Tactical Conflict Research Articles

Implementing and testing a U-space system: lessons learnt

Within the framework of the European Union’s Horizon 2020 research and innovation program, one of the main goals of the Labyrinth project, which ended on May 2023, was to develop and test the Conflict Management services of a U-space-based Unmanned Traffic Management (UTM) system. The U-space ConOps provides a high-level description of the architecture, requirements and functionalities of these systems, but the implementer has a certain degree of freedom in aspects like the techniques used or some policies and procedures. The current document describes some of those implementation decisions. The prototype included, at least in a basic version, part of the services defined by the ConOps, namely e-identification, Tracking, Geo-awareness, Drone Aeronautical Information Management, Geo-fence Provision, Operation Plan Preparation/Optimization, Operation Plan Processing, Strategic and Tactical Conflict Resolution, Emergency Management, Monitoring, Traffic Information and Legal Recording. Besides, a web app interface was developed for the operator/pilot. The system was tested in simulations and real visual line of sight (VLOS) and beyond VLOS (BVLOS) flights, with both vertical take-off and landing (VTOL) and fixed-wing platforms, while assisting final users interested in incorporating drones to support their daily tasks. The three-year experience developing and testing the environment provided many lessons at different levels: functionalities, compatibility, procedures, information, usability, ground control station (GCS) integration and aircrew roles during the mission.

Implementing and Testing a U-Space System: Lessons Learnt

Within the framework of the European Union’s Horizon 2020 research and innovation program, one of the main goals of the Labyrinth project was to develop and test the Conflict Management services of a U-space-based Unmanned Traffic Management (UTM) system. The U-space concept of operations (ConOps) provides a high-level description of the architecture, requirements and functionalities of these systems, but the implementer has a certain degree of freedom in aspects like the techniques used or some policies and procedures. The current document describes some of those implementation decisions. The prototype included part of the services defined by the ConOps, namely e-identification, Tracking, Geo-awareness, Drone Aeronautical Information Management, Geo-fence Provision, Operation Plan Preparation/Optimization, Operation Plan Processing, Strategic Conflict Resolution, Tactical Conflict Resolution, Emergency Management, Monitoring, Traffic Information and Legal Recording. Moreover, a Web app interface was developed for the operator/pilot. The system was tested in simulations and real visual line of sight (VLOS) and beyond VLOS (BVLOS) flights, with both vertical take-off and landing (VTOL) and fixed-wing platforms, while assisting final users interested in incorporating drones to support their tasks. The development and testing of the environment provided lessons at different levels: functionalities, compatibility, procedures, information, usability, ground control station (GCS) integration and aircrew roles.

Tactical Conflict Prevention Strategies in Public–Private Partnerships: Lessons from Experts

Tactical Conflict Prevention Strategies in Public–Private Partnerships: Lessons from Experts

Tactical Conflict Avoidance of Manned Traffic - Display Concepts for UAV Pilots

The number of collisions between manned and unmanned aerial vehicles (UAVs) has risen due to the increased use of UAVs. Pilots of manned aircraft can visually detect the usually much smaller UAVs only very late or not at all, which is why UAVs are required to avoid manned aircraft in lower airspace by most regulations. For the task of collision avoidance, the UAV pilot needs appropriate traffic awareness. While operating UAVs within the visual line of sight (VLOS), the UAV pilot can monitor the surrounding traffic himself, but operation beyond the visual line of sight (BVLOS) requires an additional airspace observer or a supporting detect-and-avoid system. In this work, a traffic display concept supporting UAV pilots is derived and proposed using the human-centered design process. Commonly used collision avoidance displays of manned and unmanned aviation are reviewed and the user context for the proposed traffic display is introduced. An online survey is conducted to gather user preferences for several 2D graphical elements of the traffic display. 25 experienced UAV pilots participated in the survey. The data is analyzed using Gwet’s AC1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$AC_1$$\\end{document} agreement coefficient. Overall, fair agreement can be observed with individual coefficients for different 2D graphical elements ranging from poor to almost perfect agreement. With a developed heuristic, preferred 2D graphical elements are extracted and combinations reviewed to derive a complete traffic display design. The proposed traffic display design, based on user preferences, will be implemented for further evaluation in human-in-the-loop experiments.

D-FFP: A directional force field protocol for the efficient management of aerial conflicts between UAVs

Airspace management is currently experiencing significant legislative changes in many countries due to the high complexity of the procedures involved, and the need to guarantee high levels of flight safety, particularly in urban air spaces. In this regard, both strategic and tactical conflict management solutions are being devised to handle conflicts between different UAVs. In this paper, we propose a solution for tactical conflict management that seeks to avoid collisions between UAVs while introducing low complexity in terms of calculations involved and protocol requirements. Our solution specifically addresses multirotor UAVs, and seeks to accelerate the handling of possible conflicts in aerial traffic corridors, which are the most critical scenarios envisioned for the near future due to the high density of UAVs in such spaces. Experimental results obtained in both MATLAB and our realistic drone simulator, ArduSim, evidence the advantages of our solution against previous approaches when solving collisions in a 2D plane. In addition, we propose a 3D extension to handle flight conflicts in more complex scenarios while guaranteeing that floor and ceiling flight limits are adequately enforced.

Adaptive Multi-Agent Reinforcement Learning Solver for Tactical Conflict Resolution in Diverse Urban Airspace Configurations

Adaptive Multi-Agent Reinforcement Learning Solver for Tactical Conflict Resolution in Diverse Urban Airspace Configurations

Probabilistic modeling and reasoning of conflict detection effectiveness by tracking systems towards safe urban air mobility operations

This study targets the scenario where centralized tracking systems provide tactical conflict detection for urban air mobility (UAM) flights. In this scenario, the interaction between airspace design and tracking system performances, and its impact on the effectiveness of conflict detection have not been addressed in the literature. To overcome this gap, this study aims at achieving probabilistic modeling and reasoning analysis, to provide references for stakeholders in the design of urban airspace and the deployment of flight tracking systems. A framework integrating multiple probabilistic methods is established. We formulate the event tree of pair-wise aircraft encounters with conflict detection provided by the tracking system. Then Monte Carlo simulation is performed by using an agent-based tool that we develop, to quantify the probability of event occurrences. Finally, Bayesian Networks models are built to infer the dependencies of conflict detection effectiveness on the airspace design and tracking system performances. The outcomes of this study demonstrate improvement in conflict detection that better tracking system performances can provide, and the impact of airspace design under different tracking configurations. The results can be used in the UAM traffic network planning, and systems standardization for UAM flight tracking, towards safe urban air traffic.

CONFLICT RESOLUTION STRATEGY BASED ON DEEP REINFORCEMENT LEARNING FOR AIR TRAFFIC MANAGEMENT

With the continuous increase in flight flows, the flight conflict risk in the airspace has increased. Aiming at the problem of conflict resolution in actual operation, this paper proposes a tactical conflict resolution strategy based on Deep Reinforcement Learning. The process of the controllers resolving conflicts is modelled as the Markov Decision Process. The Deep Q Network algorithm trains the agent and obtains the resolution strategy. The agent uses the command of altitude adjustment, speed adjustment, or heading adjustment to resolve a conflict, and the design of the reward function fully considers the air traffic control regulations. Finally, simulation experiments were performed to verify the feasibility of the strategy given by the conflict resolution model, and the experimental results were statistically analyzed. The results show that the conflict resolution strategy based on Deep Reinforcement Learning closely reflected actual operations regarding flight safety and conflict resolution rules.

Deep reinforcement learning in service of air traffic controllers to resolve tactical conflicts

Dense and complex air traffic requires higher levels of automation than those exhibited by tactical conflict detection and resolution (CD&R) tools that air traffic controllers (ATCOs) use today: AI tools can act on their own initiative, increasing the capacity of ATCOs to control higher volumes of traffic. However, given that the air traffic control (ATC) domain is safety critical, requires AI systems to which ATCOs are comfortable to relinquishing control, guaranteeing operational integrity and automation adoption. Two major factors towards this goal are quality of solutions and operational transparency. ResoLver, the system that this article presents, addresses these challenges using an enhanced graph convolutional reinforcement learning method operating in a multiagent setting where each agent – representing a flight – performs a CD&R task, jointly with other agents. We show that ResoLver can provide high-quality solutions with respect to stakeholders interests (air traffic controllers and airspace users), addressing also operational transparency issues, which have been validated by ATCOs in simulated real-world settings.

Urban air mobility: from complex tactical conflict resolution to network design and fairness insights

Urban Air Mobility (UAM) has the potential to revolutionize transportation. It will exploit the third dimension to help smooth ground traffic in densely populated areas. This new paradigm in mobility will require methods to ensure safety and maximize efficiency. We propose to use mathematical optimization to address tactical deconfliction in UAM. Our approach is envisioned as a way of modelling and solving tactical conflicts, but also as means for assessing future infrastructures with potential utility in the design phase. We leverage envisioned UAM corridors to provide a mathematical definition of vehicle separation. A mathematical formulation is then proposed, which minimizes the total deviation from flight schedules needed to avoid loss of pairwise separation. The deconfliction is based on both airborne adjustments (through speed changes) and ground delays (holds relative to the scheduled take-off). Our experimental setup includes three use cases standing for different sources of conflicts and three synthetic UAM network topologies, which represent heterogeneous realistic scenarios. Vehicle and network capabilities are represented through model parameters, which allows us to analyse their impact on the quality of the deconfliction. Finally, insightful comparisons between our approach and both a local deconfliction and a fairness-oriented version are provided.

Tactical conflict resolution in urban airspace for unmanned aerial vehicles operations using attention-based deep reinforcement learning

Unmanned aerial vehicles (UAVs) have gained much attention from academic and industrial areas due to the significant number of potential applications in urban airspace. A traffic management system for these UAVs is needed to manage this future traffic. Tactical conflict resolution for unmanned aerial systems (UASs) is an essential piece of the puzzle for the future UAS Traffic Management (UTM), especially in very low-level (VLL) urban airspace. Unlike conflict resolution in higher altitude airspace, the dense high-rise buildings are an essential source of potential conflict to be considered in VLL urban airspace. In this paper, we propose an attention-based deep reinforcement learning approach to solve the tactical conflict resolution problem. Specifically, we formulate this task as a sequential decision-making problem using Markov Decision Process (MDP). The double deep Q network (DDQN) framework is used as a learning framework for the host drone to learn to output conflict-free maneuvers at each time step. We use the attention mechanism to model the individual neighbor's effect on the host drone, endowing the learned conflict resolution policy to be adapted to an arbitrary number of neighboring drones. Lastly, we build a simulation environment with various scenarios covering different types of encounters to evaluate the proposed approach. The simulation results demonstrate that our proposed algorithm provides a reliable solution to minimize secondary conflict counts compared to learning and non-learning-based approaches under different traffic density scenarios.

Physiological costs to behavioural plasticity in a swordtail fish: clues to its evolutionary maintenance and loss

Behavioural plasticity may require energetically expensive sensory and neural adaptations to detect, process and respond to social cues. These costs could lead to selection against behavioural plasticity and its eventual loss. We found that males from the behaviourally plastic alternative reproductive tactic (ART) in the swordtail fish Xiphophorus multilineatus had relatively larger brains, in addition to a trade-off with testes size, that was not detected in the males from the behaviourally fixed ART. Given these costs, we consider the hypothesis that plasticity in mating behaviours is maintained due to intralocus tactical conflict, where a shared genome can constrain one or both ARTs from evolving to their optima. When we reduced any potential for intralocus tactical conflict by removing the behaviourally fixed ART from long-term breeding mesocosms, males from the behaviourally plastic ART were less plastic and had smaller brains compared to their counterparts from control mesocosms (both male ARTs). We also detected evidence for a genetic correlation between the ARTs for behaviour, which is required for intralocus conflict. Our findings suggest that intralocus tactical conflict could be maintaining behavioural plasticity, in which case behavioural plasticity may not be adaptative in some cases.

An ETA-Based Tactical Conflict Resolution Method for Air Logistics Transportation

Air logistics transportation has become one of the most promising markets for the civil drone industry. However, the large flow, high density, and complex environmental characteristics of urban scenes make tactical conflict resolution very challenging. Existing conflict resolution methods are limited by insufficient collision avoidance success rates when considering non-cooperative targets and fail to take the temporal constraints of the pre-defined 4D trajectory into consideration. In this paper, a novel reinforcement learning-based tactical conflict resolution method for air logistics transportation is designed by reconstructing the state space following the risk sectors concept and through the use of a novel Estimated Time of Arrival (ETA)-based temporal reward setting. Our contributions allow a drone to integrate the temporal constraints of the 4D trajectory pre-defined in the strategic phase. As a consequence, the drone can successfully avoid non-cooperative targets while greatly reducing the occurrence of secondary conflicts, as demonstrated by the numerical simulation results.

Live Trials of Dynamic Geo-Fencing for the Tactical Avoidance of Hazard Areas

The project City-ATM, launched by the German Aerospace Center DLR in 2018, aims to integrate new airspace users like unmanned aerial vehicles into uncontrolled airspace. An air traffic management and traffic flow control concept were developed. The phase 1 demonstration in 2019 showed a bridge inspection featuring, amongst other elements, the flight of several drones in a limited area, beyond visual line of sight operation, strategic flight planning considering strategic geo-fences and tactical conflict detection. To enrich the base concept from phase 1 with more U-Space services, the concept has been extended for phase 2 by dynamic geo-fencing for the tactical avoidance of danger spots. This paper describes how dynamic geo-fences can be modeled around a hazard area, how the dynamic opening and closure of these fences can be distributed to relevant systems, and how the fences are considered by on-ground and already in air vehicles. The complete concept has been implemented and tested at the National Experimental Test Center in Cochstedt. Finally, the execution of successful flight trials is described.

Unifying Tactical Conflict Prevention, Detection, and Resolution Methods in Non-Orthogonal Constrained Urban Airspace

The use of small aircraft for a wide range of missions in urban airspace is expected to increase in the future. In Europe, efforts have been invested into developing a unified system, called U-space, to manage aircraft in dense very-low-level urban airspace. The Metropolis II project aimed to research what degree of centralisation an air traffic management system should use in such airspace. The paper at hand is a follow-up, and investigates improvements that can be brought to the tactical conflict prevention, detection, and resolution module of such a system in order to harmonise these components with an organic high-density U-space environment. The proposed improvements are: the prioritisation of vertical conflict prevention in intersections, the use of intent in detecting and resolving conflicts, and the use of heading-based manoeuvres in open airspace. Results show that the use of intent information in the conflict detection process, as well as the implementation of suitable tactical prevention procedures, can greatly increase airspace safety. Furthermore, the experiments revealed that the effectiveness of conflict resolution algorithms is highly dependent on the airspace rules and structure. This reiterates the potential for increasing the safety and efficiency of operations within constrained airspace if the tactical separation modules are unified with the other components of air traffic management systems for U-space.

Tactical Conflict Solver Assisting Air Traffic Controllers Using Deep Reinforcement Learning

To assist air traffic controllers (ATCOs) in resolving tactical conflicts, this paper proposes a conflict detection and resolution mechanism for handling continuous traffic flow by adopting finite discrete actions to resolve conflicts. The tactical conflict solver (TCS) was developed based on deep reinforcement learning (DRL) to train a TCS agent with the actor–critic using a Kronecker-factored trust region. The agent’s actions are determined by the ATCOs’ instructions, such as altitude, speed, and heading adjustments. The reward function is designed in accordance with air traffic control regulations. Considering the uncertainty in a real-life situation, this study characterised the deviation of the aircraft’s estimated position to improve the feasibility of conflict resolution schemes. A DRL environment was developed with the actual airspace structure and traffic density of the air traffic operation simulation system. Results show that for 1000 test samples, the trained TCS could resolve 87.1% of the samples. The conflict resolution rate decreased slightly to 81.2% when the airspace density was increased by a factor of 1.4. This research can be applied to intelligent decision-making systems for air traffic control.

A Tactical Conflict Detection and Resolution Method for En Route Conflicts in Trajectory-Based Operations

In trajectory-based operation (TBO), the four-dimensional trajectories (4DTs) of aircraft are shared with all flight-related stakeholders, which makes flights visible and controllable and helps flights arrive at a location within a fixed time range. With the technical support from TBO, the operation of flights in a route sector will be more efficient and the air traffic volume will increase. However, more flights will also result in more flight conflicts. In this study, a deterministic conflict detection and resolution (CDR) module is established to assist air traffic controllers in detecting and resolving high-density conflicts rapidly and in advance. In the conflict detection (CD) submodule, a spatial data structure with low time complexity, the R tree algorithm, is used. R tree can effectively reduce the comparison number between the 4DTs of all aircraft. The experiment results show that the computing time of the R tree presents a logarithmic curve with the increase in the number of aircraft and the efficiency of the CD is more significantly improved. In the conflict resolution (CR) submodule, considering the aircraft performance and terrain constraints, the Monte Carlo tree search (MCTS) algorithm is proposed to solve the problem of huge search space and to quickly provide an effective resolution policy for pairwise conflict. The simulation results in dense airspace indicate that the MCTS-based CR algorithm has good performance in terms of safety and efficiency.

Review and Simulation of Counter-UAS Sensors for Unmanned Traffic Management.

Noncollaborative surveillance of airborne UAS (Unmanned Aerial System) is a key enabler to the safe integration of UAS within a UTM (Unmanned Traffic Management) ecosystem. Thus, a wide variety of new sensors (known as Counter-UAS sensors) are being developed to provide real-time UAS tracking, ranging from radar, RF analysis and image-based detection to even sound-based sensors. This paper aims to discuss the current state-of-the art technology in this wide variety of sensors (both academically and commercially) and to propose a set of simulation models for them. Thus, the review is focused on identifying the key parameters and processes that allow modeling their performance and operation, which reflect the variety of measurement processes. The resulting simulation models are designed to help evaluate how sensors’ performances affect UTM systems, and specifically the implications in their tracking and tactical services (i.e., tactical conflicts with uncontrolled drones). The simulation models cover probabilistic detection (i.e., false alarms and probability of detection) and measurement errors, considering equipment installation (i.e., monostatic vs. multistatic configurations, passive sensing, etc.). The models were integrated in a UTM simulation platform and simulation results are included in the paper for active radars, passive radars, and acoustic sensors.

Automated Conflict Management Framework Development for Autonomous Aerial and Ground Vehicles

The number of aerial- and ground-based unmanned vehicles and operations is expected to significantly expand in the near future. While aviation traditionally has an excellent safety record in managing conflicts, the current approaches will not be able to provide safe and efficient operations in the future. This paper presents the development of a novel framework integrating autonomous aerial and ground vehicles to facilitate short- and mid-term tactical conflict management. The methodology presents the development of a modular web service framework to develop new conflict management algorithms. This new framework is aimed at managing urban and peri-urban traffic of unmanned ground vehicles and assisting the introduction of urban air mobility into the same framework. A set of high-level system requirements is defined. The incremental development of two versions of the system prototype is presented. The discussions highlight the lessons learnt while implementing and testing the conflict management system and the introduced version of the stop-and-go resolution algorithm and defines the identified future development directions. Operation of the system was successfully demonstrated using real hardware. The developed framework implements short- and mid-term conflict management methodologies in a safe, resource efficient and scalable manner and can be used for the further development and the evaluation of various methods integrating aerial- and ground-based autonomous vehicles.

A potential role for restricted intertactical heritability in preventing intralocus conflict.

Intralocus sexual conflict, which arises when the same trait has different fitness optima in males and females, reduces population growth rates. Recently, evolutionary biologists have recognized that intralocus conflict can occur between morphs or reproductive tactics within a sex and that intralocus tactical conflict might constrain tactical dimorphism and population growth rates just as intralocus sexual conflict constrains sexual dimorphism and population growth rates. However, research has only recently focused on sexual and tactical intralocus conflict simultaneously, and there is no formal theory connecting the two. We present a graphical model of how tactical and sexual conflict over the same trait could constrain both sexual and tactical dimorphisms. We then use Coho salmon (Oncorhynchus kisutch), an important species currently protected under the Endangered Species Act, to investigate the possibility of simultaneous sexual and tactical conflict. Larger Coho males gain access to females through fighting while smaller males are favored through sneaking tactics, and female reproductive success is positively correlated with length. We tested for antagonistic selection on length at maturity among sexes and tactics and then used parent–offspring regression to calculate sex‐ and tactic‐specific heritabilities to determine whether and where intralocus conflict exists. Selection on length varied in intensity and form among tactics and years. Length was heritable between dams and daughters (h 2 ± 95% CI = 0.361 ± 0.252) and between fighter males and their fighter sons (0.867 ± 0.312), but no other heritabilities differed significantly from zero. The lack of intertactical heritabilities in this system, combined with similar selection on length among tactics, suggests the absence of intralocus conflict between sexes and among tactics, allowing for the evolution of sexual and tactical dimorphisms. Our results suggest that Coho salmon populations are unlikely to be constrained by intralocus conflict or artificial selection on male tactic.