Conflict Detection And Resolution Research Articles

Investigation of Aircraft Conflict Resolution Trajectories under Uncertainties.

As air traffic intensity increases and stochastic uncertainties, such as wind direction and speed, continue to impact air traffic controllers' workload significantly, airlines are increasingly pressured to reduce costs by flying via straighter/more direct trajectories. Due to these changes, it is important to search for new means/solutions for aircraft conflict resolution to ensure the required level of safety and rational flight trajectory. Such a solution could be the implementation of Dubin's method of flight trajectories. This paper aims to propose and deeply analyze a new mathematical model for two-aircraft conflict resolution where the Dubins method is applied in a dynamic conflict scenario. In this model, at a certain moment, the flight trajectory of one aircraft follows a path similar to a moving circle's tangential line. Upon that, the conflict detection and resolution (CDR) model considers wind uncertainty. The proposed CDR method could be applied when uncertainty such as wind direction and speed are inconstant (stochastic) throughout the simulation.

Integrating Unmanned Aerial Vehicles in Airspace: A Systematic Review

In this article, a comprehensive review of the integration of Unmanned Aerial Vehicles (UAVs) into shared airspace is presented. By applying a systematic review methodology, the study clarifies the main challenges, problems, and possible fixes related to safety, coordination, and regulatory frameworks. The results demonstrate the critical role that several elements play in supporting the safety of UAV integration. These elements include multi-layered airspace models, careful path planning, secure communication networks, Conflict Detection and Resolution (CDR) strategies, and strong regulations. The paper explores the potential of Human-in-the-loop Reinforcement Learning (HRL) and Reinforcement Learning (RL) algorithms to train UAVs for maneuvering through complex terrain and adapting to changing circumstances. The study's conclusions highlight the importance of ongoing research projects, stakeholder cooperation and continuous support for technology developments-all of which are necessary to ensure the safe and orderly integration of UAVs into airspace.

Real-time railway traffic management under moving-block signalling: A literature review and research agenda

Railway traffic management is responsible for the detection and resolution of conflicts in case of disturbed operations. To minimise delay propagation, rescheduling decisions are taken by human dispatchers, possibly supported by mathematical models. Existing conflict detection and resolution (CDR) models mostly refer to conventional fixed-block multi-aspect signalling systems, in which minimum train headways are determined based on a preset number of blocks considering worst-case braking distances and number of signal aspects. In moving-block signalling systems, minimum headways are based on absolute braking distances. This paper reviews literature on CDR with the aim to identify gaps and to propose next steps in the research on CDR under moving-block signalling. A research agenda presents various modelling options, for which modelling approaches are proposed based on a comparative analysis.

Conflict Free Flight Path: A Design Study for Automation

A number of future air traffic management concepts are being studied to enhance efficiency and flight safety. Conflict Detection and Resolution (CDR) is one such initiative for operation much beyond the capability of the conventional Traffic Collision Avoidance System where the resolutions are generally in terms of changing speed or heading depending upon the situation. The design studies presented in this paper investigate possibility of generating heading change commands and integrating with Autopilot for closed loop operation for conflict resolution as well as for recovering the flight path to the original flight leg. Simulation results for open loop and closed loop operation are presented to validate the approach. Topics requiring further studies are highlighted.

Multi-Agent Path Finding in Unmanned Aircraft System Traffic Management With Scheduling and Speed Variation

The development of an unmanned aircraft system (UAS) traffic management (UTM) system for the safe integration of unmanned aerial vehicles (UAVs) requires pre-flight conflict detection and resolution (CDR) methods to provide collision-free flight paths for all UAVs before takeoff. A popular solution consists in adapting multi-agent path finding (MAPF) techniques. However, standard MAPF solvers consider only a fixed takeoff time and fixed uniform speed for each UAV flight path, which can lead to inefficiencies in the resolution of instances. Therefore, in this article, we propose incorporating scheduling elements into MAPF solvers, which allows us to adjust the takeoff times and speeds of each UAV to solve conflicts. We introduce two time-related resolution techniques: 1) takeoff scheduling, whereby the start time of a UAV agent is delayed, and 2) speed adjustment, wherein the speed of a UAV agent is decreased over a segment on its flight path. Importantly, we present a distinction of conflict types, which enables us to combine replanning resolution to the aforementioned temporal resolution techniques. We evaluate our proposed approaches on a realistic, high-density UAV delivery scenario in Tokyo, Japan. We show that the combination of takeoff scheduling, replanning, and speed-adjustment resolution techniques improves the efficiency of route planning by reducing the average delay per flight path and the number of rejected flight paths.

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.

Decentralized Multi-Agent Path Finding for UAV Traffic Management

The development of a real-world Unmanned Aircraft System (UAS) Traffic Management (UTM) system to ensure the safe integration of Unmanned Aerial Vehicles (UAVs) in low altitude airspace, has recently generated novel research challenges. A key problem is the development of Pre-Flight Conflict Detection and Resolution (CDR) methods that provide collision-free flight paths to all UAVs before their takeoff. Such problem can be represented as a Multi-Agent Path Finding (MAPF) problem. Currently, most MAPF methods assume that the UTM system is a centralized entity in charge of CDR. However, recent discussions on UTM suggest that such centralized control might not be practical or desirable. Therefore, we explore Pre-Flight CDR methods where independent UAS Service Providers (UASSPs) with their own interests, communicate with each other to resolve conflicts among their UAV operations—without centralized UTM directives. We propose a novel MAPF model that supports the decentralized resolution of conflicts, whereby different ‘agents’, here UASSPs, manage their UAV operations. We present two approaches: (1) a prioritization approach and (2) a simple yet practical pairwise negotiation approach where UASSPs agents determine an agreement to solve conflicts between their UAV operations. We evaluate the performance of our proposed approaches with simulation scenarios based on a consultancy study of predicted UAV traffic for delivery services in Sendai, Japan, 2030. We demonstrate that our negotiation approach improves the “fairness” between UASSPs, i.e. the distribution of costs between UASSPs in terms of total delays and rejected operations due to replanning is more balanced when compared to the prioritization approach.

Conflict Detection and Resolution for Civil Aviation: A Literature Survey

It is evident from the study of literatures that there are a great many of approaches to the conflict detection and resolution (CDR) research for civil aviation. However, there has been little comparative discussion of them. This survey contributes to the summarization of the CDR methods, mainly consisting of the long-term CDR, the mediumterm CDR, and the short-term CDR at three different categories, classified on account of the acting period. Furthermore, several typical characterizations of the taxonomy have been utilized to articulate their basic functions: Detection, Resolution, Cooperation, Maneuvers, Domino effects, Multiple Conflicts, Uncertainties, State variables, Equipped locations and methods status. It offers an intuitive and comprehensive general CDR process, as well as summarizes the various approaches for collision risk prediction and the different strategies for conflict resolution at a strategic/tactical/operational level, thus offering a comprehensive perspective of the research progresses and directions to promote the development.

A Data-driven Framework for Long-Range Aircraft Conflict Detection and Resolution

At the present time, there is no mechanism for Air Navigation Service Providers (ANSPs) to probe new flight plans filed by the Airlines Operation Centers (AOCs) against the existing approved flight plans to see if they are likely to cause conflicts or bring sector traffic densities beyond control. In the current Air Traffic Control (ATC) operations, aircraft conflicts and sector traffic densities are resolved tactically, increasing workload and leading to potential safety risks and loss of capacity and efficiency. We propose a novel Data-driven Framework to address a long-range aircraft conflict detection and resolution (CDR) problem. Given a set of predicted trajectories, the framework declares a conflict when a protected zone of an aircraft on its trajectory is infringed upon by another aircraft. The framework resolves the conflict by prescribing an alternative solution that is optimized by perturbing at least one of the trajectories involved in the conflict. To achieve this, the framework learns from descriptive patterns of historical trajectories and pertinent weather observations and builds a Hidden Markov Model (HMM). Using a variant of the Viterbi algorithm, the framework avoids the airspace volume in which the conflict is detected and generates a new optimal trajectory that is conflict free. The key concept upon which the framework is built is the assumption that the airspace is nothing more than a horizontally and vertically concatenated set of spatio-temporal data cubes where each cube is considered as an atomic unit. We evaluate our framework using real trajectory datasets with pertinent weather observations from two continents and demonstrate its effectiveness for strategic CDR.

Multiple Model Method for Aircraft Conflict Detection and Resolution in Intent and Weather Uncertainty

This paper presents a novel multiple model (MM) method for aircraft conflict detection and resolution (CDR) in intent and weather uncertainty for the next generation air transportation system. It is based on probabilistic MM aircraft trajectory prediction. Conflict detection is performed through the use of predicted probability of conflict (PC). An improved algorithm for estimating PC is proposed and compared with a commonly used method. Conflict resolution (CR) is formulated as a stochastic model predictive control (MPC) problem subject to a constraint on the predicted PC to guarantee safety of the optimized CR trajectories. The cost function to be minimized is tailored to the CR problem and includes the cost of performing evasive maneuvers as well as the average cost incurred by extra travel distance to the destination due to the maneuvers. An efficient algorithm for finding the optimal path (maneuver sequence), with respect to the maneuver cost, is proposed and extended to an overall CDR optimization algorithm for the total cost, in order to produce optimized conflict-free CR trajectories. The proposed search algorithm is essentially a generalization of an optimal list Viterbi algorithm (for unconstrained minimization through a trellis) to the constrained case of our MPC-based CDR problem. The capability and computational efficiency of the proposed MM-CDR method (including PC prediction, optimal search, and overall CDR) are evaluated via comprehensive simulation of a large variety of unmanned aerial vehicle “sense-and-avoid” encounter scenarios, and are compared with existing methods in the literature.

Improved Conflict Detection and Resolution for Service UAVs in Shared Airspace

In future unmanned aerial vehicle (UAV)-based services, UAV fleets will be managed by several independent flight operation service providers in shared low-altitude airspace. Therefore, conflict detection and resolution (CDR) methods that can solve conflicts-possible collisions between UAVs of different service providers-are a key element of the unmanned aircraft system traffic management system. As our CDR method, we introduce an adaptation of ORCA, which is a state-of-the-art collision avoidance algorithm hitherto mainly used in a limited theoretical scope, to realistic UAV operations. Our approach, called adapted ORCA, addresses practical considerations that are inherent to the deployment of UAVs in shared airspace, such as navigation inaccuracies, communication overhead, and flight phases. We validate our approach through simulations. First, by empirically tuning the ORCA parameters look-ahead time window and deconfliction distance, we are able to minimize the ORCA generated deviations from the nominal flight path. Second, by simulating realistic UAV traffic for delivery, we can determine a value for separation distance between UAVs that uses airspace efficiently.

Pre-Flight Conflict Detection and Resolution for UAV Integration in Shared Airspace: Sendai 2030 Model Case

The increasing demand for services performed by Unmanned Aerial Vehicles (UAVs) requires the simulation of Unmanned Aircraft System Traffic Management (UTM) systems. In particular, Pre-Flight Conflict Detection and Resolution (CDR) methods need to scale to future demand levels and generate conflict-free paths for a potentially large number of UAVs before actual takeoff. However, few studies have examined realistic scenarios and the requirements for the UTM system. In this paper, we focus on the Sendai 2030 model case, a realistic projection of UAV usage for deliveries in one area in Japan. This model case considers up to 21,000 requests for Unmanned Aircraft Systems (UAS) operations over a 13 hour service time, and thus poses a challenge for the Pre-Flight CDR methods. Therefore, we propose an airspace reservation method based on 4DT (3D plus time Trajectories) and map the Pre-Flight CDR problem to a Multi-Agent Path Finding (MAPF) problem. We study first-come first-served (FCFS) and “batch” processing of UAS operation requests, and compare the throughput of those methods. We analyze the air traffic topology of deliveries by UAVs, and discuss several metrics to better understand the complexity of air traffic in the Sendai model case.

Cooperative Deconflicting Heading Maneuvers Applied to Unmanned Aerial Vehicles in Non-Segregated Airspace

This paper focuses on the conflict detection and resolution (CDR) of unmanned aerial vehicles (UAVs). Firstly, the airspace conflict problem of UAVs is studied and a taxonomy of conflict situation is presented. The multi-UAV conflict is studied in virtue of the graph theory. The CDR problem is casted to a nonlinear optimization problem. Secondly, a two layered optimization algorithm, which combines stochastic parallel gradient descent (SPGD) method and Sequential quadratic programming (SQP) algorithm, is presented to solve the nonlinear optimization problem. Numerical simulations are performed to demonstrate the computational efficiency of this solver. Thirdly, the proposed algorithm is extended to 3-D space. Finally, the proposed algorithm is demonstrated on several scenarios. The results demonstrate that the proposed method outperform the existing algorithms. It can obtain conflict free solutions that would not lead to unnecessary detors.

A VNS metaheuristic for solving the aircraft conflict detection and resolution problem by performing turn changes

The aircraft Conflict Detection and Resolution (CDR) problem in air traffic management consists of finding a new configuration for a set of aircraft such that conflict situations between them are avoided. A conflict situation arises if two or more aircraft violate the safety distances that they must maintain in flight. In this paper we propose a Variable Neighborhood Search approach for solving the CDR by turn changes. This metaheuristic compares favorably with previous best known methods for solving the Mixed Integer Nonlinear Programming (MINLP) model proposed elsewhere. It is worth pointing out the astonishingly short time in which the first feasible solution is obtained. This is crucial for this specific problem, where a response must be provided almost in real time if it is to be useful in a real-life problem. A comparative study between the performance of the new approach, a state-of-the-art MINLP solver and our Sequential Integer Linear Optimization approach proposed elsewhere is reported, using a testbed of instances with up to 25 aircraft.

Performance requirements of future Trajectory Prediction and Conflict Detection and Resolution tools within SESAR and NextGen: Framework for the derivation and discussion

Future Air Traffic Management will increasingly be based on a strategic, collaborative and automated concept of operations. A key prerequisite is the capability to guarantee common situational awareness amongst relevant stakeholders as a function of time, extrapolated into the future in order to strategically optimise safe air traffic flow. This is achieved with Decision Support Tools (DSTs), including Trajectory Prediction (TP) and Conflict Detection and Resolution (CDR) tools. The functions and requirements which these tools must fulfil are dependent upon the application within the concept of operations. In order to optimise the development of the DSTs, it is important to understand the requirements for each of the applications. This paper reviews the key functions of the TP and CDR elements of DSTs in relation to these applications. It discusses the key performance drivers, derives performance metrics and develops a framework for the derivation of TP and CDR performance requirements, to support industry and standardisation bodies in the harmonisation process. A mapping exercise is undertaken to identify which of the functionalities are supported by state-of-the-art TP and CDR tools (in the public domain) and establishes those that require further research and development, highlighting some of the key challenges.

Conflict Detection and Resolution Method for Cooperating Unmanned Aerial Vehicles

This paper presents a Conflict Detection and Resolution (CDR) method for cooperating Unmanned Aerial Vehicles (UAVs) sharing airspace. The proposed method detects conflicts using an algorithm based on axis-aligned minimum bounding box and solves the detected conflicts cooperatively using a genetic algorithm that modifies the trajectories of the UAVs with an overall minimum cost. The method changes the initial flight plan of each UAV by adding intermediate waypoints that define the solution flight plan while maintaining their velocities. The method has been validated with many simulations and experimental results with multiple aerial vehicles platforms based on quadrotors in a common airspace. The experiments have been carried out in the multi-UAV aerial testbed of the Center for Advanced Aerospace Technologies (CATEC).

Strategic Conflict Detection and Resolution Using Aircraft Intent Information

A number of automated decision support tools will be required in the future air traffic management system to enable continued provision of safe and efficient services in increasingly congested skies. In particular, Conflict Detection and Resolution (CDR) tools should allow for early detection of possible conflicts and propose safe and efficient resolution manoeuvres to avoid loss of separation. However, current approaches in the open literature not only use different levels of aircraft intent information but also make a number of assumptions on models of aircraft motion. Furthermore, information relevant to aircraft performance is often not considered with the consequence of the resulting resolution strategies being potentially unreliable. This paper presents an enhanced, strategic, pairwise, performance-based and distributed CDR algorithm. It accounts for the weaknesses of current approaches by using the maximum level of aircraft intent information together with a novel trajectory prediction model. Numerical results for representative conflict scenarios show that the proposed CDR method is able to generate conflict-free trajectories for participating aircraft while taking into account the actual aircraft capabilities to perform the recommended resolution manoeuvres.

Traffic Complexity Based Conflict Resolution

This paper presents a new approach to the development of Conflict Detection and Resolution (CDR) algorithms for a Free Flight environment. Most of the recently proposed CDR algorithms typically attempt to resolve impending conflicts while minimizing trajectory deviations, fuel usage, flight time, or some other “individually-oriented” criterion. However, in this research a newly developed CDR algorithm, the “Minimised number of Aircraft Solve Conflicts” (MASC) algorithm, is described that aims to resolve conflicts from a “collective” optimisation perspective. More specifically, MASC aims at reducing the local traffic complexity in conflict situations in a Free Flight sector. In this study, the results of simulations of several conflict scenarios are discussed and on the basis of so-called Dynamic Density computations, the MASC algorithm is compared with three currently existing CDR algorithms. The computed Dynamic Density time-histories are used as an indication of the local traffic complexity. The developed MASC algorithm shows considerably lower Dynamic Density values than the other three CDR algorithms.

A review of conflict detection and resolution modeling methods

A number of methods have been proposed to automate air traffic conflict detection and resolution (CDR), but there has been little cohesive discussion or comparative evaluation of approaches. The paper presents a survey of 68 CDR modeling methods, several of which are currently in use or under operational evaluation. A framework that articulates the basic functions of CDR is used to categorize the models. The taxonomy includes: dimensions of state information (vertical, horizontal, or three-dimensional, 3-D); method of dynamic state propagation (nominal, worst case, or probabilistic); conflict detection threshold; conflict resolution method (prescribed, optimized, force field, or manual); maneuvering dimensions (speed change, lateral, vertical, or combined manoeuvres); and management of multiple aircraft conflicts (pairwise or global). An overview of important considerations for these and other CDR functions is provided, and the current system design process is critiqued.