Abstract--General Visual Inspection is a manual inspection process regularly used to detect and localise obvious damage on the exterior of commercial aircraft. There has been increasing demand to perform this process at the boarding gate to minimise the downtime of the aircraft and automating this process is desired to reduce the reliance on human labour . Automating this typically requires estimating a camera's pose with respect to the aircraft for initialisation but most existing localisation methods require infrastructure, which is very challenging in uncontrolled outdoor environments and within the limited turnover time (approximately 2 hours) on an airport tarmac. Additionally, many airlines and airports do not allow contact with the aircraft's surface or using UA Vs for inspection between flights, and restrict access to commercial aircraft. Hence, this paper proposes an on-site method that is infrastructure-free and easy to deploy for estimating a pan-tilt-zoom camera's pose and localising scan images. This method initialises using the same pan-tilt-zoom camera used for the inspection task by utilising a Deep Convolutional Neural Network fine-tuned on only synthetic images to predict its own pose. We apply domain randomisation to generate the dataset for fine-tuning the network and modify its loss function by leveraging aircraft geometry to improve accuracy. We also propose a workflow for initialisation, scan path planning, and precise localisation of images captured from a pan-tilt-zoom camera. We evaluate and demonstrate our approach through experiments with real aircraft, achieving root-mean-square camera pose estimation errors of less than 0.24 m and 2 for all real scenes. General Visual Inspection (GVI) is a widely used technique as part of regular inspections of aircraft such as during pre-flight inspections on an airport tarmac or during maintenance usually performed in a hanger. This process involves visual examinations of the aircraft's exterior for noticeable damage or irregularities and provides a means for early detection of typical air-frame defects [2].

During a press conference with Japans Prime Minister Shigeru Ishiba, President Trump said relations with North Korea will happen. North Korean dictator Kim Jong Un has overseen tests of newly developed AI-powered suicide drones and called for their increased production, North Korean state media said Thursday. Photos released from the communist country show Kim inspecting new upgraded reconnaissance drones that are capable of detecting various tactical targets and enemy activities on land and at sea, KCNA state news agency said. Kim said unmanned control and AI capability must be the top priorities in modern arms development. North Korean leader Kim Jong Un has overseen tests of newly developed AI-powered suicide drones and called for their increased production, North Korean state media said.

Summary: This paper states an interesting and novel idea- that learned shape bases could outperform hand-crafted heuristic functions. On the flip side, though, the method and experimental setups make drawing clear conclusions difficult, diminishing the impact. Post-rebuttal: The authors gave convincing responses to questions about the atlasnet comparison and about the number of parameters. So the final review is increased from 6 to 7. Originality: -As stated above, the paper has an interesting and novel high-level key idea. Whether the proposed method is really learning shape bases rather than using heuristic bases is a matter of interpretation, though.

We propose a multibody model of a freewing UAV. This model allows obtaining simulations of the UAV's behaviour and, in the future, to design a control law stabilising the entire flight envelope (hovering and forward flight). We also describe the realisation of a prototype and a comparison of possible methods for estimating the UAV's states. With this prototype, we report on experimental hovering flights with a non-linear incremental dynamic inversion controller to stabilise the wing and a proportional derivative controller for the fuselage stabilization.

Recent conditional 3D completion works have mainly relied on CLIP or BERT to encode textual information, which cannot support complex instruction. Meanwhile, large language models (LLMs) have shown great potential in multi-modal understanding and generation tasks. Inspired by the recent advancements of LLM, we present Volume Patch LLM (VP-LLM), which leverages LLMs to perform conditional 3D completion in a single-forward pass. To integrate a 3D model into the LLM tokenization configuration, the incomplete 3D object is first divided into small patches that can be encoded independently. These encoded patches are then fed into an LLM along with the text prompt, instructing the LLM to capture the relations between these patches as well as injecting semantic meanings into the 3D object. Our results demonstrate a strong ability of LLMs to interpret complex text instructions and understand 3D objects, surpassing state-of-the-art diffusion-based 3D completion models in generation quality.

Perching with winged Unmanned Aerial Vehicles has often been solved by means of complex control or intricate appendages. Here, we present a simple yet novel method that relies on passive wing morphing for crash-landing on trees and other types of vertical poles. Inspired by the adaptability of animals' and bats' limbs in gripping and holding onto trees, we design dual-purpose wings that enable both aerial gliding and perching on poles. With an upturned nose design, the robot can passively reorient from horizontal flight to vertical upon a head-on crash with a pole, followed by hugging with its wings to perch. We characterize the performance of reorientation and perching in terms of impact speed and angle, pole material, and size. The robot robustly reorients at impact angles above 15{\deg} and speeds of 3 m/s to 9 m/s, and can hold onto various pole types larger than 28% of its wingspan in diameter. We demonstrate crash-perching on tree trunks with an overall success rate of 71%. The method opens up new possibilities for the use of aerial robots in applications such as inspection, maintenance, and biodiversity conservation.

This paper presents a novel VTOL UAV that owns a link connecting four rotors and a fuselage by a passive joint, allowing the control of the rotor's tilting angle by adjusting the rotors' thrust. This unique structure contributes to eliminating additional actuators, such as servo motors, to control the tilting angles of rotors, resulting in the UAV's weight lighter and simpler structure. We first derive the dynamical model of the newly designed UAV and analyze its controllability. Then, we design the controller that leverages the tiltable link with four rotors to accelerate the UAV while suppressing a deviation of the UAV's angle of attack from the desired value to restrain the change of the aerodynamic force. Finally, the validity of the proposed control strategy is evaluated in simulation study.

The design and control of winged aircraft and drones is an iterative process aimed at identifying a compromise of mission-specific costs and constraints. When agility is required, shape-shifting (morphing) drones represent an efficient solution. However, morphing drones require the addition of actuated joints that increase the topology and control coupling, making the design process more complex. We propose a co-design optimisation method that assists the engineers by proposing a morphing drone's conceptual design that includes topology, actuation, morphing strategy, and controller parameters. The method consists of applying multi-objective constraint-based optimisation to a multi-body winged drone with trajectory optimisation to solve the motion intelligence problem under diverse flight mission requirements. We show that co-designed morphing drones outperform fixed-winged drones in terms of energy efficiency and agility, suggesting that the proposed co-design method could be a useful addition to the aircraft engineering toolbox.

Aerial-aquatic vehicles are capable to move in the two most dominant fluids, making them more promising for a wide range of applications. We propose a prototype with special designs for propulsion and thruster configuration to cope with the vast differences in the fluid properties of water and air. For propulsion, the operating range is switched for the different mediums by the dual-speed propulsion unit, providing sufficient thrust and also ensuring output efficiency. For thruster configuration, thrust vectoring is realized by the rotation of the propulsion unit around the mount arm, thus enhancing the underwater maneuverability. This paper presents a quadrotor prototype of this concept and the design details and realization in practice.

Existing multirotor-based cargo transportation does not maintain a constant cargo attitude due to underactuation; however, fragile payloads may require a consistent posture. The conventional method is also cumbersome when loading cargo, and the size of the cargo to be loaded is limited. To overcome these issues, we propose a new fully-actuated multirotor unmanned aerial vehicle platform capable of translational motion while maintaining a constant attitude. Our newly developed platform has a cubic exterior and can freely place cargo at any point on the flat top surface. However, the center-of-mass (CoM) position changes when cargo is loaded, leading to undesired attitudinal motion due to unwanted torque generation. To address this problem, we introduce a new model-free center-of-mass position estimation method inspired by the extremum-seeking control (ESC) technique. Experimental results are presented to validate the performance of the proposed estimation method, effectively estimating the CoM position and showing satisfactory constant-attitude flight performance.