The multitarget tracking of multirobot systems has a wide range of applications, including urban security, search, and rescue. However, these tasks often take place in obstacle-rich environments, posing significant challenges, such as an unknown global map, unclear target locations, and inefficient resource allocation. In this article, we propose an air–ground cooperative hierarchical method for multitarget tracking. The uncrewed aerial vehicle (UAV) is equipped with a fisheye camera to provide a wide field of view (FOV) for multitarget detection and an initial global map generated from segmented aerial images. Multiple targets are assigned to uncrewed ground vehicles (UGVs) using the task allocation method, which is based on the PIO and auction mechanism. UGV track targets through motion planning to avoid obstacles and keep their assigned targets in the FOV. Experimental validation confirmed the effectiveness of our method, demonstrating that UGVs can autonomously and reliably track targets in obstacle-rich environments with UAV assistance.

Dragonflies show impressive flying skills by achieving both high efficiency and agility. They can perform distinctive flight maneuvers, such as flying backwards, which has proven to be achieved through "force vectoring" mechanism recently. In this paper, to explore the agile flight ability of dragonflies on man-made flapping wing systems, we designed, optimized and fabricated a dragonfly-inspired flapping wing robot (DFWR) with inclinable stroke plane control degrees. The proposed platform employs a four-wing configuration, each of which integrates an extra servo motor to enable the rotation of the flapping plane and imitate the "force vectoring" mechanism. Besides, referring to the flapping kinematics of dragonflies, the installation angle and wing pitch angle of the proposed DFWR are optimized considering the total lift and energy consumption through multiobjective optimization based on NSGA-II method. The "force vector" produced by the proposed platform has been illustrated through both theoretical method and experimental method. Moreover, the feasibility of the design is further verified through a series of operation validation experiments. Such a robot has the potential to provide a highly biomimetic platform to validate the flight mechanism studying of Odonata as well as the relative on-board applications such as bio-inspired vision.

Flapping Wing Rotorcraft (FWR) combines flapping and rotating wing motion in one element. Such a hybrid design integrates the high-efficiency characteristics of the rotating wing and the high-lift feature of the flapping wing under low Reynolds number, providing a broader range of simultaneous lift and power efficiency optimization. Nevertheless, the flight performance of the current FWRs is limited by their complex transmission mechanisms. Such mechanical constraints not only induce coupled wing kinematics but also render tedious assembly work and fabrication imperfections. In order to fundamentally address the constraints, we propose a motor-driven FWR with mechanically decoupled wings. The wing of the proposed DFWR is directly actuated by two bi-directional rotating motors instead of using the crank rocker (or alike) transmission. The proposed DFWR flaps within 25Hz to 35Hz, with about 12.4 grams of system weight and 185mm wingspan. With the direct-drive principle, the wing kinematics can be modulated properly by real-time motor control. In particular, we tuned the flapping frequency, stroke amplitude, and mid-stroke angle of the proposed direct-drive FWR to attain its best lift performance. As a result, it can generate about 16 grams of maximum total lift. In order to validate the proposed design, free flight tests have been conducted. The proposed FWR demonstrates stable liftoff.

Insects and hummingbirds are capable of acrobatic maneuvers such as rapid turns and tight 360$^\circ$body flips. It is challenging for bio-inspired flapping wing micro aerial vehicles to achieve animal-like performance during such maneuvers due to their limitation in mechanism sophistication and flight control. Besides being significantly underactuated compared to their natural counterparts, flight dynamics is highly nonlinear with rapidly changing, unsteady aerodynamics which remains largely unknown during aggressive maneuvers. As a result, conventional model-based control methods are inadequate to address such maneuvers effectively due to the lack of control references and aerodynamic models. In addition, during acrobatic maneuvers such as body flips, conventional control methods with underlying stabilization mechanisms would temporarily contradict the maneuvering requirements when the vehicle undergoes a full body flip including turning upside-down. In this article, reinforcement learning (RL) has been used to complement model-based control to enable animal-like maneuverability. The learned control policy serves in two different ways to either aid or even completely takeover the conventional stabilization controller in certain cases. We experimentally demonstrate animal-like maneuverability on an at-scale, dual-motor actuated flapping wing hummingbird robot. Two test cases have been performed to demonstrate the effectiveness of such integrated control methods: 1) a rapid escape maneuver recorded from hummingbirds, 2) a tight 360$^\circ$body flip inspired by houseflies. By leveraging RL, the hummingbird robot demonstrated a shorter completion time in escape maneuvers compared to the traditional control-based method. It also performed 360$^\circ$body flip successfully within only one wingspan vertical displacement.

In this paper, we present a generic fault-tolerant control (FTC) strategy via reinforcement learning (RL). We demonstrate the effectiveness of this method on quadcopter unmanned aerial vehicles (UAVs). The fault-tolerant control policy is trained to handle actuator and sensor fault/attack. Unlike traditional FTC, this policy does not require fault detection and diagnosis (FDD) nor tailoring the controller for specific attack scenarios. Instead, the policy is running simultaneously alongside the stabilizing controller without the need for on- detection activation. The effectiveness of the policy is compared with traditional active and passive FTC strategies against actuator and sensor faults. We compare their performance in position control tasks via simulation and experiments on quadcopters. The result shows that the strategy can effectively tolerate different types of attacks/faults and maintain the vehicle's position, outperforming the other two methods.

Designing a hummingbird-inspired, at-scale, tail-less flapping-wing micro aerial vehicle (FWMAV) is a challenging task in order to achieve animallike flight performance under the constraints of size, weight, power, and actuation limitations. It is even more challenging to design such a vehicle with a pair of independently controlled wings equipped with a total of only two actuators. This article details a systematic solution for the design optimization and prototyping of such FWMAVs. The proposed solution covers the complete system models and analysis of wing-actuation dynamics, control authorities, body dynamics, mechanical limitations, and electrical constraints. Each subsystem, as well as the overall system, is experimentally validated. This comprehensive approach can facilitate the design of such FWMAVs with different optimization goals. To demonstrate the effectiveness of the proposed approach, in this article, we conduct three different design optimization tasks, which yield three different prototype systems: optimizing the lift-to-weight ratio, optimizing control bandwidth, and optimizing control authority. We first construct a vehicle with an optimized lift-to-weight ratio. Based on it, the other two platforms with different design optimization goals are presented for better flight performance. Flight tests were performed on each prototype to validate their flight performance and design goals. Compared to optimized control bandwidth, we demonstrate that the vehicle with optimized control authorities is significantly more capable in terms of flight performance. It shows sustained stable flight in both hovering and heavy load carrying (more than 60% of the vehicle's total weight), which is hard for the other two platforms to achieve.

Insects and hummingbirds exhibit extraordinary flight performance and can simultaneously master seemingly conflicting goals: stable hovering and aggressive maneuvering, which are unmatched by conventional small scale man-made vehicles. Flapping Wing Micro Air Vehicles (FWMAVs) hold great promise for closing this performance gap. However, design and control of such systems remain challenging. Here, we present an open source high fidelity dynamic simulation for FWMAVs. The simulator serves as a testbed for the design, optimization and flight control of FWMAVs. To validate the simulation, we recreated the at-scale hummingbird robot developed in our lab in the simulation. System identification was performed to obtain the model parameters. Force generation and dynamic response of open-loop and closed loop systems between simulated and experimental flights were compared. The unsteady aerodynamics and the highly nonlinear flight dynamics present challenging control problems for conventional and learning control algorithms such as Reinforcement Learning. The interface of the simulation is fully compatible with OpenAI Gym environment. As a benchmark study, we present a linear controller for hovering stabilization and a Deep Reinforcement Learning control policy for goal-directed maneuvering. Finally, we demonstrate direct simulation-to-real transfer of both control policies onto the physical robot, further demonstrating the fidelity of the simulation.

Biological studies show that hummingbirds can perform extreme aerobatic maneuvers during fast escape. Given a sudden looming visual stimulus at hover, a hummingbird initiates a fast backward translation coupled with a 180-degree yaw turn, which is followed by instant posture stabilization in just under 10 wingbeats. Consider the wingbeat frequency of 40Hz, this aggressive maneuver is carried out in just 0.2 seconds. Inspired by the hummingbirds' near-maximal performance during such extreme maneuvers, we developed a flight control strategy and experimentally demonstrated that such maneuverability can be achieved by an at-scale 12-gram hummingbird robot equipped with just two actuators driving a pair of flapping wings up to 40Hz. The proposed hybrid control policy combines model-based nonlinear control with model-free reinforcement learning. We used the model-based nonlinear control for nominal flight conditions where dynamic models are relatively accurate. During extreme maneuvers when the modeling error becomes unmanageable, we use a model-free reinforcement learning policy trained and optimized in simulation to 'destabilize' the system for peak performance during maneuvering. The hybrid policy manifests a maneuver that is close to that observed in hummingbirds. Direct simulation-to-real transfer is achieved, demonstrating the hummingbird-like fast evasive maneuvers on the at-scale hummingbird robot.

Wings of flying animals can not only generate lift and control torques but also can sense their surroundings. Such dual functions of sensing and actuation coupled in one element are particularly useful for small sized bio-inspired robotic flyers, whose weight, size, and power are under stringent constraint. In this work, we present the first flapping-wing robot using its flapping wings for environmental perception and navigation in tight space, without the need for any visual feedback. As the test platform, we introduce the Purdu Hummingbird, a flapping-wing robot with 17cm wingspan and 12 grams weight, with a pair of 30-40Hz flapping wings driven by only two actuators. By interpreting the wing loading feedback and its variations, the vehicle can detect the presence of environmental changes such as grounds, walls, stairs, obstacles and wind gust. The instantaneous wing loading can be obtained through the measurements and interpretation of the current feedback by the motors that actuate the wings. The effectiveness of the proposed approach is experimentally demonstrated on several challenging flight tasks without vision: terrain following, wall following and going through a narrow corridor. To ensure flight stability, a robust controller was designed for handling unforeseen disturbances during the flight. Sensing and navigating one's environment through actuator loading is a promising method for mobile robots, and it can serve as an alternative or complementary method to visual perception.

Micro Aerial Vehicles (MAVs) rely on onboard attitude and position sensors for autonomous flight. Due to their size, weight, and power (SWaP) constraints, most modern MAVs use miniaturized inertial measurement units (IMUs) to provide attitude feedback, which is critical for flight stabilization and control. However, recent adversarial attack studies have demonstrated that many commonly used IMUs are vulnerable to attacks exploiting their physical characteristics. Conventional redundancy-based approaches are not effective against such attacks because redundant IMUs have the same or similar physical vulnerabilities. In this paper, we present a novel fault-tolerant solution for IMU compromised scenarios, using separate position and heading information to restore the failed attitude states. Rather than adding more IMU alternatives for recovery, the proposed method is intended to minimize any modifications to the existing system and control program. Thus, it is particularly useful for vehicles that have tight SWaP constraints while requiring simultaneous high performance and safety demands. To execute the recovery logic properly, a robust estimator was designed for fine-grained detection and isolation of the faulty sensors. The effectiveness of the proposed approach was validated on a quadcopter MAV through both simulation and experimental flight tests.

Robotic vehicles (RVs), such as drones and ground rovers, are a type of cyber-physical systems that operate in the physical world under the control of computing components in the cyber world. Despite RVs' robustness against natural disturbances, cyber or physical attacks against RVs may lead to physical malfunction and subsequently disruption or failure of the vehicles' missions. To avoid or mitigate such consequences, it is essential to develop attack detection techniques for RVs. In this paper, we present a novel attack detection framework to identify external, physical attacks against RVs on the fly by deriving and monitoring Control Invariants (CI). More specifically, we propose a method to extract such invariants by jointly modeling a vehicle's physical properties, its control algorithm and the laws of physics. These invariants are represented in a state-space form, which can then be implemented and inserted into the vehicle's control program binary for runtime invariant check. We apply our CI framework to eleven RVs, including quadrotor, hexarotor, and ground rover, and show that the invariant check can detect three common types of physical attacks -- including sensor attack, actuation signal attack, and parameter attack -- with very low runtime overhead.

As a rapidly growing cyber-physical platform, unmanned aerial vehicles are facing more security threats as their capabilities and applications continue to expand. Adversaries with detailed knowledge about the vehicle could orchestrate sophisticated attacks that are not easily detected or handled by the vehicle's control system. In this work, we purpose a generic security framework, termed BlueBox, capable of detecting and handling a variety of cyber-physical attacks. To demonstrate an application of BlueBox in practice, we retrofitted an off-the-shelf quadcopter. A series of attacks were then launched by embedding malicious code in the control software and by altering the vehicle's hardware with the specific targeting of sensors, controller, motors, vehicle dynamics, and operating system. Experimental results verified that BlueBox was capable of both detecting a variety of cyber-physical attacks, while also providing the means in which to recover from such attacks.

Unlike conventional aerial vehicles of fixed or rotary wings, realtime on-board attitude estimation of insect or hummingbird scale Flapping Wing Micro Aerial Vehicles (FWMAVs) is very challenging due to the severe instantaneous oscillations (approximately ten times of gravity on our platform) induced by high-frequency wing flapping. In this work, we present a novel sensor fusion algorithm for realtime on-board attitude estimation of FWMAVs. The algorithm is proposed with adaptive model-based compensation for both sensing drift and aerodynamic forces induced by flapping wings. We validated our approach on a 12.5 grams hummingbird robot. The experimental results demonstrated the accuracy, convergence, and robustness of the proposed algorithm.

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.

Controlled hovering of motor driven flapping wing micro aerial vehicles (FWMAVs) is challenging due to its limited control authority, large inertia, vibration produced by wing strokes, and limited components accuracy due to fabrication methods. In this work, we present a hummingbird inspired FWMAV with 12 grams of weight and 20 grams of maximum lift. We present its full non-linear dynamic model including the full inertia tensor, non-linear input mapping, and damping effect from flapping counter torques (FCTs) and flapping counter forces (FCFs). We also present a geometric flight controller to ensure exponentially stable and globally exponential attractive properties. We experimentally demonstrated the vehicle lifting off and hover with attitude stabilization.