With the promise of greater safety and adaptability, modular reconfigurable uncrewed air vehicles have been proposed as unique, versatile platforms holding the potential to replace multiple types of monolithic vehicles at once. State-of-the-art rigidly assembled modular vehicles are generally two-dimensional configurations in which the rotors are coplanar and assume the shape of a "flight array". We introduce the Dodecacopter, a new type of modular rotorcraft where all modules take the shape of a regular dodecahedron, allowing the creation of richer sets of configurations beyond flight arrays. In particular, we show how the chosen module design can be used to create three-dimensional and fully actuated configurations. We justify the relevance of these types of configurations in terms of their structural and actuation properties with various performance indicators. Given the broad range of configurations and capabilities that can be achieved with our proposed design, we formulate tractable optimization programs to find optimal configurations given structural and actuation constraints. Finally, a prototype of such a vehicle is presented along with results of performed flights in multiple configurations.

Security checkpoints are an important matter of concern for airport operators. When functioning effectively, they not only maintain the airport overall safety at a high level, but also provide passengers with a positive airport experience. The perceived quality of service at the airport greatly depends on the time spent by passengers at the security lines. To ensure optimal safety performance, screening lines have a limited capacity of passengers they can handle. Thus, to prevent extended waiting times for passengers, airports can only adjust the number of simultaneously open check lines. The airport operator must establish optimal schedules for opening security checkpoints and allocating necessary staff. Building upon a prior study focused on predicting the flow of passengers through the security checkpoints, this paper explores simulated annealing algorithm in conjunction with a queue simulator and an integer programming algorithm to establish the most effective opening schedule for security checkpoints based on the prediction given by this previous study. The presented approach also determines the best allocation for dedicated staff based on the forecasted passenger flow. This approach limits the number of open security lines and ensures awaiting time below the maximum limit of 45 min set by the airport. It also complies with the work regulations that security agents are subject to.

Access to reduced-gravity environments is a cornerstone of space research, enabling scientific experiments in space-like conditions. While parabolic flights have long served as an accessible platform for microgravity studies, their reliance on manual piloting limits precision and repeatability. This paper introduces an autonomous flight control framework designed to execute reduced-gravity maneuvers in large fixed-wing aircraft. The proposed system regulates all four phases of the maneuver by commanding a reference acceleration profile. This approach enables precise control over the aircraft’s acceleration, ensuring consistent reduced gravity conditions critical for experimental applications. The control architecture comprises three specialized controllers: one each for tangential and normal acceleration regulation and another for minimizing angle-of-attack variations to dampen pitch oscillations. The proposed framework is evaluated on a nonlinear Boeing 747 model implemented in MATLAB Simulink. Simulation results show that the controller maintains residual accelerations within ± 0.02 g for zero-, lunar-, and Martian-gravity manoeuvres, matching the error margins reported in published flight data. Key challenges are addressed, such as non-minimum phase dynamics, altitude-dependent air density variations, and pitch oscillations at the center of gravity. These findings contribute to the advancement of autonomous flight control for more reliable and precise reduced-gravity research.

Urban drone operations are exposed to unpredictable risks, including engine failure and deliberate signal interference. A recent and ongoing disruption in Jeddah, Saudi Arabia, has seen widespread GPS spoofing that misleads devices by hundreds of kilometers, illustrating how fragile unmanned aerial vehicle (UAV) operations can become when over-reliant on GNSS-based navigation. Such disruptions highlight the urgent need for contingency planning in drone traffic management systems. This study introduces a safety-aware pre-flight path planning framework that proactively integrates emergency landing and GPS fallback options into UAV trajectory pre-flight planning. The planner considers proximity to predesignated emergency landing zones, communication coverage, and airspace restrictions, enabling UAVs to safely complete their operations. The approach is evaluated across realistic mission profiles such as delivery, inspection, and surveillance. Results show that the planner successfully maintains mission feasibility while embedding emergency readiness throughout each flight. This work contributes toward safer, failure-resilient drone integration in urban airspace, ensuring that contingency plans are proactively incorporated into path planning before the failure even occurs.

A method for constructing homogeneous polynomial Lyapunov functions is presented for linear time-varying or switched-linear systems and the class of nonlinear systems that can be represented as such. The method uses a simple recursion based on the Kronecker product to generate a hierarchy of related dynamical systems, whose first element is the system under study and the second element is the well-known Lyapunov differential equation. It is then proven that a quadratic Lyapunov function for the system at one level in the hierarchy, which can be found via semidefinite programming, is a homogeneous polynomial Lyapunov function for the system at the base level in the hierarchy. Searching for Lyapunov functions of the foregoing kind is equivalent to searching for homogeneous polynomial Lyapunov functions via the formulation of sum-of-squares programs. The quadratic perspective presented in this paper enables the easy development of procedures to compute bounds on pointwise-in-time system metrics, such as peak norms, system stability margins, and many other performance measures. The applications of the theory to analyzing an aircraft model, on the one hand, and an experimental aerospace vehicle, on the other hand, are presented. The theory can be comprehended with a first course on state-space control systems and an elementary knowledge of convex programming.

The trade-off between resolution and speed represents a significant challenge when extrusion-based additive manufacturing (AM) is used for large-format additive manufacturing (LFAM). This paper presents an analysis of a new material extrusion process, named selective sheet extrusion (SSE), that aims to decouple these parameters. Unlike traditional single-nozzle material extrusion processes, SSE utilizes a single, very wide nozzle through which extrusion is controlled by an array of dynamically actuated teeth at the nozzle outlet. This allows the system to deposit a selectively structured sheet of material with each pass, potentially enabling the deposition of an entire layer of a part in a single pass. An analysis of the theoretical performance of the SSE technology, in terms of speed and material efficiency in comparison with single-nozzle extrusion systems, predicted speed increases of 2–3 times for the geometries that were explored. The analysis was then validated through experimental work that indicated a normalized improvement in print speed of between 2.3 and 2.5 times using a proof-of-concept SSE prototype. The SSE concept expands the opportunity frontier of LFAM technologies by enabling enhanced print speeds, while maintaining higher resolutions at scale. This enhancement in speed and/or resolution could have significant benefits, especially in large-scale prints that benefit from enhanced internal resolution.

To create a self-repairing 3D printer, it must continue operating even after experiencing corruption. This work focuses on developing a method to effectively utilize a malfunctioning printer for reliable printing. This method can be applied by the printer itself for self-repair and enhance the reliability of commercial 3D printers. We achieve this by modeling the dynamics of the corrupted printer using a machine learning model that by observing one trajectory infers the corrupted printer dynamics to improve its accuracy. Our method is evaluated on a digital twin of the 3D printer, demonstrating its capability to enable the printer to operate reliably, even when encountering new corruptions not encountered during training. The scripts are public on https://github.com/piotrpiekos/adaptive-printer.

Airport access mode disruptions, such as a subway shutdown, threaten the whole passenger door-to-door journey. When such a disruptive event occurs, knowledge of passengers' delays would help the airport operation centre to decide if a departure flight should be delayed. This paper proposes a tactical flight rescheduling at an airport to minimise the number of stranded passengers while considering operational constraints. An integer linear programming formulation of the problem is presented. Constraints such as terminal capacities, maximal runway throughput, minimum turnaround time or minimum transfer time for connecting passengers are considered. An exact and heuristic resolution is proposed and compared to a study case around Paris-Charles de Gaulle airport. The new schedule satisfies the operational constraints and reduces up to 60% of the number of stranded passengers with moderate deviation from the initial planning.

Coral reefs are home to a variety of species, and their preservation is a popular study area; however, monitoring them is a significant challenge, for which the use of robots offers a promising answer. The purpose of this study is to analyze the current techniques and tools employed in coral reef monitoring, with a focus on the role of robotics and its potential in transforming this sector. Using a systematic review methodology examining peer-reviewed literature across engineering and earth sciences from the Scopus database focusing on “robotics” and “coral reef” keywords, the article is divided into three sections: coral reef monitoring, robots in coral reef monitoring, and case studies. The initial findings indicated a variety of monitoring strategies, each with its own advantages and disadvantages. Case studies have also highlighted the global application of robotics in monitoring, emphasizing the challenges and opportunities unique to each context. Robotic interventions driven by artificial intelligence and machine learning have led to a new era in coral reef monitoring. Such developments not only improve monitoring but also support the conservation and restoration of these vulnerable ecosystems. Further research is required, particularly on robotic systems for monitoring coral nurseries and maximizing coral health in both indoor and open-sea settings.

Advanced machine learning algorithms require platforms that are extremely robust and equipped with rich sensory feedback to handle extensive trial-and-error learning without relying on strong inductive biases. Traditional robotic designs, while well-suited for their specific use cases, are often fragile when used with these algorithms. To address this gap – and inspired by the vision of enabling curiosity-driven baby robots – we present a novel robotic limb designed from scratch. Our design has a hybrid soft-hard structure, high redundancy with rich non-contact sensors (exclusively cameras), and easily replaceable failure points. Proof-of-concept experiments using two contemporary reinforcement learning algorithms on a physical prototype demonstrate that our design is able to succeed in a simple target-finding task even under simulated sensor failures, all with minimal human oversight during extended learning periods. We believe this design represents a concrete step toward more tailored robotic designs for achieving general-purpose, generally intelligent robots.

The ability for agents to arbitrarily control a safety-critical vehicle system comes with the necessity for this system to feature a "flight envelope protection system," should the agent erroneously or deliberately drive the system into hazardous situations. This need is particularly true of systems evolving in environments containing obstacles, either physical or otherwise. The "flight envelope protection system" aims to take over the vehicle’s guidance before collision with the obstacle is unavoidable. Also named run-time assurance, the system generates "safe exit trajectories" in real time. This work provides a novel solution approach that follows a two-step process: First, reference backup trajectories are generated, together with trajectory regulation control laws, using a comprehensive but possibly time-consuming and unreliable trajectory optimization software. Second, we generate backup trajectories in real time by activating the regulation loop of a neighboring reference trajectory and computing the resulting trajectory. Numerical examples illustrate the approach. The work builds upon that previously reported in [1].

This paper presents a tunable multi-threshold micro-electromechanical inertial switch with adjustable threshold capability. The demonstrated device combines the advantages of accelerometers in providing quantitative acceleration measurements and g-threshold switches in saving power when in the inactive state upon experiencing acceleration below the thresholds. The designed proof-of-concept device with two thresholds consists of a cantilever microbeam and two stationary electrodes placed at different positions in the sensing direction. The adjustable threshold capability and the effect of the shock duration on the threshold acceleration are analytically investigated using a nonlinear beam model. Results are shown for the relationships among the applied bias voltage, the duration of shock impact, and the tunable threshold. The fabricated prototypes are tested using a shock-table system. The analytical results agree with the experimental results. The designed device concept is very promising for the classification of the shock and impact loads in transportation and healthcare applications.

This article presents a comprehensive development and testing of a run time assurance (RTA) filter for a torque-controlled spacecraft in free rotational motion with torque actuation limits for which the objective is to enforce a line-of-sight constraint. A nondeterministic dynamical model is considered for the spacecraft that accounts for disturbance torques, and a guaranteed safe RTA filter is constructed using recent results from mixed monotone systems theory for reachable set overapproximations and optimization-based computation of invariant sets. The RTA filter ensures that the system is always within reach of an a priori safe terminal set by computing reachable sets of the dynamics online at run time. The approach is demonstrated on the Autonomous Spacecraft Testing of Robotic Operations in Space (ASTROS) platform at the Georgia Institute of Technology, Atlanta, GA, USA. In the experiment, potentially unsafe inputs are provided by a human, and the RTA filter overrides the human-commanded inputs when necessary to guarantee safety. The controller update rate for the ASTROS platform is about 10 Hz, while the RTA filter requires about 1 ms of computation time per controller update.

Aircraft trajectory is one of the most fundamental objects in air traffic management. Its optimization is essential to ensure efficient and sustainable aviation. This survey proposes to study all the phases of a flight, from its prediction several days before day of flight to the landing of the aircraft, including also the study of a possible emergency situation. Each phase of flight raises different issues and is subject to particular constraints. These guide the choice of potentially usable optimization methods. This study proposes, from the context, the issues, and existing studies, a methodology to identify the most appropriate solution algorithms for optimizing each phase of flight. This methodology is based on 5 evaluation criteria: optimality, computing time, adaptability, memory usage, and multi-trajectories. Finally, thanks to it, some methods are compared based on their consistency with solving problem associated to each phase of flight.

We introduce a new class of quadratic functions based on a hierarchy of linear time-varying (LTV) dynamical systems. These quadratic functions in the higher order space can be also seen as a non-homogeneous polynomial Lyapunov functions for the original system, i.e the first system in the hierarchy. These non-homogeneous polynomials are used to obtain accurate outer approximation for the reachable set given the initial condition and less conservative bounds for the impulse response peak of linear, possibly time-varying systems. In addition, we pose an extension to the presented approach to construct invariant sets that are not necessarily Lyapunov functions. The introduced methods are based on elementary linear systems theory and offer very much flexibility in defining arbitrary polynomial Lyapunov functions and invariant sets for LTV systems.

This paper reports a low-g in-plane MEMS inertial switch with multiple acceleration thresholds in multiple sensing directions. Designed for essential monitoring, this device targets low-g accelerations for health and safety applications. The device design utilizes the standard silicon-on-insulator micromachining process (SOIMUMPs) to simplify both fabrication and packaging. Through extensive testing, we demonstrate the switch ability to detect accelerations ranging from 45g to 88g. Despite minor discrepancies due to fabrication imperfections, the experimental results closely match the finite- element simulation results.

In safety or mission-critical environments, Run-Time Assurance is about performing actions with full consciousness of opt-out options and the willingness to exercise such options when they are about to run-out. This paper proposes a new framework for run time assurance for nonlinear dynamical systems that enjoy intelligent interaction with complex environments. This framework exploits the concept of transverse dynamics to compute closed-loop control laws for "Plan B" trajectories in complex obstacle environments in real-time, thereby increasing the confidence in their value, should they be used. Three-state and six-state examples illustrate the approach in complex obstacle environments.

There is growing interest in commercial aircraft formation flight as a means of reducing both airspace congestion and the carbon footprint of air transportation. Wake vortex surfing has been researched extensively and proven to have significant fuel-saving benefits, however, commercial air transportation has yet to take advantage of these formation benefits due to understandable safety concerns. Formation contingency scenarios are much more complex than those of individual aircraft and have not yet been studied in depth. This work investigates the utility of mixed-integer linear programming and optimization in generating aircraft escape paths for formation contingency planning. Two high-altitude commercial aircraft formation scenarios are presented; formation join and formation escape. Pilot expertise is used to evaluate the optimized paths. The linear programming formulation results compare well with pilot intuition and confirm viability of pilot-generated plans from previous work. The model proves useful both in presenting solutions not previously considered and in evaluating separation requirements for improvement of escape path planning.