Learning-Based UAV Trajectory Optimization with Collision Avoidance and Connectivity Constraints

• URL: http://arxiv.org/abs/2104.06256v2
• Date: Thu, 15 Apr 2021 19:22:20 GMT
• Title: Learning-Based UAV Trajectory Optimization with Collision Avoidance and Connectivity Constraints
• Authors: Xueyuan Wang and M. Cenk Gursoy
• Abstract summary: Unmanned aerial vehicles (UAVs) are expected to be an integral part of wireless networks. In this paper, we reformulate the multi-UAV trajectory optimization problem with collision avoidance and wireless connectivity constraints. We propose a decentralized deep reinforcement learning approach to solve the problem.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Unmanned aerial vehicles (UAVs) are expected to be an integral part of wireless networks, and determining collision-free trajectories for multiple UAVs while satisfying requirements of connectivity with ground base stations (GBSs) is a challenging task. In this paper, we first reformulate the multi-UAV trajectory optimization problem with collision avoidance and wireless connectivity constraints as a sequential decision making problem in the discrete time domain. We, then, propose a decentralized deep reinforcement learning approach to solve the problem. More specifically, a value network is developed to encode the expected time to destination given the agent's joint state (including the agent's information, the nearby agents' observable information, and the locations of the nearby GBSs). A signal-to-interference-plus-noise ratio (SINR)-prediction neural network is also designed, using accumulated SINR measurements obtained when interacting with the cellular network, to map the GBSs' locations into the SINR levels in order to predict the UAV's SINR. Numerical results show that with the value network and SINR-prediction network, real-time navigation for multi-UAVs can be efficiently performed in various environments with high success rate.

Related papers

• Multi-Agent Reinforcement Learning for Offloading Cellular Communications with Cooperating UAVs [21.195346908715972]
  Unmanned aerial vehicles present an alternative means to offload data traffic from terrestrial BSs. This paper presents a novel approach to efficiently serve multiple UAVs for data offloading from terrestrial BSs.
  arXiv  Detail & Related papers  (2024-02-05T12:36:08Z)
• UAV Trajectory Planning for AoI-Minimal Data Collection in UAV-Aided IoT Networks by Transformer [8.203870302926614]
  Maintaining freshness of data collection in Internet-of-Things (IoT) networks has attracted increasing attention. We investigate the trajectory planning problem of an unmanned aerial vehicle (UAV) that is used to aid a cluster-based IoT network. An optimization problem is formulated to minimize the total AoI of the collected data by the UAV from the ground IoT network.
  arXiv  Detail & Related papers  (2023-11-08T17:13:19Z)
• Multi-Objective Optimization for UAV Swarm-Assisted IoT with Virtual Antenna Arrays [55.736718475856726]
  Unmanned aerial vehicle (UAV) network is a promising technology for assisting Internet-of-Things (IoT) Existing UAV-assisted data harvesting and dissemination schemes require UAVs to frequently fly between the IoTs and access points. We introduce collaborative beamforming into IoTs and UAVs simultaneously to achieve energy and time-efficient data harvesting and dissemination.
  arXiv  Detail & Related papers  (2023-08-03T02:49:50Z)
• Integrated Sensing, Computation, and Communication for UAV-assisted Federated Edge Learning [52.7230652428711]
  Federated edge learning (FEEL) enables privacy-preserving model training through periodic communication between edge devices and the server. Unmanned Aerial Vehicle (UAV)mounted edge devices are particularly advantageous for FEEL due to their flexibility and mobility in efficient data collection.
  arXiv  Detail & Related papers  (2023-06-05T16:01:33Z)
• Joint Optimization of Deployment and Trajectory in UAV and IRS-Assisted IoT Data Collection System [25.32139119893323]
  Unmanned aerial vehicles (UAVs) can be applied in many Internet of Things (IoT) systems. The UAV-IoT wireless channels may be occasionally blocked by trees or high-rise buildings. This article aims to minimize the energy consumption of the system by jointly optimizing the deployment and trajectory of the UAV.
  arXiv  Detail & Related papers  (2022-10-27T06:27:40Z)
• 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)
• Multi-Agent Reinforcement Learning in NOMA-aided UAV Networks for Cellular Offloading [59.32570888309133]
  A novel framework is proposed for cellular offloading with the aid of multiple unmanned aerial vehicles (UAVs) Non-orthogonal multiple access (NOMA) technique is employed at each UAV to further improve the spectrum efficiency of the wireless network. A mutual deep Q-network (MDQN) algorithm is proposed to jointly determine the optimal 3D trajectory and power allocation of UAVs.
  arXiv  Detail & Related papers  (2020-10-18T20:22:05Z)
• Risk-Averse MPC via Visual-Inertial Input and Recurrent Networks for Online Collision Avoidance [95.86944752753564]
  We propose an online path planning architecture that extends the model predictive control (MPC) formulation to consider future location uncertainties. Our algorithm combines an object detection pipeline with a recurrent neural network (RNN) which infers the covariance of state estimates. The robustness of our methods is validated on complex quadruped robot dynamics and can be generally applied to most robotic platforms.
  arXiv  Detail & Related papers  (2020-07-28T07:34:30Z)
• UAV Path Planning for Wireless Data Harvesting: A Deep Reinforcement Learning Approach [18.266087952180733]
  We propose a new end-to-end reinforcement learning approach to UAV-enabled data collection from Internet of Things (IoT) devices. An autonomous drone is tasked with gathering data from distributed sensor nodes subject to limited flying time and obstacle avoidance. We show that our proposed network architecture enables the agent to make movement decisions for a variety of scenario parameters.
  arXiv  Detail & Related papers  (2020-07-01T15:14:16Z)
• Federated Learning in the Sky: Joint Power Allocation and Scheduling with UAV Swarms [98.78553146823829]
  Unmanned aerial vehicle (UAV) swarms must exploit machine learning (ML) in order to execute various tasks. In this paper, a novel framework is proposed to implement distributed learning (FL) algorithms within a UAV swarm.
  arXiv  Detail & Related papers  (2020-02-19T14:04:01Z)

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.