The ability to fast climb and descend is crucial for Unmanned Aerial Vehicle (UAV) applications in the mountains. The slower descent speed will affect the UAV’s working efficiency in reaching the rescue area. However, during the fast descent of the rotorcraft, a chaotic flow field rampages as the rotorcraft falls into its wake flow. This is known as the vortex ring. Therefore, the safe descent velocity of consumer UAVs is usually limited to approximately 3m/s. This limitation reduces the potential of UAVs to execute tasks in mountainous and plateau regions. To broaden the task capability constrained by the maximum descending speed, it is necessary to jointly analyze the flow field and the energy consumption during descending. Existing research mainly focused on how to avoid entering the vortex ring instead of offering sufficient power to fly with it. In this paper, in order to achieve an efficient rotorcraft for rescuing in mountainous and plateaus, we break through the maximum-descending-speed of a coaxial rotors UAV. Hence, a power consumption managing pipeline is proposed to extend the power tolerance of the UAV. Specifically, a theoretic model for the coaxial rotors is proposed to analyze the induced velocity and energy consumption during vertical descending. Then, the theoretic model is verified to be consistent with the Computational Fluid Dynamics (CFD) and wind tunnel experiment results. Finally, we optimized the tolerance of the power and dynamic system according to the theoretic model. With this pipeline, our real-time flight achieved 8m/s controlled vertical-descent-speed (CVDS), which is a leading result in both quadrotors and coaxial UAVs.

Small target motion detection within complex natural environments is an extremely challenging task for autonomous robots. Surprisingly, the visual systems of insects have evolved to be highly efficient in detecting mates and tracking prey, even though targets occupy as small as a few degrees of their visual fields. The excellent sensitivity to small target motion relies on a class of specialized neurons, called small target motion detectors (STMDs). However, existing STMD-based models are heavily dependent on visual contrast and perform poorly in complex natural environments, where small targets generally exhibit extremely low contrast against neighboring backgrounds. In this article, we develop an attention-and-prediction-guided visual system to overcome this limitation. The developed visual system comprises three main subsystems, namely: 1) an attention module; 2) an STMD-based neural network; and 3) a prediction module. The attention module searches for potential small targets in the predicted areas of the input image and enhances their contrast against a complex background. The STMD-based neural network receives the contrast-enhanced image and discriminates small moving targets from background false positives. The prediction module foresees future positions of the detected targets and generates a prediction map for the attention module. The three subsystems are connected in a recurrent architecture, allowing information to be processed sequentially to activate specific areas for small target detection. Extensive experiments on synthetic and real-world datasets demonstrate the effectiveness and superiority of the proposed visual system for detecting small, low-contrast moving targets against complex natural environments.

On 15th May 2021, the Tianwen-1 (TW-1) successfully landed on the surface of Mars within the southern Utopia Planitia. It delivered a rover named Zhurong equipped with ground penetrating radar (GPR), a device that can investigate the thickness and structure of the geological layers below the martian surface. The hyperbola method is commonly used for extracting dielectric property variations with depth from GPR data to aid the interpretation of the subsurface structure and material composition. This study analyzes the advantages and drawbacks of three hyperbola methods with different geometric models. Next, it proposes a new method aiming to use the highly precise, yet complex geometric model and address issues caused by its solving process. We also analyze the influencing factors contributing to measurement errors and design corresponding criteria for measurement point selection to mitigate errors. The average error of the proposed method is less than 5% for a depth of up to 9 m. We employed the proposed method to obtain the dielectric constant distribution in the shallow surface layer of the TW-1 landing zone to a depth of up to 16 m. The dielectric constant is mainly concentrated between 2 and 8 m and increases gradually with depth. Below 2 m, the dielectric constant is about 5.1 and the density is about 2.5 g/cm3. The stratification and density variation can be inferred from the dielectric constant distribution diagram.

Discriminating small moving objects within complex visual environments is a significant challenge for autonomous micro-robots that are generally limited in computational power. By exploiting their highly evolved visual systems, flying insects can effectively detect mates and track prey during rapid pursuits, even though the small targets equate to only a few pixels in their visual field. The high degree of sensitivity to small target movement is supported by a class of specialized neurons called small target motion detectors (STMDs). Existing STMD-based computational models normally comprise four sequentially arranged neural layers interconnected via feedforward loops to extract information on small target motion from raw visual inputs. However, feedback, another important regulatory circuit for motion perception, has not been investigated in the STMD pathway and its functional roles for small target motion detection are not clear. In this article, we propose an STMD-based neural network with feedback connection (feedback STMD), where the network output is temporally delayed, then fed back to the lower layers to mediate neural responses. We compare the properties of the model with and without the time-delay feedback loop and find that it shows a preference for high-velocity objects. Extensive experiments suggest that the feedback STMD achieves superior detection performance for fast-moving small targets, while significantly suppressing background false positive movements which display lower velocities. The proposed feedback model provides an effective solution in robotic visual systems for detecting fast-moving small targets that are always salient and potentially threatening.

Collision detection is one of the most challenging tasks for unmanned aerial vehicles (UAVs). This is especially true for small or micro-UAVs due to their limited computational power. In nature, flying insects with compact and simple visual systems demonstrate their remarkable ability to navigate and avoid collision in complex environments. A good example of this is provided by locusts. They can avoid collisions in a dense swarm through the activity of a motion-based visual neuron called the Lobula giant movement detector (LGMD). The defining feature of the LGMD neuron is its preference for looming. As a flying insect's visual neuron, LGMD is considered to be an ideal basis for building UAV's collision detecting system. However, existing LGMD models cannot distinguish looming clearly from other visual cues, such as complex background movements caused by UAV agile flights. To address this issue, we proposed a new model implementing distributed spatial-temporal synaptic interactions, which is inspired by recent findings in locusts' synaptic morphology. We first introduced the locally distributed excitation to enhance the excitation caused by visual motion with preferred velocities. Then, radially extending temporal latency for inhibition is incorporated to compete with the distributed excitation and selectively suppress the nonpreferred visual motions. This spatial-temporal competition between excitation and inhibition in our model is, therefore, tuned to preferred image angular velocity representing looming rather than background movements with these distributed synaptic interactions. Systematic experiments have been conducted to verify the performance of the proposed model for UAV agile flights. The results have demonstrated that this new model enhances the looming selectivity in complex flying scenes considerably and has the potential to be implemented on embedded collision detection systems for small or micro-UAVs.

In low altitude flights, powerlines are one of the most dangerous threats for Unmanned Aerial Vehicles (UAVs), as they are widely spread in human society but too thin to be perceived effectively. With the thriving of UAV technology, powerline detection has become a hot issue in numerous researches. Most of the literature, however, extracted powerlines in static images while didn't from the perspective of dynamic scenes. In nature, the motion-sensitive neurons of various insects provide us with ideal examples of perceiving environmental information through the dynamic features of image motion. An outstanding one is the locusts' looming-sensitive neuron, namely the Lobula Giant Movement Detector (LGMD). The locusts being able to fly in a dense swarm without chaos collisions is believed largely owing to the LGMD neuron. Its specialized preference for looming threats and compact morphology inspired a series of robotic researches including the modeling of UAVs' collision detecting systems. However, existing LGMD models are more reactive to large size object movements while not that effective for small ones, making them incompetent for powerline detection. To address the above-mentioned challenges, we propose a neural computational model which fuses a line-attention module with the LGMD model in three different manners, i.e. (a) the preprocessing of input images, (b) processing motion information, and (c) inter-neuron synaptic mappings. Systematic experiments demonstrated that the attention mechanism, especially when discriminating features of image motion in the third manner, excellently extracts visual cues about powerlines and helps to improve collision avoidance performance even in complex backgrounds.

The sense and avoid capability enables insects to fly versatilely and robustly in dynamic and complex environment. Their biological principles are so practical and efficient that inspired we human imitating them in our flying machines. In this paper, we studied a novel bio-inspired collision detector and its application on a quadcopter. The detector is inspired from Lobula giant movement detector (LGMD) neurons in the locusts, and modeled into an STM32F407 Microcontroller Unit (MCU). Compared to other collision detecting methods applied on quadcopters, we focused on enhancing the collision accuracy in a bio-inspired way that can considerably increase the computing efficiency during an obstacle detecting task even in complex and dynamic environment. We designed the quadcopter's responding operation to imminent collisions and tested this bio-inspired system in an indoor arena. The observed results from the experiments demonstrated that the LGMD collision detector is feasible to work as a vision module for the quadcopter's collision avoidance task.