[image] [image] Ressource protégée - Inria - Institut national de recherche en sciences et technologies du numérique [Accéder directement au contenu](#)

[image] [image]

* [Documentation](#)

* FR
  Français (FR)

* Anglais (EN)
  Se connecter

* [Portail HAL Inria](#)

* Recherche
  Loading...

## Recherche avancée

Information de documents

Titres

○ Titres

○ Sous-titre

○ Titre de l'ouvrage

○ Titre du volume (Série)

○ Champ de recherche par défaut (multicritères) + texte intégral des PDF

○ Résumé

○ Texte intégral indexé des documents PDF

○ Mots-clés

○ Type de document

○ Sous-type de document

○ Tous les identifiants du document

○ Identifiant HAL du dépôt

○ Langue du document (texte)

○ Pays (Texte)

○ Ville

○ À paraître (true ou false)

Ajouter
# Smart Agriculture Using Flapping-Wing Micro Aerial Vehicles (FWMAVs)

Shaik Abdullah, Priyasha Appari, Srihari Rao Patri, Srinivas Katkoori

► To cite this version:

Shaik Abdullah, Priyasha Appari, Srihari Rao Patri, Srinivas Katkoori. Smart Agriculture Using Flapping-Wing Micro Aerial Vehicles (FWMAVs). 4th IFIP International Internet of Things Conference (IFIPIoT), Nov 2021, Virtual, Netherlands. pp.32-47, 10.1007/978-3-030-96466-5_3. hal-04471536

HAL Id: hal-04471536

https://inria.hal.science/hal-04471536v1

Submitted on 21 Feb 2024

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
## Auteur

### Auteur (multicritères)

○ Auteur (multicritères)

○ Auteur : Nom complet

○ Auteur : Nom de famille

○ Auteur : Prénom

○ Auteur : Complément de nom, deuxième prénom

○ Auteur : Organisme payeur

○ Auteur : IdHal (chaîne de caractères)

○ Auteur : Fonction

○ Auteur : personID (entier)

○ Auteur : Identifiant de l'organisme payeur

○ Auteur : Identifiant de la structure

○ Directeur de thèse

○ Éditeur

○ Éditeur scientifique

○ Éditeur de la série

## Ajouter

### Structure

### Structure (multicritères)

○ Structure (multicritères)

○ Structure : Acronyme

○ Structure : Nom

○ Structure : Code

○ Structure : Pays

○ Structure : Type

○ Structure : État dans le référentiel

○ Structure : Identifiant HAL de la structure

○ Structure/équipe de recherche : Pays

○ Structure/regroupement d'équipes : Pays

○ Structure/laboratoire : Pays

○ Structure/regroupement de laboratoires : Pays

○ Structure/institution : Pays

○ Structure/regroupement d'institutions : Pays

## Ajouter
<figure><img src="image_2.png" /></figure>

ifip

International Federation for Information Processing

This document is the original author manuscript of a paper submitted to an IFIP
conference proceedings or other IFIP publication by Springer Nature. As such, there
may be some differences in the official published version of the paper. Such
differences, if any, are usually due to reformatting during preparation for publication or
minor corrections made by the author(s) during final proofreading of the publication
manuscript.
Autres

Champ de recherche par défaut (multicritères)

○ Champ de recherche par défaut (multicritères)

○

○ Revues (multicritères)

○ Revue : Éditeur

○ Revue : Titre abrégé

○ Revue : Titre

○ Revue : Identifiant interne

○ Revue : Date de début de publication

○ Revue : ISSN électronique

○ Revue : ISSN

○ Revue : Éditeur

○ Revue : Couleur dans SHERPA/RoMEO

○ Revue : État dans le référentiel

○

○ Colloque (multicritères)

○ Colloque : Titre

○ Colloque : Organisateur

○ Colloque : date de début (Année)

○ Colloque : date de fin (Année)

○ Conférence invitée (oui/non)

○

○ Projets ANR (multicritères)

○ Projet ANR : Acronyme

○ Projet ANR : Acronyme du programme

○ Projet ANR : Code décision (référence)

○ Projet ANR : Nom

○ Projet ANR : Identifiant interne

○ Projet ANR : État dans le référentiel

○

○ Projets européens (multicritères)

○ Projet européen : Acronyme

○ Projet européen : Identifiant de l'appel à projet

○ Projet européen : Référence

○ Projet européen : Nom

○ Projet européen : Date de fin

○ Projet européen : Financement

○ Projet européen : Date de début
# Smart Agriculture using Flapping-Wing Micro Aerial Vehicles (FWMAVs)

Shaik Abdullah<fnref n="1" />, Priyasha Appari<fnref n="1" />, Srihari Rao Patri<fnref n="1" />, and Srinivas Katkoori<fnref n="2" />

**Abstract.** One of the main problems in smart agriculture is smart artificial pollination with Micro Aerial Vehicles (MAVs). With their small body size and dimensions, they hold the promise of substituting insect pollinators. We propose an improvised version of the previously designed insect-scale flapping-wing micro air vehicles (FWMAVs), namely Harvard RoboBee-like two-winged robot and the four-winged USC Bee+. This version attempts to solve the critical issue of low controllability observed in the previous prototypes which render them inefficient for practical applications such as artificial pollination. We reduce the number of actuators to just one and eliminate the dependency of pitch, roll, and yaw on the wing-movement by controlling the system with weight shifting mechanism controlled by electromagnets. We successfully validate the proposed design in Gazebo simulations. The proposed design requires less material and low manufacturing cost due to simpler design and low number of required parts. To the best of our knowledge, this design is the first of its kind which is a fully functional FWMAV suitable for artificial pollination.

**Keywords:** Aerial Robotics, Microrobotics, Actuators, Internet-of-Things (IoT), Smart Farming, Smart Agriculture

## 1 Introduction

Bees and humming birds have always been the center of attention when it comes to fine controllability and precision. To replicate an automated model of them would be to construct a perfection incarnate aerial vehicle. The miniature size of the robot would enable applications such as artificial pollination, disaster management, search and rescue operations, etc.

Harvard RoboBee [2] and USC Bee+ [3] are the most mature designs in the literature, however, there are still challenges in terms of their stability and controllability as they depend heavily on an actuator for each wing. Each wing

<footnote n="1">¹ ECE Department, National Institute of Technology Warangal, Warangal, Telangana, India, 506004
{shaik.841972, appari.951939}@student.nitw.ac.in, patri@nitw.ac.in</footnote>

<footnote n="2">² CSE Department, University of South Florida, Tampa, FL, USA, 33620
katkoori@usf.edu</footnote>
○ Projet européen : État dans le référentiel

○ Projet européen : Identifiant interne

○

○ Date de production : année

○ Date de mise en ligne : année

○ Date de publication : année

○ Date d'écriture : année

○ Date de modification du dépôt : année

○ Date de dépôt : année

○ Date de publication électronique : année

○

○ Collection HAL (multicritères)

○ Collection HAL : catégorie

○ Collection HAL : Code

○ Collection HAL : Nom

○ Collection HAL : Identifiant interne

○

○ Identifiant interne du contributeur/déposant

○ Nom complet du contributeur/déposant

○

○ Domaines

○ Domaine primaire

○ Domaine racine

○ Sous-domaine niveau 1

○ Sous-domaine niveau 2

○ Sous-domaine niveau 3

○

○ Statut du document

○ Version du document

○ Type de dépôt

○ Type de document

○ ISBN

○ Numéro - référence

○ Identifiant DOI

○

○ Classification

○ Audience

○ Vulgarisation

○ Comité de lecture - texte (oui ou non)

○ Actes de colloque
is flapped at different frequency in order to cause an imbalance in equilibrium thus making the robot move in desired direction. Since these frequencies have to be changed at millisecond time scale, having high precision control is a big challenge.

We aim to solve this problem by using a single actuator and eliminating the dependence on wings alone for maneuvering. Instead we propose a model which changes direction by a weight shifting mechanism. The model would have both wings beating at same frequency and would determine the thrust of the body. The roll and pitch would be determined by a tiny weight attached to the robot at the bottom which would be fixed with a planar joint. This is similar in structure to the first of this type which was designed and termed the HMF [1]. We developed the model in ROS using python and then validated with simulations in Gazebo software.

The proposed solution will be easy to implement in a smart agriculture system for not only artificial pollination, but also various other applications such as plant health monitoring, etc. This model is simpler in structure, more robust and less power hungry compared to its counterparts in [2] and [3].

The rest of the paper is organized as follows. Section 2 motivates using IoT for smart agriculture. Section 3 presents the proposed vision of using beebots for smart pollination. Section 4 proposes the beebot design. Section 5 provides the design details. Section 6 reports the experimental results. Finally, Section 7 draws conclusions and outlines future work.

## 2 Smart Agriculture using IoT - Related Work

Smart agriculture is an emerging concept that helps in managing farms using modern technologies to increase the quantity and quality of products while optimizing the human labor required. It is a big leap from traditional farming as it brings certainty and predictability to the table. The application of Internet-of-Things (IoT) to agriculture could be a life-changer for humanity and the whole planet.

On farms, IoT devices can measure all kinds of data remotely and provide this information to the farmer in real time. It is capable of providing information about agriculture fields which can be acted upon by the farmer. This paper aims at making use of the evolving IoT technology for enabling smart agriculture with automation. Together, they will be paving the way for what can be called a Third Green Revolution. The most common IoT applications in smart agriculture are sensor-based systems for monitoring crops, soil, fields, livestock, storage facilities, etc.

Currently, we witness how extreme weather, deteriorating soil, drying lands, and collapsing ecosystems make food production more and more complicated and expensive. It is projected that there will be more than 9 billion people inhabiting earth by 2050 which necessitates large scale food production.

Smart agriculture based on IoT is focused on helping farmers close the supply demand gap, by ensuring high yields, profitability, and protection of the environ-
○ Référence interne

○ Financement

○ Collaborations

Ajouter

Lancer la recherche
Recherche experte (SolR)

Recherche experte (SolR)

Lancer la recherche
Vers la recherche avancée

• Déposer

Portail INRIA.HAL.SCIENCE
Les publications, logiciels... des scientifiques Inria

• Accueil

• Publications Inria

○ Consulter les derniers dépôts

○ Par équipe de recherche

○ Par auteur

○ Rapports de recherche et techniques

○ Livres blancs

○ Rapports d'activité des équipes

○ Statistiques

• Boite à Outils

○ X2Hal : importer par lot

○ Logiciels : créer le Codemeta.json

○ Créer mon IDHal

○ Créer mon CVHal

○ Haltools : créer ma page de publications

○ Haltools : extraire des données (réservé Inria)

○ Les bonnes pratiques

■ Comment bien s'affilier ?
ment. Monitoring environmental conditions is the major factor to improve yield of the efficient crops. Therefore, smart farming has a real potential to address all the needs in a safe, environmentally friendly, and resource-efficient approach. Moreover, it is a true way to scale down on the use of pesticides and fertilizers. This allows producing a cleaner and more organic final product compared to that from traditional agricultural methods. The ultimate result from this automated smart farming process would be high precision and control, eventually leading to considerable savings in all key resources.

Sushanth and Sujatha [6] propose a system that responds to the user input with the help of wireless communication (3G/4G). The system has a duplex communication link based on a cellular-Internet interface programmed through an android application that can be used to communicate with the beebot.

Multiple projects in the area of smart agriculture using IoT have been reported in the literature. In [7] algorithms were developed for predicting and monitoring plant health. [8] proposes a network of sensors for smart irrigation. [9] proposes better image acquisition algorithms based on cloud computing developed to solve problem of blurred images taken by mobile robots involved in smart farming. All of these works assert to the bright future of smart agriculture and IoT being the solution to many agricultural problems.

## 3 Smart Agriculture using Beebots - Proposed Vision

Agriculture is the one of the largest sources of livelihood in India. It plays a crucial role in the economy. But there are some challenges being faced by the farmers. There is increasing pressure from climate change, soil erosion, biodiversity loss, fulfilling the demand and dealing with farming —plants, pests and diseases. Smart farming can solve some of these problems and allows farmers to grow crops in a more controlled and productive manner.

Research has shown that the presence of wild bees increases yields across many types of crops. The vast majority of plant species—almost 90 percent, in fact—rely on pollinators to reproduce. This means bees are responsible for one out of every three bites of food we eat. As pollinators, bees play a part in every aspect of the ecosystem. But there is a decline in bee population. There are many factors that contribute to pollinator decline — most of which are related to the climate crisis. Pesticides, fertilizers, parasites, biodiversity loss, deforestation, changes in land use, and habitat destruction are just a few of the reasons.

According to [10] and [11], pollination by honeybees and wild bees significantly increased yield quantity and quality on average up to 62 percent, while exclusion of pollinators caused an average yield gap of 37 percent in cotton and 59 percent in sesame. When we combine that with artificial pollination methods, efficiency in crop yield is bound to increase significantly.

This paper discusses the framework for next generation farming based on a combination of precision agriculture and robotic systems. The work focuses on using robotic bees in agriculture. To the best of our knowledge, this work is first of its kind on the application of Flapping-Wing Micro Aerial Vehicles
■ Quelle version puis-je déposer ?

■ Choisir le bon « type de document »

■ Mettre en valeur ses publications

○ Les tutos IES Inria

■ Lier les notices dans HAL

■ Umbrhal Inria pour l'accessibilité

■ Personnaliser son CVHAL

■ Créer et Personnaliser sa collection

• Documentation

○ Aide en ligne HAL

○ Aide en ligne Haltools

○ Aide en ligne X2hal

○ Tutoriels video HAL

○ Documentation API HAL

○ Ajouter des vignettes

○ Identifiant auteur IdHAL et CV

○ Compléter son dossier ORCID avec ses dépôts HAL

○ Inria et la Science Ouverte

X

X

X

Loading...

X

Le document que vous demandez est en access restreint
jusqu'au 2025-01-01 !

Revenir a la notice

Retourner à l'accueil

Contact

•

Ressources
(FWMAVs) in artificial pollination. The beebot can be equipped with a structure enabling artificial pollination, equipped along with the database of the farm, helping far more efficient form of pollination than hand pollination or actual bees. Futuristically, as suggested in [6] the beebot can be further equipped with visual sensors and converted into an IoT, which accesses the online database, helping to generate an outcome value/expected yield, health parameters of each plant in the farm, etc., as illustrated in Fig. 1. Beebots can be released in a desired area which are constantly connected as IoT devices to a common server for instructions and access to cloud computing, database, etc.

This work identifies problems of system adaption, usability, and feasibility, health of plants and users and energy consumption. The precise implementation and use of the model will make agriculture more profitable, efficient, environmental friendly and help make the most appropriate decisions.

<figure><img src="image_3.png" /><figcaption>Fig. 1: A Beebot based IoT system for artificial pollination</figcaption></figure>

In [4], a clear picture and mechanism is proposed which shows how MAVs would perfectly fit under the umbrella of smart agriculture, how MAVs would be extremely efficient in collecting data, pollinating and monitoring, functioning as vital IoT devices. Robotics, automation, and cloud software systems are efficient tools for smart farming.

## 4 Proposed Beebot

Most of the mechanical design is extremely simple to manufacture. The body primarily consists of five components in total. Four of these are similar to the base model as proposed in [1]: the exoskeleton, transmission, wings, and actuators. In addition to that we add a small cap like structure to the bottom of the exoskeleton for carrying out the weight shifting mechanism.
* Documentation

* FAQ

* API

* OAI-PMH

* AuréHAL

## Informations

* À propos

* Données personnelles

* Mentions légales

* Accessibilité

* Conformité RGAA

## Questions juridiques

* Je publie, quels sont mes droits ?

* Loi pour une République numérique

* Stratégie de non-cession des droits

* SHERPA/RoMEO

## Portails

* Portails institutionnels

* HAL

* HAL SHS

* HAL Thèses

* MédiHAL

## CCSD

* CCSD

* Episciences

* Sciencesconf
## 4.1 The Exoskeleton

The exoskeleton or the air frame is primarily made of carbon fibre, which is light weighted and strong, ideal in case of an FWMAV. The structure is a hollowed out cuboid with some faces cut out. It would contain the actuators, weight shifting mechanism, and transmission.

## 4.2 The Wings

The wings are made of flexible material so as to be able to bend to provide the thrust. The wings are composed of a structural frame and spars (veins) made from carbon fiber and the membrane made from polyester film. The materials are chosen such that they remain rigid under various flight conditions. Each wing is 1.5 cm long, and along with the exoskeleton, the wingspan amounts to just 3.5 cm.

## 4.3 Actuation

The actuator chosen for this application is a bio-morph piezoelectric actuator. These actuators are chosen because of the fact that in their normal mode of operation they have too short a stroke to be useful for micro-robots. They can respond at high frequencies, many small displacements can be added together to give large net motion.

## 4.4 Transmission

The wing stroke is directly controlled by the actuator and transmission. Wing rotation takes place with a flexure joint in between the output of the transmission and the wings. This flexure is parallel to the wingspan direction and the joint takes care of over rotation of the wing. This system is actuated in only one degree-of-freedom.

## 4.5 Weight Shifting Mechanism

A tiny weight put in a cap is attached to the bottom of the body of the robot. The cap has four electromagnets which control the x- and y- coordinates of the weight within the cap. This enables weight shifting and hence resulting in the tilting of the body of the robot.

# 5 Proposed Design

We adopt the basic model described in [1]. It was capable of only a single vertical degree of freedom. The novelty of work is to implement the weight shifting mechanism that overcomes the drawbacks of existing designs. We add a small weight at the bottom of the body and shift it to shift the centre of mass of the whole robot resulting in a tilt thus developing a horizontal component to the thrust generated by the wings.
<figure><img src="image_4.png" /><figcaption>Fig. 2: Illustration of weight shifting mechanism</figcaption></figure>

## 5.1 System Dynamics

The net force exerted by the wings on the body is balanced by sum of the weight of the body and the resultant thrust as

$$m\ddot{r} = -mgn_3 + fb_3 \quad (1)$$

where $b_3$ is vector along the axis of the body and $n_3$ vector along universal z-axis and $r$ is the resultant force vector.

The change in orientation is given by:

$$\dot{q} = \frac{1}{2}q * p \quad (2)$$

The rotational force is given by:

$$J\dot{\omega} = -\omega \times J\omega + \tau \quad (3)$$

## 5.2 Actuator Command Generation

Let $m$ be mass of the smaller shifting weight and $M$ rest of the mass of the body, which results in the tilting of the body by an angle $\theta$ (Fig. 2). In equilibrium
conditions, for F representing the thrust generated by the flapping of wings on the body,

$$F \cos \theta = (M + m)g \qquad (4)$$

where body tilt angle, $\theta$ is

$$\theta = \arctan \frac{x_{COM}}{L} \qquad (5)$$

Where L represents vertical distance between centre of mass and the smaller mass m and $x_{COM}$ represents shift in centre of mass due to shifting of weight by distance x from (0,0). $x_{COM}$ is calculated as

$$x_{COM} = x \frac{m}{M + m} \qquad (6)$$

We compute similarly along the y-axis.

While the horizontal component provides for motion:

$$F \sin \theta = (m + M)a \qquad (7)$$

Hence we can control two parameters, F and x.

F is directly proportional to the speed of flapping of wings and can be input as a product of height required and a constant K.

x will be run through a PID controller to determine how fast and in which direction the body will move.

## 5.3 Weight Shifting Mechanism

To be able to displace the weight m through an amount x, we use electromagnetic force. The electromagnetic force generated is

$$F = \frac{\mu_o m_1 m_2}{4\pi r^2} \qquad (8)$$

Considering two electromagnets opposite to each other with magnetic poles $m_1$ and $m_2$ at a distance of r units from each other attached to the edge of the cap.

So, force by first electromagnet is given by

$$F_1 = \frac{\mu_o m_1 m_i}{4\pi x^2} \qquad (9)$$

and force by second electromagnet by

$$F_2 = \frac{\mu_o m_2 m_i}{4\pi (r - x)^2} \qquad (10)$$

where r is the diameter of the cap, x distance required to move, $m_i$ the induced magnetic moment in the weight, and $m_1$ and $m_2$ are the magnetic moments of the two electromagnets. Net force would be a difference of these two forces resulting in acceleration of the small weight
$$F_{resultant} = F_1 - F_2 = ma \qquad (11)$$

where a is the acceleration and m mass of the weight.

Now to move the weight through a distance l,

$$l = \frac{at^2}{4} \qquad (12)$$

for a zero initial velocity, considering an immediate deceleration of the same amount after t/2 time has passed.

## 5.4 Attitude controller

Based on the information regarding orientation of the body received from the IMU, we run the following control loop.

$$Roll = e_r K_p + K_i \int e_r dt + K_d \frac{de_r}{dt} \qquad (13)$$

where $e_r$ represents the error in roll, i.e., the difference between the desired orientation and current orientation, $K_p, K_i$, and $K_d$ represent PID gains.

$$Pitch = e_p K_p + K_i \int e_p dt + K_d \frac{de_p}{dt} \qquad (14)$$

where $e_p$ represents error in pitch.

Now, we define the amount of change in position of the weight along x-axis and y-axis as

$$x = Roll - Throttle \qquad (15)$$

$$y = Pitch - Throttle \qquad (16)$$

which is then used to calculate the required force and electromagnetic field induction by Eq. (11) and (12).

## 5.5 Position controller

Position controller is comprised of two sub-algorithms. The first sub-scheme generates the magnitude of the thrust force, f; the second sub-scheme generates the desired attitude. In specific, f is computed as

$$f = f_a^T b_3 \qquad (17)$$

$$f_a = -K_p(r - r_d) - K_d(\dot{r} - \dot{r}_d) - K_i \int (r - r_d)dt + mgn_3 + m\dot{r}d \qquad (18)$$

where $K_p, K_d$, and $K_i$ are positive definite diagonal gain matrices; and $r_d$ is the desired position of the robot's center of mass.
<figure><img src="image_5.png" /><figcaption>Fig. 3: Proposed Beebot ROS architecture</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Fig. 4: Structure of the Beebot</figcaption></figure>

## 5.6 ROS and Simulation

We validated the model using Gazebo simulation. It is a robot simulation software which contains a physics engine that primarily helps with rapidly testing algorithms, designing robots in a realistic scenario.

The system includes the CAD design translated into URDF which represents the physical and collisional properties of the model. A feedback controlled control system is designed taking input from the body of the robot and giving output as torque required to apply on the joints.

The basic architecture, as shown in Fig. 3, of the MAV includes a control system node which publishes input values to the simulated Beebot body in Gazebo. It also accepts input from the simulation's physics node providing for external influences such as wind, gravity, collision, etc. The simulated sensors record data which is processed by a state estimator and the processed feedback is given back as input to the control system node.
The architecture of ROS nodes and ROS topics has been quite simple and intuitive. The robot was designed as a simple cuboid with two wings attached by hinges. The wings themselves can rotate in order to properly provide for thrust as shown in Fig. 4.

<figure><img src="image_7.png" /><figcaption>Fig. 5: Flow Chart of the ROS architecture</figcaption></figure>

Each hinge and wing has a joint each, making it total four joints. Every joint has its own topic declared on which command is published. A single node containing the control system algorithm, takes in the position and state of the
wings as inputs and gives command to the joints in a feedback controlled loop structure as shown in Fig. 5.

As shown in the Fig. 4, a forward flap would not only mean yaw movement of the wings but also a negative pitch. Similarly for a backward flap, a negative yaw along with a positive pitch.

# 6 Experimental Results

We prototyped and tested the control system algorithms using ROS and Gazebo. The prototype has a wingspan of 3.5 cm and weight of 145 mg. Each wing is 15 mg, the additional weight structure 30 mg, and the body itself 85 mg. We tested a wide range of values for taking off and landing, and successfully managed to fly vertically, hover the robot and tilt it in the desired direction.

## 6.1 Flight

The flapping frequency observed was 150 Hz at which the robot took off at a reasonable speed. Fig. 6 shows the take-off mode with the command given in terms of Newton-meters and the wing responding accordingly measured in radians (Fig. 7).

<figure><img src="image_8.png" /><figcaption>Fig. 6: Input values for the wings</figcaption></figure>
<figure><img src="image_9.png" /><figcaption>Fig. 7: Flapping Pattern</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>Fig. 8: Altitude gained as a function of time. The beebot flies vertically and stops at a height of 7 meters.</figcaption></figure>
<figure><img src="image_11.png" /><figcaption>Fig. 9: Gazebo image of beebot</figcaption></figure>

## 6.2 Hover

Fig. 8 shows the position of the beebot as it tries to stabilise itself at a point in the simulation at a height of 7 meters with a flapping frequency of 120 Hz (Fig. 9).

## 6.3 Tilt

After adding the extra weight, we set to trying to get the robot body to tilt. We had the body tilting at roughly 30° as shown in Fig. 10 and Fig. 11. This is accomplished by keeping the additional weight in a cap attached to the bottom of the body, with its weight being roughly 30mg. The weight moved at a distance of 2.5 mm within the cap to achieve this value of tilt.

This shows how we can easily acquire the tilt required to move in the *xy* plane by just attaching a small structure at the bottom containing a weight. It is moved around to apply torque and tilt the body by whichever amount and direction we need seamlessly and instantaneously, resulting in a compact mechanical structure, and a simple control system design.
<figure><img src="image_12.png" /><figcaption>Fig. 10: Gazebo Image of the Beebot tilting forward</figcaption></figure>

<figure><img src="image_13.png" /><figcaption>Fig. 11: Graph representing change in pitch of the Beebot as weight gets shifted</figcaption></figure>

# 7 Conclusions and Future Work

We have proposed a light FWMAV for artificial pollination and validated it in simulation. We proposed a simpler design with a weight shifting mechanism that can be used to control the robot to move in desired direction. The future work
includes prototyping and testing for artificial pollination in real farms. Some of
the challenges would be controlling the yaw of the robot and making it resistant
and stable to wind.

References

[1] R. J. Wood, "The First Takeoff of a Biologically Inspired At-Scale Robotic Insect," in IEEE Transactions on Robotics, vol. 24, no. 2, pp. 341-347, April 2008, doi: 10.1109/TRO.2008.916997.

[2] K.Y. Ma, S. M. Felton and R. J. Wood, "Design, fabrication, and modeling of the split actuator microrobotic bee," 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2012, pp. 1133-1140, doi: 10.1109/IROS.2012.6386192.

[3] X. Yang, Y. Chen, L. Chang, A. A. Calderón and N. O. Pérez-Arancibia, "Bee+: A 95-mg Four-Winged Insect-Scale Flying Robot Driven by Twinned Unimorph Actuators," in IEEE Robotics and Automation Letters, vol. 4, no. 4, pp. 4270-4277, Oct. 2019, doi: 10.1109/LRA.2019.2931177.

[4] Y. Chen and Y. Li, "Intelligent Autonomous Pollination for Future Farming - A Micro Air Vehicle Conceptual Framework With Artificial Intelligence and Human-in-the-Loop," in IEEE Access, vol. 7, pp. 119706-119717, 2019, doi: 10.1109/ACCESS.2019.2937171.

[5] J. Zhang, F. Fei, Z. Tu and X. Deng, "Design optimization and system integration of robotic hummingbird," 2017 IEEE International Conference on Robotics and Automation (ICRA), 2017, pp. 5422-5428, doi: 10.1109/ICRA.2017.7989639.

[6] G. Sushanth and S. Sujatha, "IOT Based Smart Agriculture System," 2018 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), 2018, pp. 1-4, doi: 10.1109/WiSPNET.2018.8538702.

[7] S. Kumar, G. Chowdhary, V. Udutalapally, D. Das and S. P. Mohanty, "gCrop: Internet-of-Leaf-Things (IoLT) for Monitoring of the Growth of Crops in Smart Agriculture," 2019 IEEE International Symposium on Smart Electronic Systems (iSES) (Formerly iNiS), 2019, pp. 53-56, doi: 10.1109/iSES47678.2019.00024.

[8] K. V. d. Oliveira, H. M. Esgalha Castelli, S. José Montebeller and T. G. Prado Avancini, "Wireless Sensor Network for Smart Agriculture using ZigBee Protocol," 2017 IEEE First Summer School on Smart Cities (S3C), 2017, pp. 61-66, doi: 10.1109/S3C.2017.8501379.

[9] X. Xu, X. Li and R. Zhang, "Remote Configurable Image Acquisition Lifting Robot for Smart Agriculture," 2019 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), 2019, pp. 1545-1548, doi: 10.1109/IAEAC47372.2019.8997721.

[10] Alexander Chautá-Mellizo, Stuart A. Campbell, Maria Argenis Bonilla, Jennifer S. Thaler, Katja Poveda, "Effects of natural and artificial pollination on fruit and offspring quality", Basic and Applied Ecology, Volume 13, Issue 6, 2012, Pages 524-532, ISSN 1439-1791, doi: https://doi.org/10.1016/j.baae.2012.08.013.

[11] Stein, Katharina and Coulibaly, Drissa and Stenchly, Kathrin and Goetze, De-
thardt and Porembski, Stefan and Lindner, Andre and Konaté. Souleymane and
Linsenmair, K. Eduard. (2017). "Bee pollination increases yield quantity and quality
of cash crops in Burkina Faso, West Africa". Scientific Reports. 7. 10.1038/s41598-
017-17970-2.