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

2021/04/03 by Xueyuan Wang, Wang, Xueyuan, M. Cenk Gursoy +1 · 4 citations
Computer Science · Engineering · #Distributed Control Multi-Agent Systems #FOS: Computer and information sciences #Information Theory (cs.IT) #Machine Learning (cs.LG) #Robotic Path Planning Algorithms #Robotics (cs.RO) #UAV Applications and Optimization

paper · pdf · doi:10.48550/arxiv.2104.06256

openalex publication_date 2021/04/03 · openalex created_date 2021/04/26 · openalex updated_date 2026/07/28

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.

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• Co-planning of Flight Corridors and Communication Infrastructure for Urban Drone Logistics Networks
• Trajectory Design for UAV-Assisted Logistics Collection in Low-Altitude Economy
• Navigation in a Three-Dimensional Urban Flow using Deep Reinforcement Learning
• Trajectory-Aware Air-to-Ground Channel Characterization for Low-Altitude UAVs Using MaMIMO Measurements

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