We present Target Netgrams as a visualization technique for radial layouts of graphs. Inspired by manually created target sociograms, we propose an annulus-constrained stress model that aims to position nodes onto the annuli between adjacent circles for indicating their radial hierarchy, while maintaining the network structure (clusters and neighborhoods) and improving readability as much as possible. This is achieved by having more space on the annuli than traditional layout techniques. By adapting stress majorization to this model, the layout is computed as a constrained least square optimization problem. Additional constraints (e.g., parent-child preservation...

This paper presents an innovative approach that combines optimization and simulation techniques for solving scheduling problems under uncertainty. We introduce an Opt–Sim closed-loop feedback framework (Opt–Sim) based on a sliding-window method, where a simulation model is used for evaluating the optimized solution with inherent uncertainties for scheduling activities. The specific problem tackled in this paper, refers to the airport capacity management under uncertainty, and the Opt–Sim framework is applied to a real case study (Paris Charles de Gaulle Airport, France). Different implementations of the Opt–Sim framework were tested based on: parameters for driving the...

Anomaly detection is critical in Intelligent Transportation Systems (ITS) due to its significant impact on safety. This paper introduces a Bayesian probabilistic framework for identifying anomalous trajectories without explicitly modeling anomalies reliably. The framework can be adapted according to the sensor quality, balancing speed and accuracy, and avoids out-of-sample performance issues commonly encountered in deep learning methods. By reducing the dimensionality of time series data using Functional Principal Component Analysis (FPCA), a prior distribution of FPCA scores is learned and continuously updated in an online manner. We conducted numerical experiments to...

In response to escalating drone traffic, this paper investigates the interplay between drone density, communication frequency, and collision avoidance. We utilize artificial potential fields as a guidance mechanism for drones navigating in dense airspace. Through numerical simulations involving two to nine drones, we analyze how communication frequency correlates with collision rates as drone density increases. Our findings reveal the nuanced relationship between these variables, showing that communication frequency requirements increase with drone density for collision-free navigation. Furthermore, we present a theoretical framework predicting the relationship between...

In this study, a vector field-based guidance algorithm is presented for tactical deconfliction in urban air mobility. The proposed guidance algorithm mimics fluid flow around obstacles to generate a vector field that guide vehicles through flight corridors while ensuring collision avoidance. The algorithm is flexible and can be tailored to fit the needs of current air traffic regulations. The conflict resolution and corridor following capability of the proposed method is evaluated through extensive flight test campaign. Flight tests are conducted in a scaled urban environment for two different scenarios: the conflict between of two medical drones in the same corridor and...

This paper introduces FLighthouse, an open-source python [1] framework designed for development and testing of multi-agent guidance and path planning algorithms. FLighthouse is composed of three key components: SceneBuilder for intuitive 2D use case creation, guidance algorithms integration, and an execution module with visualization and post processing tools. The proposed framework can be used with a wide range of guidance and path planning algorithms and allows for execution and comparison of metrics for different guidance approaches. The framework supports execution in both simulation and real flights. The visualization tool is equipped with analysis tools for detailed...

This paper presents a novel velocity planning method in displacement-time graphs with multiple constraints and optimization goals using deep reinforcement learning. The method formulates the velocity planning problem as a reinforcement learning task with state representation including time, position, velocity, acceleration, and distances to each obstacle triangle representative. The action space is discretized within allowable accelerations, and the kinematics ensure velocity constraints during state transitions. The advantage of this method lies in its independence from scene-specific tuning, and exhibiting robustness in various complex scenarios. Comparative analysis...

This paper investigates the use of deep reinforcement learning to act as closed-loop guidance for quadrotors and the ability for such a system to be trained entirely in simulation before being transferred for use on a real quadrotor. It improves upon previous work where velocity-based deep reinforcement learning was used to guide the motion of spacecraft. Here, an acceleration-based closed-loop deep reinforcement learning guidance system is developed and compared to previous work. In addition, state augmentation is included due to dynamics delays present. Simulated results show acceleration-based deep reinforcement learning closed-loop guidance has significant performance...

The recent developments in fast additive manufacturing, such as rapid 3d-printing of composite materials, presents opportunities for the manufacturing of small unmanned aerial vehicles (UAVs) that are tailor-made for the specific mission needs. This paper presents a novel framework for mission-oriented, modular design and construction of mini-UAVs using additive manufacturing. The outcome is a manufacturing method which is suitable for a parametric design that can be tailored for the specific mission requirements and rapidly constructed using additive manufacturing techniques. We show how additive manufacturing enables an agile design methodology by allowing fast and...