Massive Autonomous UAV Path Planning: A Neural Network Based Mean-Field Game Theoretic Approach
By Hamid Shiri, Jihong Park, Medhi Bennis. 2019
69 citations recorded by the source corpus.
This paper investigates the autonomous control of massive unmanned aerial vehicles (UAVs) for mission-critical applications (e.g., dispatching many UAVs from a source to a destination for firefighting). Achieving their fast travel and low motion energy without inter-UAV collision under wind perturbation is a daunting control task, which incurs huge communication energy for exchanging UAV states in real time. We tackle this problem by exploiting a mean-field game (MFG) theoretic control method that requires the UAV state exchanges only once at the initial source. Afterwards, each UAV can control its acceleration by locally solving two partial differential equations (PDEs), known as the Hamilton-Jacobi- Bellman (HJB) and Fokker-Planck-Kolmogorov (FPK) equations. This approach, however, brings about huge computation energy for solving the PDEs, particularly under multi-dimensional UAV states. We address this issue by utilizing a machine learning (ML) method where two separate ML models approximate the solutions of the HJB and FPK equations. These ML models are trained and exploited using an online gradient descent method with low computational complexity. Numerical evaluations validate that the proposed ML aided MFG theoretic algorithm, referred to as \emph{MFG learning control}, is effective in collision avoidance with low communication energy and acceptable computation energy.
Subfields: Aerospace Engineering
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In Aerospace Engineering
- Medhi Bennis
- Merouane Debbah
- Jihong Park
- Sumudu Samarakoon
- Hamid Shiri
- Rui Zhang
- Walid Saad
- Yong Zeng
Citations in the corpus
- Extreme URLLC: Vision, Challenges, and Key Enablers (2020, background): “Not only that, a key requirement overlooked in 5G URLLC is stability, which makes CoCoCo of utmost importance for guaranteeing physical stability [15].”
- Communication-Efficient and Distributed Learning Over Wireless Networks: Principles and Applications (2020, background): “While 2) is fixed, 1) and 3) are recursively updated until convergence, in which the optimal action 1) is finally determined [156], [157].”
- Federated Reinforcement Distillation with Proxy Experience Memory (2019, background): “Recent advances in mobile computing power have led to the emergence of intelligent autonomous systems [Park et al., 2018, Shiri et al., 2019], ranging from driverless cars and drones to self-controlled robots in smart factories.”