Flight of the Future: An Experimental Analysis of Event-Based Vision for Online Perception Onboard Flapping-Wing Robots

Flight of the Future: An Experimental Analysis of Event-Based Vision for Online Perception Onboard Flapping-Wing Robots Raul Tapia,* Javier Luna-Santamaria, Ivan Gutierrez Rodriguez, Juan Pablo Rodríguez-Gómez, José Ramiro Martínez-de Dios, and Anibal Ollero 1. Introduction Flapping-wing robots, also known as ornithopters, are aerial platforms that generate lift and thrust by mimicking the flight mechanism of birds and insects. These robots have high maneuverability and combine glide and flapping flight modes to minimize energy consumption. Compared to multirotor and fixed-wing platforms, flapping-wing robots consume less energy and are less dangerous in the event of a collision. In addition, their wide range of potential applications has motivated a significant research and development interest in recent years. [1–3] The design of perception systems for flapping-wing robots faces various challenges and limitations. [4] First, ornithopters have strict restrictions on payload and weight distribution, which directly affect the number, size, and shape of sensors, electronics, batteries, and other components onboard. In addition, their agile movements and flapping strokes produce strong mechanical vibrations that impose relevant constraints on the sensors that can be used for online perception. In addition, online onboard processing plays a crucial role in aerial robot perception. It enables the real-time decision-making and control required to achieve high degrees of safety, autonomy, and responsiveness through autonomous functionalities such as obstacle detection, collision avoidance, and navigation, among others. Fast reactivity is particularly critical considering the agile maneuvers of flapping-wing platforms. However, their constrained payload capacity also imposes complex restrictions on the processing units that can be used, requiring low-sized lightweight computers whose resources can be limiting for some applications. The ornithopters’constrained payload and onboard computational resources limit the number and type of sensors that can be used and the type of onboard perception processing. Sensors such as light detection and ranging (LiDARs), ultrasound sensors, radars, and infrared cameras, which are widely used in multirotors, present several problems when used for flapping-wing robots, whose payload is in the range of a few hundred grams. [5] On the contrary, vision sensors have low size and weight, providing rich information about the environment. Most existing works have proposed using vision-based perception for ornithopter autonomy. [4,6] Some works have used schemes based on traditional frame-based cameras in monocular [7–9] or stereo [10–14] configurations. Others have proposed perception schemes based on event cameras. [15,16] These novel bioinspired sensors, which operate asynchronously and per-pixel independently, try to mimic animal vision systems, either in speed and energy efficiency, [17] offering advantages such as low latency, high robustness against motion blur, and high dynamic range, among R. Tapia, J. Luna-Santamaria, I. Gutierrez Rodriguez, J. P. Rodríguez-Gómez, J. R. Martínez-de Dios, A. Ollero GRVC Robotics Lab University of Seville 41092 Seville, Spain E-mail: [email protected] The ORCID identification number(s) for the author(s) of this article can be found under https://doi.org/10.1002/aisy.202401065. © 2025 The Author(s). Advanced Intelligent Systems published by WileyVCH GmbH. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. DOI: 10.1002/aisy.202401065 Inspired by bird flight, flapping-wing robots have gained significant attention due to their high maneuverability and energy efficiency. However, the development of their perception systems faces several challenges, mainly related to payload restrictions and the effects of flapping strokes on sensor data. The limited resources of lightweight onboard processors further constrain the online processing required for autonomous flight. Event cameras exhibit several properties suitable for ornithopter perception, such as low latency, robustness to motion blur, high dynamic range, and low power consumption. This article explores the use of event-based vision for online processing onboard flapping-wing robots. First, the suitability of event cameras under flight conditions is assessed through experimental tests. Second, the integration of event-based vision systems onboard flapping-wing robots is analyzed. Finally, the performance, accuracy, and computational cost of some widely used event-based vision algorithms are experimentally evaluated when integrated into flapping-wing robots flying in indoor and outdoor scenarios under different conditions. The results confirm the benefits and suitability of event-based vision for online perception onboard ornithopters, paving the way for enhanced autonomy and safety in real-world flight operations. RESEARCH ARTICLE www.advintellsyst.com Adv. Intell. Syst. 2025, 2401065 2401065 (1 of 20) © 2025 The Author(s). Advanced Intelligent Systems published by Wiley-VCH GmbH others. [18,19] In a recent previous work, [20] we compared framebased and event cameras for flapping-wing robot perception, concluding that although event-based technology has a lower degree of maturity, it suits the requirements of ornithopters. Taking that work as the starting point, we analyze the use of event-based vision for online perception on board flapping-wing robots with a broader approach, evaluating commonly used event-based vision algorithms, suitability for indoor and outdoor ornithopter perception, ease of integration, and available resources and support tools. This article broadly analyzes the suitability of event-based vision for the online onboard perception of flapping-wing robots. We intend to answer these three questions: Q1: Are event cameras suitable for online onboard perception considering the challenging flight conditions of flapping-wing robots? Q2: Are event cameras suitable to be integrated (HW&SW) on flapping-wing robots considering the constraints of these platforms? Q3: Are the performance and computational cost of event-based algorithms feasible to enable online onboard perception task for ornithopters? We aim to answer this question by performing three analyses: 1) evaluation of the suitability and robustness of event cameras in experiments that mimic the conditions that can be found in flapping-wing flights; 2) review of the available event-based devices and resources and support tools focusing on the HW&SW integrability on flapping-wing platforms; and 3) experimental evaluation of the performance and computational cost of some widely used event-based vision algorithms when integrated into flapping-wing robots flying in different scenarios (including the platform in Figure 1). To the best of the authors’knowledge, this is the first work broadly analyzing the use of event vision for large-scale flapping-wing robots. In addition, we provide the data recorded onboard two different flapping-wing robots in indoor and outdoor scenarios used in the experimental evaluation of this work. The rest of the article is structured as follows: Section 2 briefly summarizes the main works in the topics addressed in the article. The experimental analysis of the suitability of event cameras for the perception challenges of flapping-wing flight is presented in Section 3. The HW&SW integrability of event-based vision onboard ornithopters is analyzed in Section 4. The evaluation of widely used event-based vision algorithms for ornithopters’ onboard perception is presented in Section 5. Finally, Section 6 concludes the article with the findings and main future steps. 2. Related Work 2.1. Event Cameras In recent years, event cameras have attracted increasing interest in fields such as artificial intelligence, computer vision, neuromorphics, and robotics. The origins of event cameras trace back to 1991 with pioneering efforts in neuromorphic vision technology and the emergence of the silicon retina, [21] which mimicked biological eyes’processing capabilities. This foundation led to the EU’s CAVIAR project, [22] which advanced early AER-based event vision systems. In 2008, the first commercial event cameras (128 128 resolution, 40 μm pixel size; now iniVation’s DVS128) were introduced. [23–26] By 2019, major players like Sony and Samsung entered the market, signaling growing commercial interest in event-based sensors. Technological advancements continued rapidly, with the first HD event cameras [27] (1280 720 resolution, 5 μm pixel size) launching in 2021. In 2022, Meta began showing interest in this innovative technology, [28] followed by a significant collaboration between Prophesee and Qualcomm (https://www.prophesee.ai/2023/02/27) in 2023 and with AMD and Lucid Vision Labs (among others) (https:// www.prophesee.ai/2024/10/02) in 2024. Foundation studies introducing novel event camera designs have explored their advantages over traditional sensors. [23–27,29–34] However, there are few works that analyze and compare frame-based and event-based vision. Addressing this gap, Holešovský et al. [35,36] conducted an experimental analysis using a speed-controlled spinning disk and a bullet fired at various velocities to compare the performance of an ATIS HVGA Gen3 event camera, a DVS240 event camera, and two high-speed global-shutter cameras. Their results demonstrated the advantages of event cameras, particularly in terms of bandwidth efficiency, and also explored the limitations of event-based sensors in pixel latency and readout bandwidth, particularly in highly cluttered scenes. Similarly, Barrios et al. [37] carried out a comparison employing a GENIE M640 CCD camera and a non-commercial event CMOS camera connected to a Powerlink IEEE 61 158 industrial network for controlling a two-axis planar robot during object tracking. The results showcase the event camera’s capability to enable the robot to track the target with greater speed, accuracy, and stability, especially under varying light conditions. The work of Censi et al. [38] proposed a formal evaluation of diverse sensor families using a power-performance curve. The study focused on contrasting traditional CCD/CMOS sensors with neuromorphic vision sensors and revealed the task-dependent dominance of different sensors across various sensing power ranges. Cox et al. [39] introduced a theoretical methodology to assess the performance of event and frame cameras. Their approach involved employing system-level models and surrogate performance metrics for target recognition tasks. Furthermore, data-processing perspectives have been explored in comparative studies between frame and event cameras. Farabet et al. [40] conducted a comparison between frame-based convolutional neural networks and frame-free spiking neural networks for object recognition applications. Implementation examples using VLSI chips and field-programmable gate array (FPGAs) were provided, analyzing differences in computational speed, scalability, multiplexing, and signal representation. Figure 1. Hybrid: one of the ornithopters developed by the GRVC Robotics Lab employed for recording the dataset used in this work. www.advancedsciencenews.com www.advintellsyst.com Adv. Intell. Syst. 2025, 2401065 2401065 (2 of 20) © 2025 The Author(s). Advanced Intelligent Systems published by Wiley-VCH GmbH 26404567, 0, Downloaded from https://advanced.onlinelibrary.wiley.com/doi/10.1002/aisy.202401065 by Readcube (Labtiva Inc.), Wiley Online Library on [13/05/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License Rebecq et al. [41,42] proposed an image reconstruction method based on a recurrent neural network trained with simulated events, evaluating and comparing the quality of the reconstructed images using standard computer vision algorithms such as visual-inertial odometry and object classification. Further, some studies shed light on the use of events and frames for specific tasks (e.g., the work from Rodríguez-Gómez et al. [43] on visual stabilization). 2.2. Flapping-Wing Robot Perception The development of online onboard perception systems for flapping-wing robots is a challenging problem. Traditionally, control and guidance methods for ornithopters have relied on external sensors such as motion capture systems. [44–46] In addition, some studies have performed off-board processing of visual sensors. [6,7,47–51] However, recent advances in the size and weight reduction of visual sensors and processors have enabled the integration of fully onboard perception systems for ornithopters. One of the first methods proposed for ornithopters was the obstacle avoidance method of Wagter et al. [11] and Tijmons et al. [12] which used a lightweight stereo camera with a CPU module to detect static obstacles using disparity maps. More recently, Gómez et al. [15] presented an ornithopter guidance method based on event cameras. The algorithm tracks line pattern references from events and feeds a visual servoing controller to guide the robot toward a goal position. RodríguezGómez et al. [16] presented an event-based dynamic obstacle avoidance method for ornithopters, which quickly detects moving obstacles and triggers evasive actions controlling ornithopter tail deflections. The use of event-based frequency processing onboard a flapping-wing robot is explored in Tapia et al. [52] These works use different types of vision sensors (frame-based, event-based, and/or stereo setups) but do not conclude which sensor is the most suitable for flapping-wing robots. In a previous work, [20] we compared frame and event cameras for ornithopters, concluding that although event cameras have a lower level of maturity, they are more suitable for flapping-wing robot onboard perception than frame-based cameras. This article represents a step forward and evaluates the use of the complete event-based vision system for ornithopter onboard perception from a comprehensive perspective by assessing 1) the operational limits of event-based vision considering effects such as vibration level; 2) the ease of integration of event cameras on board ornithopters; and 3) the capacity of event-based systems to perform different perception tasks on board our flapping-wing robots indoors and outdoors. 3. Flight Challenges This section intends to answer Q1: Are event cameras suitable for online onboard perception considering the challenging flight conditions of flapping-wing robots? For this purpose, it is necessary to consider what these challenging conditions are and how they affect onboard vision systems. As stated in ref. [20], the flight of the ornithopters imposes substantial requirements on the onboard vision system. In search of efficiency and robustness, we need to know the operational limits of event-based vision under several aspects. Agile movements, strong vibrations, and changes in tilt angle caused by flapping strokes lead to sudden changes in the visual scene captured by the camera. Therefore, the onboard vision system requires a high temporal resolution to ensure that these rapid changes can be perceived. The ability to capture data with a precise temporal resolution enables a high degree of responsiveness and reactive decision-making, which is particularly critical in dynamic scenarios. However, high temporal resolution is generally associated with generating a large amount of information during flight. Hence, it is also necessary to analyze the bandwidth of the onboard sensors. In addition, to avoid information loss, the vision system must be robust to the motion blur that can be caused by the rapid shifts and vibrations mentioned earlier. Although event cameras are significantly robust to blur, they are not entirely insensitive. [53] Moreover, vibrations, fast motions, or changes in the tilt angle can cause abrupt fluctuations in the lighting captured by the camera. This potentially leads to information loss as the camera fails to adapt to changing lighting conditions. Hence, a high dynamic range is critical, particularly in outdoor scenarios. The analysis of operation in dark lighting conditions is also relevant for indoor applications. We have experimentally analyzed the response of event-based vision systems in terms of 1) temporal resolution; 2) bandwidth; 3) motion blur; 4) dynamic range; and 5) operation in dark conditions. These analyses include experiments in test benches designed to mimic the flapping-wing flight conditions. 3.1. Temporal Resolution Ornithopter vibration and agile maneuvers require perception systems capable of effectively capturing and analyzing objects with very high temporal resolution. This is particularly crucial, for instance, for sense-and-avoid systems. Unlike traditional cameras that operate by capturing a fixed number of frames per second, event cameras capture visual information asynchronously, hence enabling temporal resolutions that are mainly limited by the camera’s refractory period. To illustrate the potentialities of event sensors in terms of temporal resolution, we designed an experiment in which different cameras were used to detect a blinking LED, whose frequency varies from 1 Hz to 1 kHz. The temporal resolution of event-based cameras provides a significant advantage for detecting and tracking rapid movements. In the context of ornithopter perception, it is essential to consider the combined effects of the flapping motion and the detected target dynamics. These factors can increase the operational frequency demand of the detection system. Therefore, the operational flapping frequency of the ornithopter (yellow area in Figure 2) should be regarded as a minimum requirement for the onboard detection system. The dynamic vision sensor (DVS) of a DAVIS346, a DVXplorer Mini, and two frame-based sensors (with frame rates 30 and 40 Hz) were evaluated. To avoid any possible degradation produced by motion blur, see discussion in Section 3.3, the LED was placed to cover a sufficient number of pixels, ensuring its detection even if some pixels did not produce events. In the case of the DVS and the DVX, a blink of the LED is detected as two consecutive events with opposite polarities www.advancedsciencenews.com www.advintellsyst.com Adv. Intell. Syst. 2025, 2401065 2401065 (3 of 20) © 2025 The Author(s). Advanced Intelligent Systems published by Wiley-VCH GmbH 26404567, 0, Downloaded from https://advanced.onlinelibrary.wiley.com/doi/10.1002/aisy.202401065 by Readcube (Labtiva Inc.), Wiley Online Library on [13/05/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License (ON and OFF) in the corresponding pixels. For the frame cameras, an abrupt change in the mean intensity of the LED’s pixels is computed. As shown in Figure 2, DVS and DVX can correctly detect blinking LEDs in the entire range of frequencies analyzed. In contrast, frame-based detection of the blinking frequency is limited by the Nyquist frequency—that is, half of the camera frame rate: 15 and 20 Hz, respectively. A frame camera with a temporal resolution similar to that of the DVS or the DVX would require a frame rate of >2 kHz, which would require bigger and heavier cameras that are far beyond the payload capabilities of ornithopters. Besides, processing frames at high frequencies requires dedicated hardware (e.g., GPU, parallelization) while increasing payload and power consumption. Results for higher frequencies are not shown because the event camera’s refractory period would deteriorate the ON/OFF trigger detection. 3.2. Bandwidth The lightweight computers that can be mounted on board ornithopters impose severe limitations in terms of computing power (i.e., bit/s that can be processed). Standard cameras suffer from the bandwidth-latency trade-off (BW ∝1/Δt). Conversely, event cameras are not governed by a framerate and, hence, can achieve a variable bandwidth while maintaining a low latency. Given an electronic camera configuration (i.e., the current biases that control bandwidth, contrast threshold, and refractory period, among others), the event generation rate depends mainly on the relative motion between the camera and the scene. Therefore, it is necessary to quantitatively evaluate the amount of information to be processed in both frame and event cameras. For that purpose, we analyzed the number of pixels (APS) and events (DVS) generated per second by the DAVIS346 event camera in Soccer,Testbed, and Hills scenarios from the GRIFFIN Perception Dataset. [4] Both sensors have the same resolution (346 260), have their pixel coordinates aligned, and share the same optics (i.e., same FoV, AFoV, focal length, distortion, etc.). Table 1 shows the number of events and pixels generated per second for three different scenarios in the dataset. It is worth noting that the values are lower for the DVS since it only captures changes in brightness, hence reducing the amount of redundant information from the scene (e.g., static background such as open sky). This can also be inferred in Figure 3, where the number of events and pixels per millisecond in the sequence Hills Base 1 are shown. The influence of the different ornithopter flight stages on event generation can be easily noticed: 1) launching (low event generation); 2) flapping (high event generation); and 3) landing (abrupt event generation). We can conclude that frame-based sensors suffer from oversampling during most of the flight but also from undersampling during certain aggressive maneuvers (e.g., ornithopter landing at t=38 s at Figure 3). In contrast, the ability of event cameras to generate information according to the scene dynamics contributes to an enhancement in information capture efficiency. 3.3. Motion Blur Global and rolling shutter frame-based cameras suffer from motion blur, mainly when the exposure time is relatively long with respect to the dynamic of the visual scene. In contrast, event cameras are more robust against motion blur due to their asynchronous nature, low latency, and high temporal resolution. Ornithopter flapping strokes produce aggressive tilting motions that affect the information captured by the cameras. [4] Although the work in ref. [54] presents a mechanical stabilizer to reduce tilt motion, the strict payload requirements of flapping-wing robots restrict the integration of gimbal devices. Furthermore, although some software deblurring or stabilization approaches may be Figure 2. Measured LED blinking frequencies with two event-based cameras (Async.) and two traditional cameras with frame rates of 30 and 40 Hz. The effect of aliasing can be noticed with blinking frequencies beyond 15 and 20 Hz for the frame-based cameras. The yellow area corresponds to the typical operational flapping frequency of reported ornithopters (see Table 2 in Section 4; excluding those platforms weighing less than 3 g for being unable to carry any vision system). Table 1. Mean, median, and maximum number of pixels and events generated in all the Soccer,Hills, and Testbed sequences. [4] MEPS stands for million events per second, MPPS stands for million pixels per second. Soccer Hills Testbed Mean Median Max Mean Median Max Mean Median Max APS [MPPS] 3.60 3.60 3.60 3.60 3.60 3.60 3.60 3.60 3.60 DVS [MEPS] 0.42 0.39 8.35 0.39 0.30 8.69 0.91 0.80 4.16 DVS [%] a) 3.50 3.25 69.58 3.25 2.50 72.42 7.58 6.67 34.67 a) Assuming a maximum bandwidth of 12 MEPS [ref. 18, Table 1]. www.advancedsciencenews.com www.advintellsyst.com Adv. Intell. Syst. 2025, 2401065 2401065 (4 of 20) © 2025 The Author(s). Advanced Intelligent Systems published by Wiley-VCH GmbH 26404567, 0, Downloaded from https://advanced.onlinelibrary.wiley.com/doi/10.1002/aisy.202401065 by Readcube (Labtiva Inc.), Wiley Online Library on [13/05/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License feasible for low levels of blur, [55] they introduce a significant delay in the processing pipeline that prevents real-time operation. Despite the higher robustness to motion blur when compared to frame-based cameras, [ref. 20, Section 5.2], event cameras can also suffer from motion blur under certain conditions. However, the effect observed in an event-based sensor when the relative camera-scene motion increases is quite different from that experienced by a conventional sensor. According to Benosman et al. [53] as the speed increases, motion blur causes events to form clusters rather than sharp edges, resulting in a sparser motion flow in event-based systems. Additionally, the camera does not generate enough events for all spatial locations, and as a result, some pixels are not activated. This effect, produced by the latencies of the sensor when capturing the light, was evaluated by mounting a DAVIS346 on a platform that mimics the horizontal flapping motion of ornithopters; see Appendix A. The camera was set pointing toward a black canvas with a white horizontal line under uniform and constant illumination conditions, and the camera-canvas distance was such that the line was present within the camera FoV during the whole experiment. Events are mainly triggered by the changes in pixels’intensities caused by the platform’s oscillatory motion. Thus, in these experiments, events are triggered at the projections of the line edges on the image plane. During the experiment, the platform oscillates first at 5.0 Hz and then gradually reduces to 2.5 Hz. To estimate the motion blur, we composed event images by accumulating a fixed number of events into frames and drawing them as black pixels in a white image. Figure 4 shows the event frames obtained by accumulating 1000 events at different oscillation frequencies. At higher frequencies, motion blur becomes visible as holes (white pixels) among the black pixels that describe the shape of the line. Another effect can be observed: the thickness of the line increases with higher frequencies. This occurs since the number of activated pixels decreases with the increase in frequency (i.e., the line’s speed), as previously discussed. We define the percentage of triggered events (PTE) as the number of triggered events divided by the area of the patch caused by the line in each event frame. That is, PTE is an indicator of events lost by motion blur on event cameras. Figure 5 shows the experimental results obtained. At 5.0 Hz, PTE is around 74.38% and increases as the oscillation frequency reduces, evidencing that flapping strokes produce motion blur in event cameras. This experiment cannot directly measure the total number of lost data, as the sensor might lose one or more events triggered at the same pixel, but it provides a valid approximation. 3.4. Dynamic Range Due to mechanical vibrations and sudden tilt angle changes due to the flapping strokes, the cameras on board ornithopters can have sudden drastic changes in lighting conditions, including brightly lit scenes, dark scenes, and also cases in which the camera FoV includes both bright and dark scene parts at the same time. Therefore, dynamic range is a critical aspect to consider when selecting the onboard sensors. In [ref. 20, Section 5.1], a DVS sensor was evaluated for ArUco detection in eventreconstructed images under different dynamic range conditions and presented an excellent performance of up to 80 dB. In that experiment, the noise and the insufficient number of events prevented the correct reconstruction used for the detection with strong dynamic range conditions. To remove that influence and complement the analysis, we designed a setup consisting of a spinning dot (a black rotating disk with a white dot on it), which was partially illuminated with a strong light source (mimicking a situation similar to a visual scene partially affected by the sunlight). The disk was spinning at a constant velocity. The velocity was low enough to assume that motion blur was not produced. We pointed our DAVIS346 camera toward the disk together with other frame-based cameras (Intel Realsense D345 and ELP Mini720p; hereinafter RS and ELP, respectively) to Figure 3. Number of events or pixels per millisecond generated respectively by DVS and APS in Hills Base 1 sequence. [4] A–C) correspond to the ornithopter launching, flapping, and landing, respectively. Notation: MEPS stands for million events per second, MPPS stands for million pixels per second. Figure 4. Event frames from the motion blur experiments while the frequency of the motorized platform is varied from 2.5 (left) to 5.0 Hz (right). Each frame was rendered by accumulating 1000 events. The events are less scattered (involving higher PTE) when reducing the frequency of the platform oscillations (from left to right). Figure 5. Percentage of triggered events (PTE, red) when varying the frequency of the flapping platform (blue). The number of lost events increases with the frequency of the platform oscillations. www.advancedsciencenews.com www.advintellsyst.com Adv. Intell. Syst. 2025, 2401065 2401065 (5 of 20) © 2025 The Author(s). Advanced Intelligent Systems published by Wiley-VCH GmbH 26404567, 0, Downloaded from https://advanced.onlinelibrary.wiley.com/doi/10.1002/aisy.202401065 by Readcube (Labtiva Inc.), Wiley Online Library on [13/05/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License detect the dot. We recorded a sequence of 30 s for each camera and then processed the images using a circle detection algorithm based on the Hough transform. A detection is considered when the number of votes received in a Hough space cell is higher than a threshold τ=0.6τ max , where τ max is computed by assuming that all pixels of the dot perimeter produce a vote in the same coordinates. As a result, the algorithm was able to reject false positives while maintaining an accurate detection. The input images for the Hough detection were obtained using Canny (for framebased cameras) or event images generated by accumulating events in the temporal windows of 3 ms. Then, we computed the number of dot detections performed in different disk sectors. Figure 6 shows a polar histogram with the number of dot detections in each disk sector for each sensor; the disk sector affected by the light source is shown in gray color. The DVS was able to detect the dot in the illuminated area, and the number of detections was similar to that in the non-illuminated sector. In contrast, all frame-based cameras failed to detect the dot in the illuminated area due to the sensor saturation. 3.5. Dark Lighting Conditions Perception using visual sensors becomes particularly challenging in dark scenes. Although, in some cases, increasing the exposure time of frame-based sensors can mitigate the problem, it might also increment the motion blur and noise level. We evaluated the performance of DAVIS346’s DVS and active pixel sensor (APS), ELP, and RS under three different conditions: pitch-dark (0 lx), dark (5 lx), and well-lit conditions (100 lx). All cameras were set with parallel optical axes that pointed to a pattern with four lines. The pattern was moved slowly to generate events while minimizing motion blur. In addition, the three different lighting conditions were applied sequentially (10 s between every change). Line detection was performed using the Hough transform applied to frames and event images (generated by accumulating 1000 events per image, see Figure 7). For a fair evaluation, the frame-based sensors were configured with autoexposure. DVS was the only sensor capable of detecting lines during the whole experiment. As expected, pitch-dark conditions hinder line detection for frame-based cameras even with high exposure times. Conversely, although the abrupt change in the lighting conditions generates events that lead to false line detections, the event camera was able to detect all lines in all the tested lighting conditions satisfactorily. 3.6. Discussion Event cameras demonstrate a strong potential for onboard perception in flapping-wing robots by addressing the challenges posed by their dynamic flight. Their asynchronous nature allows them to capture only relevant scene changes, reducing data load while maintaining responsiveness during agile maneuvers. Although motion blur can affect frame-based cameras’performance under extreme conditions (e.g., vibrations and aggressive flapping strokes), event cameras remain more robust. The reliability and high autonomy of flapping-wing robots make them suitable platforms for both indoor and outdoor operations. Hence, a high dynamic range and good performance under different light conditions are properties to be emphasized. All the results presented in this section highlight the suitability of event cameras for onboard vision in ornithopters. However, the growing interest in R&D for ornithopter technology is leading to platforms with higher payload capacities, with smoother flapping strokes, and capable of performing smoother trajectories. This may enable the integration of multi-sensor approaches. The use of event cameras with additional sensors can enhance the performance and reliability of perception systems by mitigating individual sensor limitations, leading to a more robust and adaptable perception system through data fusion. 4. Integration Challenges This section aims to answer Q2: Are event cameras suitable to be integrated (HW&SW) on flapping-wing robots considering the constraints of these platforms? The strict payload and weight distribution requirements of the flapping-wing robots severely restrict the integration of onboard sensors. The size and weight of the camera are critical and can severely affect the flight ability and controllability of these platforms. Recent advances in event-based technology point to an increasing miniaturization of devices with Figure 6. Dot detections for different disk sectors expressed as a polar histogram. Each sector comprises a range of 18%. The height of the bars represents the number of detections produced in each angular range. The gray area corresponds to the strongly illuminated sectors. Figure 7. Event images generated by accumulating 1000 events under different lighting conditions (from left to right: pitch-dark, 0 lx; dark, 5 lx; and well-lit conditions, 100 lx). www.advancedsciencenews.com www.advintellsyst.com Adv. Intell. Syst. 2025, 2401065 2401065 (6 of 20) © 2025 The Author(s). Advanced Intelligent Systems published by Wiley-VCH GmbH 26404567, 0, Downloaded from https://advanced.onlinelibrary.wiley.com/doi/10.1002/aisy.202401065 by Readcube (Labtiva Inc.), Wiley Online Library on [13/05/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License increasing spatial resolution—now over 1 Mpx. [34,56] In addition, the limited payload of the ornithopter prevents the use of highcapacity batteries. Hence, low energy consumption becomes a critical aspect to be considered. Frame-based camera technology has been in development for years. Consequently, there are a large number of algorithms, datasets, and benchmarks available. On the contrary, event-based vision does not have such a long history, and the direct use of frame-based algorithms and data is not always feasible. Therefore, from a software integration point of view, it is also necessary to analyze the availability of resources and support tools. In this section, we analyze the integrability of event cameras onboard flapping-wing robots by reviewing sensor miniaturization, resolution, energy consumption, and the availability of resources and support tools. Different large-scale flapping-wing aerial platforms have been developed in recent years, such as RoBird, [57] Festo SmartBird (https://festo.com), RoboRaven, [58–61] Beihawk. [62] Other bioinspired platforms with a shorter wingspan are Bat Bot, [63] Dove, [64] ThunderI, [65] and UST-Bird. [66] Besides these platforms, other small-scale and insect-like solutions [67–69] are DelFly, [6,11,70] Flapper Nimbleþby Flapper Drones (https://flapper-drones. com), Nano Hummingbird, [71] KUBeetle-S, [72,73] MetaFly by BionicBird (https://bionicbird.com), and Robobee [45,74–78] (which inspired the robot in ref. [49,50] and Beeþ). [79] The GRIFFIN ERC Advanced Grant project (GRIFFIN (Action 7 882 479): General compliant aerial Robotic manipulation system Integrating Fixed and Flapping-wings to INcrease range and safety. https://griffin-erc-advanced-grant.eu) aims to develop novel ornithopters with advanced perception and manipulation capabilities. One of the platforms developed in this project is E-Flap, [5,80–83] the first flapping-wing platform with onboard processing and perception capabilities, a remarkable payload, and the ability to fly at low speed, enabling the interaction with the environment. [84–86] In addition, the Hybrid robot [87] offers autonomous navigation capabilities with an onboard autopilot capable of switching between fixed-wing and flapping-wing flight modes. Other research projects of great relevance include the PortWings ERC Advanced Grant [88,89] and the DelFly Project. [2,6] To bridge the gap between the experimental setup and real-world conditions, we conducted an analysis to examine the feasibility of integrating the event cameras detailed in Table 2 into several flapping-wing robots (encompassing data from both research literature and commercially available designs) listed in Table 3. The main goal is to provide insights into the possibility of enhancing the performance of the robots and expanding their operational capabilities through the use of advanced event-based perception technology. Table 2. Summary of the main specifications of some event cameras from various companies. Data collected from [18] and from manufactures’datasheets and product briefs. Camera Sensor Weight a) [g] Volume [mm] Consumption Max BW [MEPS] Latency [μs] Spatial Res. [px] iniVation d) DVS128 [26] 65 40 60 25 60 mA@5 v 1 >12 128 128 DVS240 [32] 75 40 60 25 180 mA@5 v 12 >12 240 180 DAVIS240 [32] 75 56 55 27 180 mA@5 v 12 >12 240 180 DAVIS346 –100 40 60 25 180 mA@5 v 12 <1000 346 260 DVXplorer –100 40 60 25 140 mA@5 v 165 <1000 640 480 DVXplorer Lite –75 40 60 25 140 mA@5 v 100 <1000 320 240 DVXplorer Mini –21 29 29 32 140 mA@5 v 450 <1000 640 480 Prophesee EVK1 ATIS [30] 82.5 60 38 50 50–175 mW b) –>3 304 240 Gen3 ATIS –82.5 60 38 50 25–87 mW b) 66 40–200 480 360 Gen3 CD –82.5 60 38 50 36–95 mW b) 66 40–200 640 480 Gen4 CD [27] 82.5 60 38 50 32–84 mW b) 1066 20–150 1280 720 EVK2 Gen4 CD [27] 260 102 58 42 7500 mW 1066 220 1280 720 EVK3 Gen3 CD –112 108 76 45 4500 mW 1.6 Gbps 220 640 480 Gen4 CD [27] 112 108 76 45 4500 mW 1.6 Gbps 220 1280 720 GenX320 –112 108 76 45 4500 mW 1.6 Gbps 220 320 320 EVK4 IMX636Es c) –40 30 30 36 500 mW 1.6 Gbps 220 1280 720 Samsung DVS-Gen2 [33] no case no case 27–50 mW b) 300 65–410 640 480 DVS-Gen3 –no case no case 40 mW b) 600 50 640 480 DVS-Gen4 [34] no case no case 130 mW b) 1200 150 1280 960 CelePixel e) CeleX-IV [156] no case no case –200 >10 768 640 CeleX-V [56] no case no case 400 mW b) 140 >8 1280 800 Insightness f) Rino3 –15 350 350 20–70 mW b) 20 125 320 262 a) Excluding lens; b) Event sensor power consumption; c) IMX636ES realized in collaboration between Sony and Prophesee; d) now part of SynSense; e) now part of OmniVision, Will Semiconductor; f) now part of Sony. www.advancedsciencenews.com www.advintellsyst.com Adv. Intell. Syst. 2025, 2401065 2401065 (7 of 20) © 2025 The Author(s). Advanced Intelligent Systems published by Wiley-VCH GmbH 26404567, 0, Downloaded from https://advanced.onlinelibrary.wiley.com/doi/10.1002/aisy.202401065 by Readcube (Labtiva Inc.), Wiley Online Library on [13/05/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License 4.1. Miniaturization Similarly to frame-based vision systems, the improvement in event vision technology has led to smaller sensors, as evidenced in Figure 8. This clear trend toward sensor miniaturization endorses the possibility of developing smaller and lighter event cameras. Figure 9 shows the evolution in weight and volume of some commercial devices manufactured by iniVation, one of the most relevant event camera companies. There is a widespread trend among several event camera manufacturers toward achieving levels of miniaturization comparable to those of frame-based vision sensors. Relevant is to mention SPECK (https://www.synsense.ai/products/speck-2) by SynSense, the first fully event-based neuromorphic vision system-on-chip (SoC) that weighing a few grams integrates a DVS. To assess the feasibility of integrating event camera models into existing flapping-wing robots, Table 4 Table 3. Summary of the main characteristics of some flapping-wing robots from various research institutions and manufacturers with their specifications. Weight [g] Wingspan [m] Flap. Freq. [Hz] Payload [g] Battery [mAh@LiPo cells] Robobee [45] Harvard U. 0.075 0.035 120 –– Four-wings [157] UW 0.143 0.056 160 –– Beeþ [79] USC 0.095 0.033 100 –– DelFly Micro [6] TU Delft 3.07 0.10 30 0.4 a) 100-120@1S DelFly Explorer [11] TU Delft 20 0.28 10–14 4 a) 250@1S DelFly Nimble [70] TU Delft 29 0.33 17 4 a) 250@1S Flapper Nimbleþ b) Flapper Drones 102 0.49 12–20 25 300@2S N. Hummingbird [71] AeroVironment 19 0.165 –– – KUBeetle-S [73] Konkuk U. 15.8 0.20 18 4 160@1S MetaFly c) BionicBird 9.5 0.29 –– – MetaBird d) BionicBird 9.5 0.33 –– – X-Fly e) BionicBird 12 0.38 –– 60@1S Bat Bot [63] UIUC 93 0.469 10 –– Dove [64] NWPU 220 0.50 12 –– Thunder I [65] NMSU 350 0.70 5.88 –800@3S UST-Bird [66] USTB 83.2 0.80 4 18 180@3S USTB-Hawk [158] USTB 985 1.78 4.108 192 7000@3S RoboRaven I [61] UMD 285 1.168 4 43.8 370@2S RoboRaven II [61] UMD 301.6 1.330 4 80.4 370@2S RoboRaven III [61] UMD 317 1.330 4 71 370@2S RoboRaven IV [61] UMD 438.1 1.168 4 272.9 370@2S RoboRaven V [61] UMD 438.1 1.168 4 272.9 950@2S Beihawk [62] Beihang U. 1200 1.50 10 –– E-Flap [5] U. Seville 510 1.50 5.5 520 450@4S Hybrid [87] U. Seville 930 1.50 3.0 300 450@4S RoBird B. Eagle [57] U. Twente l) 2100 1.76 4 1000 – RoBird P. Falcon [57] U. Twente l) 730 1.12 5.5 100 – SmartBird f) Festo 450 2.00 –– 450@2S BionicOpter g) Festo 175 0.63 15–20 –– eMotionButterflies h) Festo 32 0.50 1–2–7.4@2S BionicFlyingFox i) Festo 580 2.28 –– – BionicSwift j) Festo 42 0.68 –– – BionicBee k) Festo 34 0.24 15–20 –300@1S a) Weight of the vision system; b) https://flapper-drones.com/wp/nimbleplus; c) https://www.bionicbird.com/world/metafly-page; d) https://www.bionicbird.com/world/ metabird-page; e) https://www.bionicbird.com/world/x-fly_details; f) https://www.festo.com/PDF_Flip/corp/Festo_SmartBird/en; g) https://www.festo.com/PDF_Flip/corp/ Festo_BionicOpter/en; h) https://www.festo.com/PDF_Flip/corp/Festo_eMotionButterflies/en; i) https://www.festo.com/PDF_Flip/corp/Festo_BionicFlyingFox/en; j) https://www.festo.com/PDF_Flip/corp/Festo_BionicSwift/en; k) https://www.festo.com/PDF_Flip/corp/Festo_BionicBee/en. l) Spin-off: Clear Flight Solutions, acquired by AERIUM Analytics. www.advancedsciencenews.com www.advintellsyst.com Adv. Intell. Syst. 2025, 2401065 2401065 (8 of 20) © 2025 The Author(s). Advanced Intelligent Systems published by Wiley-VCH GmbH 26404567, 0, Downloaded from https://advanced.onlinelibrary.wiley.com/doi/10.1002/aisy.202401065 by Readcube (Labtiva Inc.), Wiley Online Library on [13/05/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License presents a comparison between the original weights of these cameras and the payload capacities of the ornithopters. While this analysis provides a preliminary insight into the potential for incorporating event-based sensors, a successful integration would also require consideration of additional factors such as: 1) weight distribution; 2) volume; 3) camera connector and cable weights; or 4) weight reduction (e.g., replacement of heavy cases by lighter cases), among others. 4.2. Spatial Resolution High-resolution cameras offer more detailed information about the scene, leading, for example, to finer grain mapping or more accurate detection algorithms. Event cameras are trending toward higher sensor resolutions, as illustrated in Figure 10. However, high resolution involves some drawbacks, such as increased weight and power consumption. There are different opinions on the benefits of using high-resolution event cameras to solve standard computer vision tasks. Higher resolutions involve higher computational resources to process the generated events. In particular, Gehrig and Scaramuzza [90] pointed out that low-resolution event cameras can achieve better performance than high-resolution cameras while using significantly less bandwidth. 4.3. Energy Consumption Ornithopters’payload constraints also affect the onboard batteries. Therefore, the power consumption of the different components mounted onboard requires careful consideration. First, it has been experimentally demonstrated that flapping-wing robots consume in gliding flight mode about 90% less than in flapping flight mode. [91] Thus, planning the flight stages to minimize the flapping and efficiently manage the energy is of high relevance. In addition, the results in ref. [87] suggest a better efficiency of flapping under certain conditions. The importance of gliding to save energy is a major paradigm shift with respect to multirotor platforms. Event cameras suit perfectly in this shift. Whereas frame-based cameras always generate images at a constant rate, Figure 8. Evolution of pixel width of the main event camera designs from 2008 to 2024. DVX stands for DVXplorer. Figure 9. Evolution of weight and volume of some commercial event cameras developed by iniVation. Table 4. Comparison of the weight of event-based vision cameras (Table 2) and the payload capacity of flapping-wing robots (Table 3). A green cell indicates that the camera could be mounted onboard (considering payload restrictions only), while a red cell indicates the opposite. (21 g) DVX Mini (40 g) EVK4 (65 g) DVS128 (75 g) DVS240 (75 g) DAVIS240 (75 g) DVX Lite (82.5 g) EVK1 (100 g) DAVIS346 (100 g) DVX (112 g) EVK3 (260 g) EVK2 DelFly Micro (0.4 g) DelFly Explorer (4 g) DelFly Nimble (4 g) KUBeetle-S (4 g) UST-Bird (18 g) Flapper Nimble+ (25 g) RoboRaven I (43.8 g) RoboRaven III (71 g) RoboRaven II (80.4 g) RoBird P. Falcon (100 g) USTB-Hawk (192 g) RoboRaven IV (272.9 g) RoboRaven V (272.9 g) Hybird (300 g) E-Flap (520 g) RoBird B. Eagle (1000 g) Figure 10. Evolution of the resolution of some main event camera commercial models from 2008 to 2024. DVX stands for DVXplorer. www.advancedsciencenews.com www.advintellsyst.com Adv. Intell. Syst. 2025, 2401065 2401065 (9 of 20) © 2025 The Author(s). Advanced Intelligent Systems published by Wiley-VCH GmbH 26404567, 0, Downloaded from https://advanced.onlinelibrary.wiley.com/doi/10.1002/aisy.202401065 by Readcube (Labtiva Inc.), Wiley Online Library on [13/05/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License on a vibration-isolated platform. The calibration results for the DAVIS346 IMU (davis_imu.yaml), the VectorNav VN200 IMU (vn_imu.yaml), and the Matek H743 Mini V3 autopilot IMU (mavros_imu.yaml) are provided. The original rosbag files (davis_imu.bag, vn_imu.bag, and mavros_imu.bag) used to obtain the results are also provided. A3. C3 Intrinsic Camera Calibration We used the pinhole camera model (parameters: focal length f u ,f y and principal point p u ,p v ) with the radial-tangential distortion (parameters: radial k 1 ,k 2 and tangential p 1 ,p 2 ). Cameras were calibrated with Kalibr (https://github.com/ethz-asl/kalibr) [154,155] using a 6 6 AprilGrid (specifications in aprilgrid.yaml). Calibration results are provided as yaml files. The rosbag files (calibration_indoors.bag, calibration_outdoors.bag, and calibration_outdoors_gps.bag) used for calibration are also provided in case another calibration method is preferable. A4. C4 Extrinsic Calibration Camera-camera and camera-IMU calibrations were also computed with Kalibr. The values of I T D , V T D , A T D ,and E T D (see Figure A3) are provided in the corresponding yaml files. The rest of the transformations can be computed as a composition of the provided matrices. The camera-imu temporal shifts were also computed by Kalibr. The same rosbag files used for the camera intrinsic calibration can be used if another calibration method is preferable. Acknowledgements This work was funded by the European Research Council as part of the GRIFFIN ERC Advanced Grant 2017 (Action 788247). Partial funding was obtained from the Plan Estatal de Investigación Científica y Técnica y de Innovación of the Ministerio de Universidades del Gobierno de Espa˜na (FPU19/04692). The authors thank Mario Hernández for his help with the design of the experimental setups and José Manuel Carmona for his support during the flapping-wing robot flight experiments. The authors extend their gratitude to Geert Folkertsma from the University of Twente, Abdessattar Abdelkefifrom New Mexico State University, Hoang Vu Phan from the École Polytechnique Fédérale de Lausanne, Qiang Fu from the University of Science and Technology Beijing, and Christophe de Wagter and Guido de Croon from the Technical University of Delft for providing valuable details and specifications regarding their flapping-wing platforms. The authors also thank Guillermo Gallego from the Technical University of Berlin for his assistance in gathering specifications of event cameras. R.T. also thanks Sara Ruiz-Moreno for her collaboration and valuable advice. Conflict of Interest The authors declare no conflict of interest. Author Contributions Raul Tapia: conceptualization (lead); investigation (lead); methodology (lead); supervision (lead); visualization (lead); writing—original draft (lead); writing—review & editing (lead). Javier Luna-Santamaria: conceptualization (supporting); investigation (supporting); methodology (supporting); writing—original draft (supporting); writing—review & Table A2. List of indoor and outdoor sequences. Sequence Dataset Duration [s] Size [GB] DAVIS346 ELP VN200 GPS MOCAP boards_indoors_1 TORRICELLI 72.398 2.358 ✓✓✓⨯✓ boards_indoors_2 TORRICELLI 60.398 1.911 ✓✓✓⨯✓ human_indoors_1 TORRICELLI 52.498 1.678 ✓✓✓⨯✓ human_indoors_2 TORRICELLI 68.096 2.159 ✓✓✓⨯✓ boards_outdoors_1 SAETA 70.995 2.434 ✓✓✓⨯⨯ boards_outdoors_2 SAETA 55.198 1.939 ✓✓✓⨯⨯ gps_outdoors_1 SAETA 160.092 1.616 ✓⨯⨯✓⨯ gps_outdoors_2 SAETA 153.487 1.427 ✓⨯⨯✓⨯ Table A3. List of ROS topics. Topic Description Freq. [Hz] /dvs/events DAVIS346 events 30 /dvs/image_raw DAVIS346 grayscale images 40 /dvs/imu DAVIS346 IMU 1000 /elp/image_raw ELP RGB images 30 /vn/imu VectorNav VN200 IMU 200 /mocap/pose OptiTrack pose 120 /mavros/imu Autopilot IMU 10 /mavros/gps Autopilot GPS 3 Figure A3. Reference frames used for external calibration: DAVIS346 camera {D} and IMU {I}, ELP camera {E}, VectorNav VN200 {V}, and autopilot {A}. www.advancedsciencenews.com www.advintellsyst.com Adv. Intell. Syst. 2025, 2401065 2401065 (16 of 20) © 2025 The Author(s). Advanced Intelligent Systems published by Wiley-VCH GmbH 26404567, 0, Downloaded from https://advanced.onlinelibrary.wiley.com/doi/10.1002/aisy.202401065 by Readcube (Labtiva Inc.), Wiley Online Library on [13/05/2025]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License editing (supporting). Ivan Gutierrez Rodriguez: conceptualization (supporting); investigation (supporting); methodology (supporting); writing —original draft (supporting); writing—review & editing (supporting). Juan Pablo Rodríguez-Gómez: conceptualization (supporting); investigation (supporting); methodology (supporting); writing—original draft (supporting); writing—review & editing (supporting). 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