MADRL-based UAVs Trajectory Design with Anti-Collision Mechanism in Vehicular Networks

• URL: http://arxiv.org/abs/2402.03342v1
• Date: Sun, 21 Jan 2024 20:08:32 GMT
• Title: MADRL-based UAVs Trajectory Design with Anti-Collision Mechanism in Vehicular Networks
• Authors: Leonardo Spampinato, Enrico Testi, Chiara Buratti, Riccardo Marini
• Abstract summary: 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.
• Score: 1.9662978733004604
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In upcoming 6G networks, unmanned aerial vehicles (UAVs) are expected to play a fundamental role by acting as mobile base stations, particularly for demanding vehicle-to-everything (V2X) applications. In this scenario, one of the most challenging problems is the design of trajectories for multiple UAVs, cooperatively serving the same area. Such joint trajectory design can be performed using multi-agent deep reinforcement learning (MADRL) algorithms, but ensuring collision-free paths among UAVs becomes a critical challenge. Traditional methods involve imposing high penalties during training to discourage unsafe conditions, but these can be proven to be ineffective, whereas binary masks can be used to restrict unsafe actions, but naively applying them to all agents can lead to suboptimal solutions and inefficiencies. To address these issues, we propose a rank-based binary masking approach. Higher-ranked UAVs move optimally, while lower-ranked UAVs use this information to define improved binary masks, reducing the number of unsafe actions. This approach allows to obtain a good trade-off between exploration and exploitation, resulting in enhanced training performance, while maintaining safety constraints.

Related papers

• Multi-UAV Multi-RIS QoS-Aware Aerial Communication Systems using DRL and PSO [34.951735976771765]
  Unmanned Aerial Vehicles (UAVs) have attracted the attention of researchers in academia and industry for providing wireless services to ground users. limited resources of UAVs can pose challenges for adopting UAVs for such applications. Our system model considers a UAV swarm that navigates an area, providing wireless communication to ground users with RIS support to improve the coverage of the UAVs.
  arXiv  Detail & Related papers  (2024-06-16T17:53:56Z)
• 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)
• Anti-Jamming Path Planning Using GCN for Multi-UAV [0.0]
  The effectiveness of UAV swarms can be severely compromised by jamming technology. A novel approach, where UAV swarms leverage collective intelligence to predict jamming areas, is proposed. A multi-agent control algorithm is then employed to disperse the UAV swarm, avoid jamming, and regroup upon reaching the target.
  arXiv  Detail & Related papers  (2024-03-13T07:28:05Z)
• Tiny Multi-Agent DRL for Twins Migration in UAV Metaverses: A Multi-Leader Multi-Follower Stackelberg Game Approach [57.15309977293297]
  The synergy between Unmanned Aerial Vehicles (UAVs) and metaverses is giving rise to an emerging paradigm named UAV metaverses. We propose a tiny machine learning-based Stackelberg game framework based on pruning techniques for efficient UT migration in UAV metaverses.
  arXiv  Detail & Related papers  (2024-01-18T02:14:13Z)
• UAV Swarm-enabled Collaborative Secure Relay Communications with Time-domain Colluding Eavesdropper [115.56455278813756]
  Unmanned aerial vehicles (UAV) as aerial relays are practically appealing for assisting Internet Things (IoT) network. In this work, we aim to utilize the UAV to assist secure communication between the UAV base station and terminal terminal devices.
  arXiv  Detail & Related papers  (2023-10-03T11:47:01Z)
• 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)
• DL-DRL: A double-level deep reinforcement learning approach for large-scale task scheduling of multi-UAV [65.07776277630228]
  We propose a double-level deep reinforcement learning (DL-DRL) approach based on a divide and conquer framework (DCF) Particularly, we design an encoder-decoder structured policy network in our upper-level DRL model to allocate the tasks to different UAVs. We also exploit another attention based policy network in our lower-level DRL model to construct the route for each UAV, with the objective to maximize the number of executed tasks.
  arXiv  Detail & Related papers  (2022-08-04T04:35:53Z)
• Robust Adversarial Attacks Detection based on Explainable Deep Reinforcement Learning For UAV Guidance and Planning [4.640835690336653]
  Adversarial attacks on Uncrewed Aerial Vehicle (UAV) agents operating in public are increasing. Deep Learning (DL) approaches to control and guide these UAVs can be beneficial in terms of performance but can add concerns regarding the safety of those techniques and their vulnerability against adversarial attacks. This paper proposes an innovative approach based on the explainability of DL methods to build an efficient detector that will protect these DL schemes and the UAVs adopting them from attacks.
  arXiv  Detail & Related papers  (2022-06-06T15:16:10Z)
• 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)
• 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)
• Secure communication between UAVs using a method based on smart agents in unmanned aerial vehicles [1.2691047660244335]
  Unmanned aerial vehicles (UAVs) can be deployed to monitor very large areas without the need for network infrastructure. Such communication poses security challenges due to its dynamic topology. The proposed method uses two phases to counter malicious UAV attacks.
  arXiv  Detail & Related papers  (2020-11-03T10:33:39Z)

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.