Multi-aircraft Conflict Resolution Research Articles

Priority-based intelligent resolution method of multi-aircraft flight conflicts

Abstract The rising demand for air traffic will inevitably result in a surge in both the number and complexity of flight conflicts, necessitating intelligent strategies for conflict resolution. This study addresses the critical challenges of scalability and real-time performance in multi-aircraft flight conflict resolution by proposing a comprehensive method that integrates a priority ranking mechanism with a conflict resolution model based on the Markov decision process (MDP). Within this framework, the proximity between aircraft in a multi-aircraft conflict set is dynamically assessed to establish a conflict resolution ranking mechanism. The problem of multi-aircraft conflict resolution is formalised through the MDP, encompassing the design of state space, discrete action space and reward function, with the transition function implemented via simulation prediction using model-free methods. To address the positional uncertainty of aircraft in real-time scenarios, the conflict detection mechanism introduces the aircraft’s positional error. A deep reinforcement learning (DRL) environment is constructed incorporating actual airspace structures and traffic densities, leveraging the Actor Critic using Kronecker-factored Trust Region (ACKTR) algorithm to determine resolution actions. The experimental results indicate that with 20–30 aircraft in the airspace, the success rate can reach 94% for the training set and 85% for the test set. Furthermore, this study analyses the impact of varying aircraft numbers on the success rate within a specific airspace scenario. The outcomes of this research provide valuable insights for the automation of flight conflict resolution.

Graph Reinforcement Learning for Multi-Aircraft Conflict Resolution

Graph Reinforcement Learning for Multi-Aircraft Conflict Resolution

General real-time three-dimensional multi-aircraft conflict resolution method using multi-agent reinforcement learning

Reinforcement learning (RL) techniques have been studied for solving the conflict resolution (CR) problem in air traffic management, leveraging their potential for computation and ability to handle uncertainty. However, challenges remain that impede the application of RL methods to CR in practice, including three-dimensional manoeuvres, generalisation, trajectory recovery, and success rate. This paper proposes a general multi-agent reinforcement learning approach for real-time three-dimensional multi-aircraft conflict resolution, in which agents share a neural network and are deployed on each aircraft to form a distributed decision-making system. To address the challenges, several technologies are introduced, including a partial observation model based on imminent threats for generalisation, a safety separation relaxation model for multiple flight levels for three-dimensional manoeuvres, an adaptive manoeuvre strategy for trajectory recovery, and a conflict buffer model for success rate. The Rainbow Deep Q-learning Network (DQN) is used to enhance the efficiency of the RL process. A simulation environment that considers flight uncertainty (resulting from mechanical and navigation errors and wind) is constructed to train and evaluate the proposed approach. The experimental results demonstrate that the proposed method can resolve conflicts in scenarios with much higher traffic density than in today’s real-world situations.

General multi-agent reinforcement learning integrating adaptive manoeuvre strategy for real-time multi-aircraft conflict resolution

Reinforcement learning (RL) techniques are under investigation for resolving conflict in air traffic management (ATM), exploiting their computational capabilities and ability to cope with flight uncertainty. However, the limitations of generalisation make it difficult for existing RL-based conflict resolution (CR) methods to be effective in practice. This paper proposes a general multi-agent reinforcement learning (MARL) method that integrates an adaptive manoeuvre strategy to enhance both the solution’s efficiency and the model’s generalisation in multi-aircraft conflict resolution (MACR). A partial observation approach based on the imminent threat detection sectors is used to gather critical environmental information, enabling the model to be applied in arbitrary scenarios. Agents are trained to provide the correct flight intention (such as increasing speed and yawing to the left), while an adaptive manoeuvre strategy generates the specific manoeuvre (speed and heading parameters) based on the flight intention. To address flight uncertainty and performance challenges caused by the intrinsic non-stationarity in MARL, a warning area for each aircraft is introduced. We employ a state-of-the-art Deep Q-learning Network (DQN) method, Rainbow DQN, to improve the efficiency of the RL algorithm. The multi-agent system is trained and deployed in a distributed manner to adapt to real-world scenarios. A sensitivity analysis of uncertainty levels and warning area sizes is conducted to explore their impact on the proposed method. Simulation experiments confirm the effectiveness of the training and generalisation of the proposed method.

Three-Dimensional Conflict Resolution Strategy Based on Network Cooperative Game

Aiming at the problems of difficult handling of three-dimensional flight conflicts and unfair distribution of resolution costs, we propose a multi-aircraft conflict resolution method based on the network cooperative game. Firstly, we establish a flight conflict network model with aircraft as nodes and the conflict relationship between node pairs as edges. After that, we propose a comprehensive network index that can evaluate the effect of resolution strategy. Based on the concept of “nucleolus solution”, we establish a conflict network alliance with all nodes as participants, and balance the interests of all participants through the resolution cost function. In order to improve the timeliness of the method, we propose two optimization methods: adjusting high-priority nodes and customizing the initial resolution scheme. Finally, we combine the NSGA-II algorithm to solve the optimal conflict resolution scheme. The simulation results show that our method can adjust 10 aircraft in 15.17 s and resolve 12 flight conflicts in a complex conflict scenario containing 40 aircraft; our method reduces the resolution cost by more than 22.1% on average compared with the method without considering the resolution cost. The method ensures both the conflict resolution capability and the reduction in resolution cost.

General Multi-Agent Reinforcement Learning Integrating Adaptive Manoeuvre Strategy for Real-Time Multi-Aircraft Conflict Resolution

General Multi-Agent Reinforcement Learning Integrating Adaptive Manoeuvre Strategy for Real-Time Multi-Aircraft Conflict Resolution

Velocity obstacle–based conflict resolution and recovery method

ABSTRACTConsidering the shortcomings of current methods for real-time resolution of two-aircraft flight conflicts, a geometric optimal conflict resolution and recovery method based on the velocity obstacle method for two aircraft and a cooperative conflict resolution method for multiple aircraft are proposed. The conflict type was determined according to the relative position and velocity of the aircraft, and a corresponding conflict mitigation strategy was selected. A resolution manoeuvre and a recovery manoeuvre were performed. On the basis of a two-aircraft conflict resolution model, a multi-aircraft cooperative conflict resolution game was constructed to identify an optimal solution for maximising group welfare. The solution and recovery method is simple and effective, and no new flight conflicts are introduced during track recovery. For multi-aircraft conflict resolution, an equilibrium point that maximises the welfare function of the group was identified, and thus, an optimal strategy for multi-aircraft conflict resolution was obtained.

Method for optimal conflict-free aircraft trajectory generation

Purpose A number of automated tools will be required for the purpose of enabling efficient services in air traffic control. The purpose of this paper is to devise an optimal flight trajectory search method that optimizes airspace system efficiency for 3D space in the presence of uncertainties. Design/methodology/approach This paper put forward an optimization model for generating applicable solutions of multi-aircraft conflict resolution problem, and the solution is based on the principle of optimality. Findings The conflict resolution problem between multiple aircraft can be described by spatial discretization, and the approach taken digitizes the 3D space into a grid of nodes. Practical implications The simulation examples are given to illustrate the validity of trajectory search model and stress on the impact of different system parameters. Originality/value Realistic constraints that are convenient to operate are incorporated in the system model, and the results show that it can provide reliable decision-making for conflict avoidance.

A Multi-Criteria Decision-Making Scheme for Multi-Aircraft Conflict Resolution

Multi-Aircraft Conflict Resolution (MACR) is a Multi-Criteria Decision-Making (MCDM) problem, which involves multiple stakeholders (airline, air traffic controller, and aircraft) with competing and incommensurable objectives. This paper proposes a two-step MCDM scheme to the solution of MACR. In the first step, a second order cone program is adopted to generate a set of candidate resolution strategies with different minimum separations between trajectories. Each candidate strategy is then evaluated via three criteria modeling the interests of the stakeholders. In the second step, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) approach is used to determine the best strategy that realizes an adequate tradeoff among the competing interests while coping with their incommensurability. Some numerical results are presented to show the efficacy of the proposed scheme. Interestingly, the minimum separations associated with the best resolution strategies according to either the interest of the airline or that of the aircraft both differ from the one adopted in the current air traffic control operation.

The uncertainty estimated of aircraft with the consistent resolution of conflicts

The peculiarities of sequential multi-aircraft conflict resolution using stochastic position prediction model are considered. The horizontal turn maneuver is employed as a basic resolution strategy for each aircraft. Corresponding surfaces of permissible trajectories have been calculated and analyzed.