Air Vehicle link (MAVLink) protocol is an open source, point-to-point networking protocol used to carry telemetry and to command and control many small unmanned aircrafts. In this paper, communication requirements, such as network latency and data loss of the MAVLink protocol have been analyzed.

Hovering flight is one of the valuable capabilities for unmanned aerial vehicles (UAVs) to perform various missions. It is easily attainable by rotary-wing aircrafts such as a helicopter and multirotor. This flight maneuver, however, remains a significant challenge to a flapping wing micro air vehicle (FWMAV). This paper aims to regulate attitude angles of a FWMAV and maintain an altitude during hovering. For this task, we model dynamic equations of FWMAV to analyze the stability of longitudinal dynamics under the influence of tail wings. In order to examine the effect of the flapping wings on the fuselage, we carry out measurements on the power train. We develop a FWMAV with two pairs of main flapping wings and tail wings, to achieve stable hovering flight. For autonomous control, PID controllers are designed and onboard sensors are used; 9-degree of freedom (DoF) IMU is for attitude regulation and barometer is for an altitude control. For estimating an altitude, we also apply processing filter. Experimental results show that our strategy can accomplish and sustain autonomous hovering of a FWMAV.

Currently, fusion algorithms for visual and inertia data have become a research hotspot in the field of computer vision and robotics. Compared to loosely coupled method, the tightly coupled one can obtain higher accuracy and robustness. In this paper, a tightly coupled visual-inertial system is implemented based on Multi-State Constraint Kalman Filter (MSCKF) [1] considering both accuracy and efficiency in order to navigate a micro air vehicle (MAV) in GPS-denied indoor environment. Firstly, a C++ implementation of the standard MSCKF with a feature matching front-end is realized. Then a new feature tracking front-end based on modified optical flow is proposed. Experiments show that the new method is faster than the feature matching method and has almost the same accuracy. Finally, a Hybrid-MSCKF algorithm is implemented to reduce the drift. The algorithm is tested on a MAV dataset, and experiments show that the robustness and positioning accuracy of the algorithm reach navigation requirements of indoor MAVs.

Those employing Evolutionary Algorithms (EA) are constantly challenged to engineer candidate solution representations that balance expressive power (I.E. can a wide variety of potentially useful solutions be represented?) and meta-heuristic search support (I.E. does the representation support fast acquisition and subsequent fine-tuning of adequate solution candidates). In previous work with a simulated insect-like Flapping-Wing Micro Air Vehicle (FW-MAV), an evolutionary algorithm was employed to blend descriptions of wing flapping patterns to restore correct flight behavior after physical damage to one or both of the wings. Some preliminary work had been done to reduce the overall size of the search space as a means of improving time required to acquire a solution. This of course would likely sacrifice breadth of solutions types and potential expressive power of the representation. In this work, we focus on methods to improve performance by augmenting EA search to dynamically restrict and open access to the whole space to improve solution acquisition time without sacrificing expressive power of the representation. This paper will describe some potential restriction/access control methods and provide preliminary experimental results on the efficacy of these methods in the context of adapting FW-MAV wing gaits.

In this paper, a fully-autonomous quadrotor aerial robot for solving the different missions proposed in the 2016 International Micro Air Vehicle (IMAV) Indoor Competition is presented. The missions proposed in the IMAV 2016 competition involve the execution of high-level missions such as entering and exiting a building, exploring an unknown indoor environment, recognizing and interacting with objects, landing autonomously on a moving platform, etc. For solving the aforementioned missions, a fully-autonomous quadrotor aerial robot has been designed, based on a complete hardware configuration and a versatile software architecture, which allows the aerial robot to complete all the missions in a fully autonomous and consecutive manner. A thorough evaluation of the proposed system has been carried out in both simulated flights, using the Gazebo simulator in combination with PX4 Software-In-The-Loop, and real flights, demonstrating the appropriate capabilities of the proposed system for performing high-level missions and its flexibility for being adapted to a wide variety of applications.

This paper addresses the modeling, identification and adaptive sliding mode control for a quadrotor micro aerial vehicle. In order to obtain a compact kinematics and dynamics representation, the spatial vectors convention is used, making more efficiently the model computation by reducing the number of algebraic operations. Identification of parameters such as inertia moments, thrust and torque of the motor-propeller with motor features by means of estimation and experimental tests are also included. In addition, a flight control based on a recent adaptive second order sliding mode control is designed. This controller allows to mitigate external disturbances with unknown bounds. Simulation results shows the feasibility and attractiveness of the proposed set up.

In the field of autonomous drone or micro air vehicle (MAV) research, much of the existing literature focuses on novel approaches to MAV automation and navigation. Whilst discovering these new approaches has scientific merit, these works rarely focus on the impact that the deployment of such systems have in terms of the operational time, power consumption or efficiency of the MAV. This work sets out to review the parallel tracking and mapping algorithm (PTAM) as applied to MAV control systems. Through experimentation, the limits of this algorithm are found in an attempt to determine the minimum computational and power requirements for a computer to have, in order to run PTAM effectively. This work demonstrates that it is feasible with current available technology, to operate PTAM on a 5 watt computer by limiting the parameters that add computational overhead to the system.

This paper addresses practical guidelines for propulsion system design and implementation in a quadrotor micro air vehicle. Due to tiny scale of the motor and propellers required by MAVs, information about features of these devices is frequently unavailable. Therefore, the proposal includes selection of devices, parameter identification and electronic power interface in order to control a motor propeller system. From characterization of the system, thrust and torque coefficients as well as the inertia moment of the motor propeller are obtained, allowing to choose a proper propulsion set according to lift requirements. Furthermore, some aspects for implementing a low level control are presented, including a power interface circuit design based on high efficient consumption of energy criteria, where pulse width modulation is used for implementing some control strategy. The proposed practical guidelines are supported by means of the theoretical and experimental test benches.

Flying animals with flapping wings may best exemplify the astonishing ability of natural selection on design optimization by excelling both stability and maneuverability at insect/hummingbird scale. Flapping Wing Micro Air Vehicle (FWMAV) holds great promise in bridging the performance gap between engineering system and their natural counterparts. Designing and constructing such a system is a challenging problem under stringent size, weight and power (SWaP) constraints. In this work, we presented a systematic approach for design optimization and integration for a hummingbird inspired FWMAV. Our formulation covers aspects of actuation, dynamics, flight stability and control, which was validated by experimental data for both rigid and flexible wings, ranging from low to high wing loading. The optimization yields prototypes with onboard sensors, electronics, and computation units. The prototype flaps at 30Hz to 40Hz, with 7.5 to 12 grams of system weight and 12 to 20 grams of maximum lift. Liftoff was demonstrated with added payloads. Flapping wing platforms with different requirements and scales can now be designed and optimized with minor modifications of proposed formulation.

In this paper, image registration of aerial image with reference satellite image is carried out by computing multi-level sub bands using dual tree complex wavelets. Features are extracted from sub bands and the corresponding features are mapped for similarity by SVD methods. The registered images are evaluated for their performance metrics and the improvements in registration process in terms of transformational parameters are evaluated. The registration process proposed is accurate as it identifies the features with six orientations and thus compares the features and performs orientations foe perfect registration. The algorithm is designed to be faster by eliminating false features thus making it suitable for real time applications.

This work introduces the design and controlled hovering-flight test of an insect-like tailless flapping-wing micro air vehicle (FW-MAV). A lightweight flapping mechanism was developed using a combination of four-bar linkage and pulley-string mechanism to achieve a high flapping amplitude of approximately 190°. For attitude control of the FW-MAV without control surface at tail, control moments were generated by adjusting location of the trailing edges at the wing roots for modulation of wing rotation angle in the wings. Measurements by a load cell showed that the control mechanism produces reasonable pitch, roll and yaw moments. Controlled flight with a simple feedback PID controller was enabled by onboard micro gyroscopes sensing the pitch and roll motions of the FW-MAV. Several flight tests demonstrated that the tailless FW-MAV could successfully perform controlled flight for about 40 seconds.

This paper presents a linear time invariant model identification for beetle mimicking flapping wing micro air vehicle (FWMAV) in hovering mode. The identification is facilitated by assuming the wings of the beetle rectangular in shape and the body is considered cylindrical. The main thrust is provided by flapping the wings of the FWMAV whereas the trailing-edges-change mechanism was kept off. The measurable aerodynamic forces and moment's measured data are utilized in the states' reconstruction. The state reconstruction was carried out via the discrete time integration of linear and angular acceleration. Having reconstructed the states, the standard least mean square estimation (LMSE) based estimation approach is used to estimate the system's stability and control derivatives. The results of the estimation are validated with the experimental data, and show the success of the estimation approach.

Recent interest in hobby aerial vehicles and their applications has seen the emergence of quadrotor designs with various form factors, and development of waypoint-based flight controllers such as APM and Pixhawk. Common to these flight controllers is the use of the Micro Air Vehicle Communications Protocol (MAVLink) that allows the flight controller to communicate with a ground control station. However, it is still challenging to experiment with multiple such aerial vehicles concurrently. To this end, we have developed an open software/hardware platform — the University at Buffalo's Airborne Networking and Communications testbed (UB-ANC) — that facilitates rapid development and deployment of multi-drone applications. In this paper, we present the UB-ANC Emulator, an emulation environment to design, implement, and test various applications (missions) involving one or more drones in software, and provides seamless transition to experimentation. We validate the UB-ANC Emulator's output against experimental data collected in a mission with three networked drones to demonstrate its feasibility and accuracy, and we demonstrate its scalability up to 100 drones using a simulated “leader-follower” mission.

This paper presents a novel control method for autonomous-odor-source localization using vision and odor sensing by micro air vehicles (MAVs). Our method is based on biomimetics, which enable highly autonomous localization. Our method does not need any instruction signals, including even global positioning system (GPS) signals. An experimenter just blows a whistle, and the MAV starts to hover, to seek an odor source, and to keep hovering near the source. The GPS-signal-free control based on vision enables indoor/underground use. Moreover, the MAV is light-weight (85 grams) and does not cause harm to others even if it accidentally falls. Experiments conducted in the real world were successful in enabling odor source localization using the MAV with a bio-inspired searching method. The distance error of the localization was 63 cm, more accurate than the target distance of 120 cm for individual identification. Our odor source localization is the first step to a proof of concept for a danger warning system. These results will be applied to the system to enable a safer and more secure society.

This study was undertaken to create an indoor environmental map using a monocular camera based on structure from motion (SfM). Numerous methods on simultaneous localization and mapping (SLAM) have been proposed for 3D map construction used for various types of mobile robots. We used MonoSLAM proposed by Davidson et al., which is a fundamental method using extended Kalman filters (EKFs) for feature tracking and updating with camera position estimation. For this study, we developed our original micro air vehicle (MAV) prototype to obtain time-series images in a wide-range of indoor environments. The MAV prototype has four monocular cameras to actualize rapid omnidirectional sensing and has computer systems for online vision processing. We applied the method to a benchmark dataset and our original dataset, which was obtained using a single monocular camera. Using the benchmark dataset, we confirmed the appearance tendencies and their meaning of three color patches: red, blue, and yellow patches that respectively correspond to correct tracking, failure tracking, and temporary waiting local features. The experimentally obtained results obtained using our dataset reveal detection of numerous yellow patches, a few blue patches, and a few red patches. Moreover, we obtained 3D trajectories of a camera on the MAV as 3D maps.

In this paper, we design an autonomous flight controller for height regulation and bang-bang controller for directional control of a light-weight flapping-wing micro air vehicle (FWMAV) with limited payload. We also present autonomous vision-based target tracking for a FWMAV equipped with a low-cost and light-weight first person view (FPV) camera. We construct a ground station, integrated with control and vision algorithms, which performs the image processing and the computation of control inputs based on the acquired state variables from motion capture system. In addition, we employ a vision algorithm for a low-quality camera to detect a static target with the discussions on the techniques to improve the reliability of visual detection. Experimental results show satisfactory flight performance, achieving the height regulation and directional control, and autonomous vision-based target tracking.

This study was undertaken to establish basic technologies and knowledge of aerial photography and its application to support safe rowing. For the water sport of rowing, managers and coaches use a motorboat to follow a rowing boat for coaching and safe rowing observation. Utilization of a motorboat gives rise to numerous problems in terms of pulled waves, narrow visual ranges, limited tracking of boats at any one time, fuel consumption, and maintenance costs. Moreover, rowing boats present collision risks to other rowing boats or obstacles floating on water, especially for a cox-less rowing boat because the visual direction for rowers is opposite to the moving direction. The aim of this study is to actualize rowing aerial photography using a Micro Air Vehicle (MAV): a radio-controlled small multi-rotor helicopter that has become popularly used for numerous applications recently. We obtained rowing movies using three-camera compositional patterns with changing altitudes and tilt angles. We examined the benefits of rowing aerial photography compared with movies obtained from a motorboat with consideration of safety improvement.

This paper presents a novel object extraction method using a micro air vehicle (MAV) for improving the robustness of occlusion. The proposed method is based on saliency of objects for extracting regions of interest (RoIs) using scale invariant feature transform (SIFT) features and segmentation of target objects using GrabCut, which requires advance learning. We obtained original aerial photographic time-series image datasets using a MAV. Results of experiments revealed that object extraction accuracies measured using precision, recall, and F-measure improved according to the MAV movement for images with changing rates of collusion between two objects: a chair and a table. Especially for images of a chair, which is smaller than the table, our method functioned well for the extraction of object regions. For improving extraction accuracy based on the result to extract the table, an advanced mechanism combined with flight patterns is necessary to adjust the suitable distance between the MAV and a target object.

Previously, we introduced Evolutionary Model Consistency Checking (EMCC) as an adjunct to Evolvable and Adaptive Hardware (EAH) methods. The core idea was to dual-purpose objective function evaluations to simultaneously enable EA search of hardware configurations while simultaneously enabling a model-based inference of the nature of the damage that necessitated the hardware adaptation. We demonstrated the efficacy of this method by modifying a pair of EAH oscillators inside a simulated Flapping-Wing Micro Air Vehicle (FW-MAV). In that work, we were able to show that one could, while online in normal service, evolve wing gait patterns that corrected altitude control errors cause by mechanical wing damage while simultaneously determining, with high precision, what the wing lift force deficits that necessitated the adaptation. In this work, we extend the method to be able to also determine wing drag force deficits. Further, we infer the now extended set of four unknown damage estimates without substantially increasing the number of objective function evaluations required. In this paper we will provide the outlines of a formal derivation of the new inference method plus experimental validation of efficacy. The paper will conclude with commentary on several practical issues, including better containment of estimation error by introducing more in-flight learning trials and why one might argue that these techniques could eventually be used on a true free-flying flapping wing vehicle.

The development of flapping wing micro air vehicles (MAVs) have yielded remarkable progress over the last decades. Achieving high component stiffness is often in conflict with low weight requirement, which is highly desirable for longer flight time and higher payload. Moreover, the vibration originated predominantly from the wings, the gears and the frame excitations, compromises the flapping wing MAV's stability and the fatigue life. In order to improve the vehicle's efficiency and performance, optimization of these various parameters is necessary. In this work, we present the structural optimization of the flapping wing micro air vehicles. Specifically, we focus on the gearbox optimization using Simulia Tosca Structure in Abaqus, which is a robust tool for designing lightweight, rigid and durable components. The various numerical experiments have been conducted towards optimizing the components' topology. The results provide understanding of the optimal design topology for a spur gear and the structural components used in MAVs.

This paper details the design of a nonlinear controller for the Roll&Fly mode of a wheeled tilt-body micro air vehicle (MAV), developed at ISAE-SUPAERO, called the MAVion. The Roll&Fly mode consists in flying while rolling on walls or onto the ground to guide or increase the range of the MAV during detection or inspection missions. It therefore implies wall or ground mechanical interactions that calls for nontrivial control laws. Our approach consists in enabling a nonlinear obstacle-free attitude/height controller to adapt itself to wall interactions. The controller regulates the velocity and attitude of the drone by means of an approach based on backstepping and feedback linearization techniques. The attitude controller is parametrized by quaternion algebra to avoid orientation singularities.