Abstract--The aim of this paper is to design a new type of grasping and perching unmanned aerial vehicle (UA V), Flexbee, characterized by its soft vector-propulsion nozzle (SVPN). Compared to previous UA Vs, Flexbee integrates flight, grasping, and perching functionalities into the four SVPNs, offering advantages such as decoupled position and attitude control, high structural reuse, and strong adaptability for grasping and perching. A dynamics model of Flexbee has been developed, and the nonlinear coupling issue of the moment has been resolved through lin-earization of the equivalent moment model. Hierarchical control strategy was employed to design the controllers for Flexbee's two operational modes. Finally, flight, grasping, and perching experiments were conducted to validate Flexbee's kinematic capabilities and the effectiveness of the control strategy. UL TI-ROTOR unmanned aerial vehicles (UA Vs), with their three-dimensional maneuverabilities, have demonstrated remarkable effectiveness in environments that are difficult for humans to reach [1]-[5]. As people's requirements for UA V endurance performance and adaptability to complex environments offer greater advantages, compared with large UA Vs, small UA Vs have the characteristics of small size, light weight, low cost, and high maneuverability, which play a greater advantage in complex environments [6]-[8].

Air-land bimodal vehicles provide a promising solution for navigating complex environments by combining the flexibility of aerial locomotion with the energy efficiency of ground mobility. However, planning dynamically feasible, smooth, collision-free, and energy-efficient trajectories remains challenging due to two key factors: 1) unknown dynamic disturbances in both aerial and terrestrial domains, and 2) the inherent complexity of managing bimodal dynamics with distinct constraint characteristics. This paper proposes a disturbance-aware motion-planning framework that addresses this challenge through real-time disturbance estimation and adaptive trajectory generation. The framework comprises two key components: 1) a disturbance-adaptive safety boundary adjustment mechanism that dynamically determines the feasible region of dynamic constraints for both air and land modes based on estimated disturbances via a disturbance observer, and 2) a constraint-adaptive bimodal motion planner that integrates disturbance-aware path searching to guide trajectories toward regions with reduced disturbances and B-spline-based trajectory optimization to refine trajectories within the established feasible constraint boundaries. Experimental validation on a self-developed air-land bimodal vehicle demonstrates substantial performance improvements across three representative disturbance scenarios, achieving an average 33.9% reduction in trajectory tracking error while still maintaining superior time-energy trade-offs compared to existing methods.

Stop-rotor aircraft have long been proposed as the ideal vertical takeoff and landing (VTOL) aircraft for missions with equal time spent in both flight regimes, such as agricultural monitoring, search and rescue, and last-mile delivery. Featuring a central lifting surface that rotates in VTOL to generate vertical thrust and locks in forward flight to generate passive lift, the stop-rotor offers the potential for high efficiency across both modes. However, practical implementation has remained infeasible due to aerodynamic and stability conflicts between flight modes. In this work, we present SPERO (Stopped-Penta Rotor), a stop-rotor uncrewed aerial vehicle (UAV) featuring a flipping and latching wing, an active center of pressure mechanism, thrust vectored counterbalances, a five-rotor architecture, and an eleven-state machine flight controller coordinating geometric and controller reconfiguration. Furthermore, SPERO establishes a generalizable design and control framework for stopped-rotor UAVs. Together, these innovations overcome longstanding challenges in stop-rotor flight and enable the first stable, bidirectional transition between VTOL and forward flight.

This paper presents Duawlfin, a drone with unified actuation for wheeled locomotion and flight operation that achieves efficient, bidirectional ground mobility. Unlike existing hybrid designs, Duawlfin eliminates the need for additional actuators or propeller-driven ground propulsion by leveraging only its standard quadrotor motors and introducing a differential drivetrain with one-way bearings. This innovation simplifies the mechanical system, significantly reduces energy usage, and prevents the disturbance caused by propellers spinning near the ground, such as dust interference with sensors. Besides, the one-way bearings minimize the power transfer from motors to propellers in the ground mode, which enables the vehicle to operate safely near humans. We provide a detailed mechanical design, present control strategies for rapid and smooth mode transitions, and validate the concept through extensive experimental testing. Flight-mode tests confirm stable aerial performance comparable to conventional quadcopters, while ground-mode experiments demonstrate efficient slope climbing (up to 30°) and agile turning maneuvers approaching 1g lateral acceleration. The seamless transitions between aerial and ground modes further underscore the practicality and effectiveness of our approach for applications like urban logistics and indoor navigation. All the materials including 3-D model files, demonstration video and other assets are open-sourced at https://sites.google.com/view/Duawlfin.

After creating a stir with the 200 Neo, DJI is back at it with another innovative drone, the Flip. It has a first-of-a-kind folding design and shrouded propellers to keep people safe. It also integrates 3D infrared obstacle detection to track subjects and has a long list of impressive features. With a camera borrowed from the Mini 4 Pro, the Flip can take high-quality 4K 60p video indoors or out with little risk. It comes with vlogger-friendly features like Direction Track and Quickshots for social media.

Fuzz testing effectively uncovers software vulnerabilities; however, it faces challenges with Autonomous Systems (AS) due to their vast search spaces and complex state spaces, which reflect the unpredictability and complexity of real-world environments. This paper presents a universal framework aimed at improving the efficiency of fuzz testing for AS. At its core is SaFliTe, a predictive component that evaluates whether a test case meets predefined safety criteria. By leveraging the large language model (LLM) with information about the test objective and the AS state, SaFliTe assesses the relevance of each test case. We evaluated SaFliTe by instantiating it with various LLMs, including GPT-3.5, Mistral-7B, and Llama2-7B, and integrating it into four fuzz testing tools: PGFuzz, DeepHyperion-UAV, CAMBA, and TUMB. These tools are designed specifically for testing autonomous drone control systems, such as ArduPilot, PX4, and PX4-Avoidance. The experimental results demonstrate that, compared to PGFuzz, SaFliTe increased the likelihood of selecting operations that triggered bug occurrences in each fuzzing iteration by an average of 93.1\%. Additionally, after integrating SaFliTe, the ability of DeepHyperion-UAV, CAMBA, and TUMB to generate test cases that caused system violations increased by 234.5\%, 33.3\%, and 17.8\%, respectively. The benchmark for this evaluation was sourced from a UAV Testing Competition.

Birds exhibit a variety of flight styles, primarily classified as flapping, which is characterized by rapid up-and-down wing movements, and soaring, which involves gliding with wings outstretched. Each species usually performs specific flight styles, and this has been argued in terms of morphological and physiological adaptation. However, it remains a challenge to evaluate the contribution of each factor to the difference in flight styles. In this study, using phenotypic data from 635 migratory bird species, such as body mass, wing length, and breeding periods, we quantified the relative importance of each feature using Feature Importance and SHAP values, and used them to construct weighted L1 distance matrices and construct NJ trees. Comparison with traditional phylogenetic logistic regression revealed similarity in top-ranked features, but also differences in overall weight distributions and clustering patterns in NJ trees. Our results highlight the complexity of constructing a biologically useful distance matrix from correlated phenotypic features, while the complementary nature of these weighting methods suggests the potential utility of multi-faceted approaches to assessing feature contributions.

Advanced Air Mobility aircraft require energy efficient flight plans to be economically viable. This paper defines minimum energy direct trajectories between waypoints for Lift+Cruise electric Vertical Take-Off and Landing (eVTOL) aircraft. Energy consumption is optimized over accelerated and cruise flight profiles with consideration of mode transitions. Because eVTOL operations start and end in hover for vertical take-off and landing, hover waypoints are utilized. Energy consumption is modeled as a function of airspeed for each flight mode, providing the basis to prove energy optimality for multi-mode traversal. Wind magnitude and direction dictate feasibility of straight-line traversal because Lift+Cruise aircraft point into the relative wind direction while hovering but also have a maximum heading rate constraint. Energy and power use for an experimentally validated QuadPlane small eVTOL aircraft are characterized with respect to airspeed and acceleration in all flight modes. Optimal QuadPlane traversals are presented. Constraints on acceleration and wind are derived for straight-line QuadPlane traversal. Results show an optimal QuadPlane $500m$ traversal between hover waypoints saves $71\%$ energy compared to pure vertical flight traversal for a representative case study with a direct $4m/s$ crosswind. Energy optimal eVTOL direct trajectory definition with transitions to and from hover is novel to this work. Future work should model three-dimensional flight and wind as well as optimize maneuver primitives when required.

Recent advances in Unmanned Aerial Vehicles (UAVs) have resulted in their quick adoption for wide a range of civilian applications, including precision agriculture, biosecurity, disaster monitoring and surveillance. UAVs offer low-cost platforms with flexible hardware configurations, as well as an increasing number of autonomous capabilities, including take-off, landing, object tracking and obstacle avoidance. However, little attention has been paid to how UAVs deal with object detection uncertainties caused by false readings from vision-based detectors, data noise, vibrations, and occlusion. In most situations, the relevance and understanding of these detections are delegated to human operators, as many UAVs have limited cognition power to interact autonomously with the environment. This paper presents a framework for autonomous navigation under uncertainty in outdoor scenarios for small UAVs using a probabilistic-based motion planner. The framework is evaluated with real flight tests using a sub 2 kg quadrotor UAV and illustrated in victim finding Search and Rescue (SAR) case study in a forest/bushland. The navigation problem is modelled using a Partially Observable Markov Decision Process (POMDP), and solved in real time onboard the small UAV using Augmented Belief Trees (ABT) and the TAPIR toolkit. Results from experiments using colour and thermal imagery show that the proposed motion planner provides accurate victim localisation coordinates, as the UAV has the flexibility to interact with the environment and obtain clearer visualisations of any potential victims compared to the baseline motion planner. Incorporating this system allows optimised UAV surveillance operations by diminishing false positive readings from vision-based object detectors.

The usage of drones and rovers helps to overcome the limitations of traditional agriculture which has been predominantly human-intensive, for carrying out tasks such as removal of weeds and spraying of fertilizers and pesticides. Drones and rovers are helping to realize precision agriculture and farmers with improved monitoring and surveying at affordable costs. Major benefits have come for vertical farming and fields with irrigation canals. However, drones have a limitation of flight time due to payload constraints. Rovers have limitations in vertical farming and obstacles like canals in agricultural fields. To meet the different requirements of multiple terrains and vertical farming in agriculture, we propose an autonomous hybrid drone-rover vehicle that combines the advantages of both rovers and drones. The prototype is described along with experimental results regarding its ability to avoid obstacles, pluck weeds and spray pesticides.