Vertebrate flyers perform intermittent flights as bounding or oscillating flights for power management. Intermittent flights and the resulting oscillating height during flapping and soaring provide the means of increasing speed without increasing flapping speed. These maneuvers and their robotic biomimicry have remained unexplored so far, which, if understood, can lead to aerial robot designs with endured flight operations. This works attempts to achieve robotic bounding flight using Northeastern’s Aerobat platform. Aerobat can dynamically morph its wings by collapsing them rapidly during each gaitcycle. We present a launcher designed that allows bounding flight experimentation of Aerobat in a computer-aided fashion. We augmented Aerobat with a plural tiny thruster to stabilize its unstable roll, pitch, and yaw dynamics. This paper presents our control design based on extending Aerobat’s states to accommodate unobservable aerodynamic forces and offers experimental results to support our proposed approach.

The primary aim of this study is to enhance the accuracy of our aerodynamic Fluid-Structure Interaction (FSI) model to support controlled 3D flight trajectories by Aerobat, a dynamic morphing winged drone. Building on our existing Unsteady Aerodynamic model based on horseshoe vortices, we introduce Aerobat version β, designed for attachment to a Kinova arm. We conduct experiments to gather force-moment data from the robotic arm, which is then used to fine-tune our unsteady model for banking turns. The tuned FSI model, combined with a collocation control strategy, is employed to achieve 3D banking turns in simulation. The study’s primary contribution is a methodical approach to calibrate our FSI model for predicting complex maneuvers and assessing its potential for closed-loop flight control of Aerobat using an optimization-based collocation method.

Controlling 3D flight in a flapping wing robot is a challenging problem due to the complex dynamics of aerodynamic forces on the wing membrane. Bats perform agile aerial maneuvers, such as tight banking and bounding flight, using their highly flexible wings. In this work, we develop a control method for a bio-inspired bat robot, Aerobat, which uses small, low-powered actuators to manipulate the flapping gait and the resulting aerodynamic forces. We implemented a controller based on a collocation approach to track desired roll and execute a banking maneuver, intended for use in trajectory tracking. This controller is tested in simulation to demonstrate its performance and feasibility.

Fluidic locomotion of flapping Micro Aerial Vehicles (MAVs) is complex, especially when insect flight dynamics (fast flapping, light wings) don’t apply, making standard averaging techniques ineffective. Our goal is to model complex aerial locomotion where wings, constituting a large part of body mass, deform in multiple directions, resulting in morphing wings with significant inertial effects. These systems involve complex inertial, Coriolis, and gravity terms due to high degrees of freedom. We employ Algorithmic Differentiation (AD) and Bayesian filters, computed with cubature rules, to efficiently estimate fluid-structure interactions. Using cubature rules for Gaussian-weighted integrals and AD, we compute the multi-degree-of-freedom dynamics of morphing MAVs accurately and efficiently, enabling closed-loop feedback control.

This work briefly covers our efforts to stabilize the flight dynamics of Northeatern’s tailless bat-inspired micro aerial vehicle, Aerobat. Flapping robots are not new. A plethora of examples is mainly dominated by insect-style design paradigms that are passively stable. However, Aerobat, in addition to being tailless, possesses morphing wings that add to the inherent complexity of flight control. The robot can dynamically adjust its wing platform configurations during gaitcycles, increasing its efficiency and agility. We employ a guard design with manifold small thrusters to stabilize Aerobat’s position and orientation in hovering, a flapping system in tandem with a multi-rotor. For flight control purposes, we take an approach based on assuming the guard cannot observe Aeroat’s states. Then, we propose an observer to estimate the unknown states of the guard which are then used for closed-loop hovering control of the Guard-Aerobat platform.

Fluidic locomotion of flapping Micro Aerial Vehicles (MAVs) can be very complex, particularly when the rules from insect flight dynamics (fast flapping dynamics and light wings) are not applicable. In these situations, widely used averaging techniques can fail quickly. The primary motivation is to find efficient models for complex forms of aerial locomotion where wings constitute a large part of body mass (i.e., dominant inertial effects) and deform in multiple directions (i.e., morphing wing). In these systems, high degrees of freedom yields complex inertial, Coriolis, and gravity terms. We use Algorithmic Differentiation (AD) and Bayesian filters computed with cubature rules conjointly to quickly estimate complex fluid-structure interactions. In general, Bayesian filters involve finding complex numerical integration (e.g., find posterior integrals). Using cubature rules to compute Gaussian-weighted integrals and AD, we show that the complex multi-degrees-of-freedom dynamics of morphing MAVs can be computed very efficiently and accurately. Therefore, our work facilitates closed-loop feedback control of these morphing MAVs.

Biologically-inspired robots are a very interesting and difficult branch of robotics dues to its very rich dynamical and morphological complexities. Among them, flying animals, such as bats, have been among the most difficult to take inspiration from as they exhibit complex wing articulation. We attempt to capture several of the key degrees-of-freedom that are present in the natural flapping gait of a bat. In this work, we present the mechanical design and analysis of our flapping wing robot, the Aerobat, where we capture the plunging and flexion-extension in the bat’s flapping modes. This robot utilizes gears, cranks, and four-bar linkage mechanisms to actuate the arm-wing structure composed of rigid and flexible components monolithically fabricated using PolyJet 3D printing. The resulting robot exhibits wing expansion and retraction during the downstroke and upstroke respectively which minimizes the negative lift and results in a more efficient flapping gait.

Flying animals, such as bats, fly through their fluidic environment as they create air jets and form wake structures downstream of their flight path. Bats, in particular, dynamically morph their highly flexible and dexterous armwing to manipulate their fluidic environment which is key to their agility and flight efficiency. This paper presents the theoretical and numerical analysis of the wake-structure-based gait design inspired by bat flight for flapping robots using the notion of reduced-order models and unsteady aerodynamic model incorporating Wagner function. The objective of this paper is to introduce the notion of gait design for flapping robots by systematically searching the design space in the context of optimization. The solution found using our gait design framework was used to design and test a flapping robot.

This work presents an actuation framework for a bioinspired flapping drone called Aerobat. This drone, capable of producing dynamically versatile wing conformations, possesses 14 body joints and is tail-less. Therefore, in our robot, unlike mainstream flapping wing designs that are open-loop stable and have no pronounced morphing characteristics, the actuation, and closed-loop feedback design can pose significant challenges. We propose a framework based on integrating mechanical intelligence and control. In this design framework, small adjustments led by several tiny low-power actuators called primers can yield significant flight control roles owing to the robot’s computational structures. Since they are incredibly lightweight, the system can host the primers in large numbers. In this work, we aim to show the feasibility of joints’ motion regulation in Aerobat’s untethered flights.

Flying animals possess highly complex physical characteristics and are capable of performing agile maneuvers using their wings. The flapping wings generate complex wake structures that influence the aerodynamic forces, which can be difficult to model. While it is possible to model these forces using fluid-structure interaction, it is very computationally expensive and difficult to formulate. In this paper, we follow a simpler approach by deriving the aerodynamic forces using a relatively small number of states and presenting them in a simple state space form. The formulation utilizes Prandtl’s lifting line theory and Wagner’s function to determine the unsteady aerodynamic forces acting on the wing in a simulation, which then are compared to experimental data of the bat-inspired robot called the Aerobat. The simulated trailing-edge vortex shedding can be evaluated from this model, which then can be analyzed for a wake-based gait design approach to improve the aerodynamic performance of the robot.

Bats’ dynamic morphing wings are known to be extremely high-dimensional, and they employ the combination of inertial dynamics and aerodynamics manipulations to showcase extremely agile maneuvers. Bats heavily rely on their highly flexible wings and are capable of dynamically morphing their wings to adjust aerodynamic and inertial forces applied to their wing and perform sharp banking turns. There are technical hardware and control challenges in copying the morphing wing flight capabilities of flying animals. This work is majorly focused on the modeling and control aspects of stable, tail-less, morphing wing flight. A classical control approach using bangbang control is proposed to stabilize a bio-inspired morphing wing robot called Aerobat. Robot-environment interactions based on horseshoe vortex shedding and Wagner functions is derived to realistically evaluate the feasibility of the bang-bang control, which is then implemented on the robot in experiments to demonstrate first-time closed-loop stable flights of Aerobat.

The study explores innovative solutions for enhancing aerial mobility on Mars, focusing on novel morphing Micro Aerial Vehicles (MAVs). Mars’ thin atmosphere presents unique challenges, particularly for traditional rotary-wing systems, which demand high energy due to their large mass inertia. Inspired by biological mechanisms, the MAVs employ dynamic wing morphing, enabling efficient lift generation and energy use through optimized wing motions. This design enhances operational endurance by minimizing negative lift forces and leveraging reduced wing inertia. Despite challenges in stability and actuator design, ongoing research on fluid-structure interactions and control mechanisms promises significant advancements for Mars exploration.

Our goal in this work is to expand the theory and practice of robot locomotion by addressing critical challenges associated with the robotic biomimicry of bat aerial locomotion. Bats are known for their pronounced, fast wing articulations, e.g., bats can mobilize as many as forty joints during a single wingbeat, with some joints reaching to over one thousand degrees per second in angular speed. Copying bats flight is a significant ordeal, however, very rewarding. Aerial drones with morphing bodies similar to bats can be safer, agile and energy efficient owing to their articulated and soft wings. Current design paradigms have failed to copy bat flight because they assume only closed-loop feedback roles and ignore computational roles carried out by morphology. To respond to the urgency, a design framework called Morphing via Integrated Mechanical Intelligence and Control (MIMIC) is proposed. In this paper, using the dynamic model of Northeastern University’s Aerobat, which is designed to test the effectiveness of the MIMIC framework, it will be shown that computational structures and closed-loop feedback can be successfully used to mimic bats stable flight apparatus.

Flapping wing flight is a challenging dynamical problem and is also a very fascinating subject to study in the field of biomimetic robotics. A Bat, in particular, has a very articulated armwing mechanism with high degrees-of-freedom and flexibility which allows the animal to perform highly dynamic and complex maneuvers, such as upside-down perching. This paper presents the derivation of a multi-body dynamical system of a bio-inspired bat robot called Aerobat which captures multiple biologically meaningful degrees-of-freedom for flapping flight that is present in biological bats. Then, the work attempts to manifest closed-loop aerial body reorientation and preparation for landing through the manipulation of inertial dynamics and aerodynamics by enforcing nonholonomic constraints onto the system. The proposed design paradigm assumes for rapidly exponentially stable controllers that enforce holonomic constraints in the joint space of the model. A model and optimization-based nonlinear controller is applied to resolve the joint trajectories such that the desired angular momentum about the roll axis is achieved.

Bat membranous wings possess unique functions that make them a good example to take inspiration from and transform current aerial drones. In contrast with other flying vertebrates, bats have an extremely articulated musculoskeletal system which is key to their energetic efficiency with impressively adaptive and multimodal locomotion. Biomimicry of this flight apparatus is a significant engineering or deal and we seek to achieve mechanical intelligence through sophisticated interactions of morphology. Such morphological computation or mechanical intelligence draws our attention to the obvious fact that there is a common interconnection between the boundaries of morphology and closed-loop feedback. In this work, we demonstrate that several biologically meaningful degrees of freedom can be interconnected to one another by mechanical intelligence and, as a result, the responsibility of feedback-driven components (e.g., actuated joints) is subsumed under computational morphology. The results reported in this work significantly contribute to the design of bio-inspired Micro Aerial Vehicles (MAVs) with articulated body and attributes such as efficiency, safety, and collision-tolerance.

Planning flight trajectories is important for practical application of flying systems. This topic has been well studied for fixed and rotary winged aerial vehicles, but far fewer works have explored it for flapping systems. Bat Bot (B2) is a bio-inspired flying robot that mimics bat flight, and it possesses the ability to follow a designed trajectory with its on-board electronics and sensing. However, B2’s periodic flapping and its complex aerodynamics present major challenges in modeling and planning feasible flight paths. In this paper, we present a generalized approach that uses a model with direct collocation methods to plan dynamically feasible flight maneuvers. The model is made to be both accurate through collection of load cell force data for parameter selection and computationally inexpensive such that it can be used efficiently in a nonlinear solver. We compute the trajectory of launching B2 to a desired altitude and a banked turn maneuver, and we validate our methods with experimental flight results of tracking the launch trajectory with a PD controller.

In this article, we present methods to optimize the design and flight characteristics of a biologically inspired bat-like robot. In previous, work we have designed the topological structure for the wing kinematics of this robot; here we present methods to optimize the geometry of this structure, and to compute actuator trajectories such that its wingbeat pattern closely matches biological counterparts. Our approach is motivated by recent studies on biological bat flight that have shown that the salient aspects of wing motion can be accurately represented in a low-dimensional space. Although bats have over 40 degrees of freedom (DoFs), our robot possesses several biologically meaningful morphing specializations. We use principal component analysis (PCA) to characterize the two most dominant modes of biological bat flight kinematics, and we optimize our robot’s parametric kinematics to mimic these. The method yields a robot that is reduced from five degrees of actuation (DoAs) to just three, and that actively folds its wings within a wingbeat period. As a result of mimicking synergies, the robot produces an average net lift improvesment of 89% over the same robot when its wings cannot fold.

Bats are unique in that they can achieve unrivaled agile maneuvers due to their functionally versatile wing conformations. Among these maneuvers, roosting (landing) has captured attentions because bats perform this acrobatic maneuver with a great composure. This work attempts to reconstruct bat landing maneuvers with a Micro Aerial Vehicle (MAV) called Allice. Allice is capable of adjusting the position of its Center of Gravity (CG) with respect to the Center of Pressure (CP) using a nonlinear closed-loop feedback. This nonlinear control law, which is based on the method of input-output feedback linearization, enables attitude regulations through variations in CG-CP distance. To design the model-based nonlinear controller, the Newton-Euler dynamic model of the robot is considered, in which the aerodynamic coefficients of lift and drag are obtained experimentally. The performance of the proposed control architecture is validated by conducting several experiments.

Bats have long captured the imaginations of scientists and engineers with their unrivaled agility and maneuvering characteristics, achieved by functionally versatile dynamic wing conformations as well as more than 40 active and passive joints on the wings. Wing flexibility and complex wing kinematics not only bring a unique perspective to research in biology and aerial robotics but also pose substantial technological challenges for robot modeling, design, and control. We have created a fully self-contained, autonomous flying robot that weighs 93 grams, called Bat Bot (B2), to mimic such morphological properties of bat wings. Instead of using a large number of distributed control actuators, we implement highly stretchable silicone-based membrane wings that are controlled at a reduced number of dominant wing joints to best match the morphological characteristics of bat flight. First, the dominant degrees of freedom (DOFs) in the bat flight mechanism are identified and incorporated in B2’s design by means of a series of mechanical constraints. These biologically meaningful DOFs include asynchronous and mediolateral movements of the armwings and dorsoventral movements of the legs. Second, the continuous surface and elastic properties of bat skin under wing morphing are realized by an ultrathin (56 micrometers) membranous skin that covers the skeleton of the morphing wings. We have successfully achieved autonomous flight of B2 using a series of virtual constraints to control the articulated, morphing wings.

From a dynamic system point of view, bat locomotion stands out among other forms of flight. During a large part of bat wingbeat cycle the moving body is not in a static equilibrium. This is in sharp contrast to what we observe in other simpler forms of flight such as insects, which stay at their static equilibrium. Encouraged by biological examinations that have revealed bats exhibit periodic and stable limit cycles, this work demonstrates that one effective approach to stabilize articulated flying robots with bat morphology is locating feasible limit cycles for these robots; then, designing controllers that retain the closed-loop system trajectories within a bounded neighborhood of the designed periodic orbits. This control design paradigm has been evaluated in practice on a recently developed bio-inspired robot called Bat Bot (B2).

The sophisticated and intricate connection between bat morphology and flight capabilities makes it challenging to employ conventional flying robots to replicate the aerial locomotion of these creatures. In recent work, a bat inspired soft Micro Aerial Vehicle (MAV) called Bat Bot (B2) with five Degrees of Actuation (DoA) has been constructed to mimic the flight behavior of a biological bat. Major differences in structural topology resulted from this simpler kinematic complexity, and thus it is necessary to find the dimensions of B2’s structure and the behavior of its actuators such that the wingbeat cycle of B2 closely mimics that of a biological bat. The current work assumes the previously designed structure of B2 and presents a synergistic design approach to imitate the kinematic synergies of a biological bat. Recent findings have unveiled that the most dominant synergies in a biological bat could be combined to accurately represent the original kinematic movement, therefore simplifying its dimensional complexity. In this work, Principal Component Analysis (PCA) has been employed in order to extract dominant principal components of biological bat flight kinematics. Thereafter, first and second principal components are chosen to shape the parametric kinematics and actuator trajectories of B2 through finite state nonlinear constrained optimization. The method yields a robot mechanism that despite having a few DoAs, it possesses several biologically meaningful morphing specializations.

It is challenging to analyze the aerial locomotion of bats because of the complicated and intricate relationship between their morphology and flight capabilities. Developing a biologically inspired bat robot would yield insight into how bats control their body attitude and position through the complex interaction of nonlinear forces (e.g., aerodynamic) and their intricate musculoskeletal mechanism. The current work introduces a biologically inspired soft robot called Bat Bot (B2). The overall system is a flapping machine with 5 Degrees of Actuation (DoA). This work reports on some of the preliminary untethered flights of B2. B2 has a nontrivial morphology and it has been designed after examining several biological bats. Key DoAs, which contribute significantly to bat flight, are picked and incorporated in B2’s flight mechanism design. These DoAs are: 1) forelimb flapping motion, 2) forelimb mediolateral motion (folding and unfolding) and 3) hindlimb dorsoventral motion (upward and downward movement).

This paper presents a systematic flight controller design based on the mathematics of parametrized manifolds and calculus of variations for the Bat Bot (B2), which possesses many articulated wings. Wing kinematics and morphological properties are crucial in the powered flight of flying vertebrates. The articulated skeleton of these mammals, which contains many degrees of actuation and underactuation, has made it difficult to understand the connection between the bat’s flight dynamics and its intricate array of physiological and morphological specializations. B2 is a biomimetic micro aerial vehicle (MAV) that possesses similar morphological properties to a bat in order to duplicate bats powered ballistic motion. In an effort to design the advanced flight control algorithm for B2, this paper reports two major contributions. First, a systematic mathematical framework is introduced that evaluates the holonomically-constrained Lagrangian model of a flapping robot with specified active and passive degrees of freedom (DoF) in order to locate physically feasible and biologically meaningful periodic solutions using optimization. These are parametrized constraint manifolds; the flapping wing dynamics are governed by these manifolds. Second, calculus of variations and the wellrecognized method of inverse dynamics are applied in order to synthesize the flight control algorithm for the flapping wings.

Recent works have shown success in mimicking the flapping flight of bats on the robotic platform Bat Bot (B2). This robot has only five actuators but retains the ability to flap and fold-unfold its wings in flight. However, this bat-like robot has been unable to perform folding-unfolding of its wings within the period of a wingbeat cycle, about 100 ms. The DC motors operating the spindle mechanisms cannot attain this folding speed. Biological bats rely on this periodic folding of their wings during the upstroke of the wingbeat cycle. It reduces the moment of inertia of the wings and limits the negative lift generated during the upstroke. Thus, we consider it important to achieve wing folding during the upstroke. A mechanism was designed to couple the flapping cycle to the folding cycle of the robot. We then use biological data to further optimize the mechanism such that the kinematic synergies of the robot best match those of a biological bat. This ensures that folding is performed at the correct point in the wingbeat cycle.

In recent years, few works [1] have applied rigorous mathematics to design sophisticated flapping motions similar to bats. However, the morphological and kinematic features of flying vertebrates are still the underappreciated aspects of locomotor control designs for Unmanned Aerial Vehicles (UAVs). In this work we introduce Bat Bot, in short B2, shown in Fig. 1-(a), which is a biologically inspired articulated winged micro aerial vehicle that is capable of mimicking a variety of motions and maneuvers, including but not limited to steady level flight without a conventional tail, rapid turning, inverse perching and hovering. To achieve such goals, biologic bats were examined to determine certain key morphological features of B2. Because of the inefficiency of electric actuators compared to the biologic muscles of these mammals, certain compromises have to be made to enable B2 for self-sustained flight.