Multi-UAV Conflict Resolution with Graph Convolutional Reinforcement Learning

• URL: http://arxiv.org/abs/2111.14598v1
• Date: Mon, 29 Nov 2021 15:29:32 GMT
• Title: Multi-UAV Conflict Resolution with Graph Convolutional Reinforcement Learning
• Authors: Ralvi Isufaj, Marsel Omeri, Miquel Angel Piera
• Abstract summary: In-flight safety between Unmanned Aircraft Vehicles (UAVs) is ensured through pairwise separation minima. Due to an expected increase in traffic density, encounters with more than two UAVs are likely to happen. We model multi-UAV conflict resolution as a multi-agent reinforcement learning problem.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Safety is the primary concern when it comes to air traffic. In-flight safety between Unmanned Aircraft Vehicles (UAVs) is ensured through pairwise separation minima, utilizing conflict detection and resolution methods. Existing methods mainly deal with pairwise conflicts, however due to an expected increase in traffic density, encounters with more than two UAVs are likely to happen. In this paper, we model multi-UAV conflict resolution as a multi-agent reinforcement learning problem. We implement an algorithm based on graph neural networks where cooperative agents can communicate to jointly generate resolution maneuvers. The model is evaluated in scenarios with 3 and 4 present agents. Results show that agents are able to successfully solve the multi-UAV conflicts through a cooperative strategy.

Related papers

• Autonomous Decision Making for UAV Cooperative Pursuit-Evasion Game with Reinforcement Learning [50.33447711072726]
  This paper proposes a deep reinforcement learning-based model for decision-making in multi-role UAV cooperative pursuit-evasion game. The proposed method enables autonomous decision-making of the UAVs in pursuit-evasion game scenarios.
  arXiv  Detail & Related papers  (2024-11-05T10:45:30Z)
• UAV-enabled Collaborative Beamforming via Multi-Agent Deep Reinforcement Learning [79.16150966434299]
  We formulate a UAV-enabled collaborative beamforming multi-objective optimization problem (UCBMOP) to maximize the transmission rate of the UVAA and minimize the energy consumption of all UAVs. We use the heterogeneous-agent trust region policy optimization (HATRPO) as the basic framework, and then propose an improved HATRPO algorithm, namely HATRPO-UCB.
  arXiv  Detail & Related papers  (2024-04-11T03:19:22Z)
• Graph Attention-based Reinforcement Learning for Trajectory Design and Resource Assignment in Multi-UAV Assisted Communication [20.79743323142469]
  It is challenging for UAV base stations (UAV BSs) to realize trajectory design and resource assignment in unknown environments. The cooperation and competition between UAV BSs in the communication network leads to a Markov game problem. In this paper, a novel graph-attention multi-agent trust region (GA-MATR) reinforcement learning framework is proposed to solve the multi-UAV assisted communication problem.
  arXiv  Detail & Related papers  (2024-01-31T14:37:06Z)
• MADRL-based UAVs Trajectory Design with Anti-Collision Mechanism in Vehicular Networks [1.9662978733004604]
  In upcoming 6G networks, unmanned aerial vehicles (UAVs) are expected to play a fundamental role by acting as mobile base stations. One of the most challenging problems is the design of trajectories for multiple UAVs, cooperatively serving the same area. We propose a rank-based binary masking approach to address these issues.
  arXiv  Detail & Related papers  (2024-01-21T20:08:32Z)
• Toward collision-free trajectory for autonomous and pilot-controlled unmanned aerial vehicles [1.018017727755629]
  This study makes greater use of electronic conspicuity (EC) information made available by PilotAware Ltd in developing an advanced collision management methodology. The merits of the DACM methodology have been demonstrated through extensive simulations and real-world field tests in avoiding mid-air collisions.
  arXiv  Detail & Related papers  (2023-09-18T18:24:31Z)
• Optimization for Master-UAV-powered Auxiliary-Aerial-IRS-assisted IoT Networks: An Option-based Multi-agent Hierarchical Deep Reinforcement Learning Approach [56.84948632954274]
  This paper investigates a master unmanned aerial vehicle (MUAV)-powered Internet of Things (IoT) network. We propose using a rechargeable auxiliary UAV (AUAV) equipped with an intelligent reflecting surface (IRS) to enhance the communication signals from the MUAV. Under the proposed model, we investigate the optimal collaboration strategy of these energy-limited UAVs to maximize the accumulated throughput of the IoT network.
  arXiv  Detail & Related papers  (2021-12-20T15:45:28Z)
• A Multi-UAV System for Exploration and Target Finding in Cluttered and GPS-Denied Environments [68.31522961125589]
  We propose a framework for a team of UAVs to cooperatively explore and find a target in complex GPS-denied environments with obstacles. The team of UAVs autonomously navigates, explores, detects, and finds the target in a cluttered environment with a known map. Results indicate that the proposed multi-UAV system has improvements in terms of time-cost, the proportion of search area surveyed, as well as successful rates for search and rescue missions.
  arXiv  Detail & Related papers  (2021-07-19T12:54:04Z)
• Attention-based Reinforcement Learning for Real-Time UAV Semantic Communication [53.46235596543596]
  We study the problem of air-to-ground ultra-reliable and low-latency communication (URLLC) for a moving ground user. We propose a novel multi-agent deep reinforcement learning framework, coined a graph attention exchange network (GAXNet) GAXNet achieves 6.5x lower latency with the target 0.0000001 error rate, compared to a state-of-the-art baseline framework.
  arXiv  Detail & Related papers  (2021-05-22T12:43:25Z)
• Jamming-Resilient Path Planning for Multiple UAVs via Deep Reinforcement Learning [1.2330326247154968]
  Unmanned aerial vehicles (UAVs) are expected to be an integral part of wireless networks. In this paper, we aim to find collision-free paths for multiple cellular-connected UAVs. We propose an offline temporal difference (TD) learning algorithm with online signal-to-interference-plus-noise ratio mapping to solve the problem.
  arXiv  Detail & Related papers  (2021-04-09T16:52:33Z)
• Distributed Reinforcement Learning for Flexible and Efficient UAV Swarm Control [28.463670610865837]
  We propose a distributed Reinforcement Learning (RL) approach that scales to larger swarms without modifications. Our experiments show that the proposed method can yield effective strategies, which are robust to communication channel impairments. We also show that our approach achieves better performance compared to a computationally intensive look-ahead.
  arXiv  Detail & Related papers  (2021-03-08T11:06:28Z)
• Efficient UAV Trajectory-Planning using Economic Reinforcement Learning [65.91405908268662]
  We introduce REPlanner, a novel reinforcement learning algorithm inspired by economic transactions to distribute tasks between UAVs. We formulate the path planning problem as a multi-agent economic game, where agents can cooperate and compete for resources. As the system computes task distributions via UAV cooperation, it is highly resilient to any change in the swarm size.
  arXiv  Detail & Related papers  (2021-03-03T20:54:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.