Aircraft Deconfliction via Mathematical Programming: Review and Insights

Computer-aided air traffic management has increasingly attracted the interest of the operations research community. This includes, among other tasks, the design of decision support tools for the detection and resolution of conflict situations during flight. Even if numerous optimization approaches have been proposed, there has been little debate toward homogenization. We synthesize the efforts made by the operations research community in the past few decades to provide mathematical models to aid conflict detection and resolution at a tactical level. Different mathematical representations of aircraft separation conditions are presented in a unifying analysis. The models, which hinge on these conditions, are then revisited, providing insight into their computational performance.

Midfrontal theta oscillation encodes haptic delay

Haptic technologies aim to simulate tactile or kinesthetic interactions with a physical or virtual environment in order to enhance user experience and/or performance. However, due to stringent communication and computational needs, the user experience is influenced by delayed haptic feedback. While delayed feedback is well understood in the visual and auditory modalities, little research has systematically examined the neural correlates associated with delayed haptic feedback. In this paper, we used electroencephalography (EEG) to study sensory and cognitive neural correlates caused by haptic delay during passive and active tasks performed using a haptic device and a computer screen. Results revealed that theta power oscillation was significantly higher at the midfrontal cortex under the presence of haptic delay. Sensory correlates represented by beta rebound were found to be similar in the passive task and different in the active task under the delayed and synchronous conditions. Additionally, the event related potential (ERP) P200 component is modulated under the haptic delay condition during the passive task. The P200 amplitude significantly reduced in the last 20% of trials during the passive task and in the absence of haptic delay. Results suggest that haptic delay could be associated with increased cognitive control processes including multi-sensory divided attention followed by conflict detection and resolution with an earlier detection during the active task. Additionally, haptic delay tends to generate greater perceptual attention that does not significantly decay across trials during the passive task.

COLREGs Compliant Fuzzy-Based Collision Avoidance System for Multiple Ship Encounters

As the number of ships for marine transportation increases with the advancement of global trade, encountering multiple ships in marine traffic becomes common. This situation raises the risk of collision of the ships; hence, this paper proposes a novel Fuzzy-logic based intelligent conflict detection and resolution algorithm, where the collision courses and possible avoiding actions are analysed by considering ship motion dynamics and the input and output fuzzy membership functions are derived. As a conflict detection module, the Collision Risk (CR) is measured for each ship by using a scaled nondimensional Distance to the Closest Point of Approach (DCPA) and Time to the Closest Point of Approach (TCPA) as inputs. Afterwards, the decisions for collision avoidance are made based on the calculated CR, encountering angle and relative angle of each ship measured from others. In this regard, the rules for the Fuzzy interface system are defined in accordance with the COLREGs, and the whole system is implemented on the MATLAB Simulink platform. In addition, to deal with the multiple ship encounters, the paper proposes a unique maximum-course and minimum-speed change approach for decision making, which has been found to be efficient to solve Imazu problems, and other complicated multiple-ship encounters.

COLREGs Compliant Fuzzy-based Collision Avoidance System for Multiple Ship Encounters

As the number of ships for marine transportation increases with the advancement of global trade, encountering multiple ships in marine traffic becomes common. This situation raises the risk of collision of the ships; hence this paper proposes a novel Fuzzy-logic based intelligent conflict detection and resolution algorithm, where the collision courses and possible avoiding actions are analyzed by considering ship motion dynamics and the input and output fuzzy membership functions are derived. As a conflict detection module, the Collision Risk (CR) is measured for each ship by using a scaled nondimensional Distance to the Closest Point of Approach (DCPA) and Time to the Closest Point of Approach (TCPA) as inputs. Afterwards, the decisions for collision avoidance are made based on the calculated CR, encountering angle and relative angle of each ship measured from others. In this regard, the rules for the Fuzzy interface system are defined in accordance with the COLREGs, and the whole system is implemented on the MATLAB Simulink platform. In addition, to deal with the multiple ship encounters, the paper proposes a unique maximum-course and minimum-speed change approach for decision making, which has been found to be efficient to solve Imazu problems, and other complicated multiple-ship encounters.

Aerial Robotics: State-based Conflict Detection and Resolution (Detect and Avoid) in High Traffic Densities and Complexities

Purpose of Review A lot of research into decentralised, state-based conflict detection and resolution, or detect and avoid algorithms has been executed. This paper explains the essential properties of state-based conflict detection and reviews the work in the context of applications for not only manned but also unmanned aerial vehicles, where this might be applied relatively soon. Recent Findings Lately, based on several reviews of a variety of published algorithms, a selection has been implemented and simulated in extremely high traffic densities for comparison. Summary The modified voltage potential has been surprisingly efficient, even compared with more complex algorithms or adaptations, as is apparent from looking at macroscopic metrics like domino effect, efficiency and safety. This indicates that to this date, it is so far the most suitable algorithm for the detect and avoid role for unmanned aerial vehicles in urban airspaces, or other areas where a high density is expected.

Conflict Resolution in Mechatronic Collaborative Design Using Category Theory

Due to the multitude of disciplines involved in mechatronic design, heterogeneous languages and expert models are used to describe the system from different domain-specific views. Despite their heterogeneity, these models are highly interrelated. As a consequence, conflicts among expert models are likely to occur. In order to ensure that these models are not contradictory, the necessity to detect and manage conflicts among the models arises. Detecting these inconsistencies at an early stage significantly reduces the amount of engineering activities re-execution. Therefore, to deal with this issue, a formal framework relying upon mathematical concepts is required. The mathematical theory, namely category theory (CT), is considered as an efficient tool to provide a formal and unifying framework supporting conflict detection and management. This paper proposes a comprehensive methodology that allows conflict detection and resolution in the context of mechatronic collaborative design. CT is used in order to explicitly capture the inconsistencies occurred between the disparate expert models. By means of this theory, the conflicts can be detected and handled in an easy and formal way. Our proposed approach is applied to a collaborative scenario concerning the electro-mechanical actuator (EMA) of the aileron.

Velocity Obstacle Based Conflict Avoidance in Urban Environment with Variable Speed Limit

Current investigations into urban aerial mobility, as well as the continuing growth of global air transportation, have renewed interest in conflict detection and resolution (CD&R) methods. The use of drones for applications such as package delivery, would result in traffic densities that are orders of magnitude higher than those currently observed in manned aviation. Such densities do not only make automated conflict detection and resolution a necessity, but will also force a re-evaluation of aspects such as coordination vs. priority, or state vs. intent. This paper looks into enabling a safe introduction of drones into urban airspace by setting travelling rules in the operating airspace which benefit tactical conflict resolution. First, conflicts resulting from changes of direction are added to conflict resolution with intent trajectory propagation. Second, the likelihood of aircraft with opposing headings meeting in conflict is reduced by separating traffic into different layers per heading–altitude rules. Guidelines are set in place to make sure aircraft respect the heading ranges allowed at every crossed layer. Finally, we use a reinforcement learning agent to implement variable speed limits towards creating a more homogeneous traffic situation between cruising and climbing/descending aircraft. The effects of all of these variables were tested through fast-time simulations on an open source airspace simulation platform. Results showed that we were able to improve the operational safety of several scenarios.