Deep Reinforcement Learning for Trajectory Path Planning and Distributed Inference in Resource-Constrained UAV Swarms

• Published in: arXiv.org 2022-12
• Main Authors: Dhuheir, Marwan, Baccour, Emna, Erbad, Aiman, Sinan Sabeeh Al-Obaidi, Hamdi, Mounir
• Format: Article
• Language: English
• Subjects: Collaboration; Communication; Constraints; Data processing; Data transmission; Deep learning; Inference; Machine learning; Maneuverability; Optimization; Real time; State-of-the-art reviews; Trajectory planning; Unmanned aerial vehicles; Wildfires
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.2212.11201

The deployment flexibility and maneuverability of Unmanned Aerial Vehicles (UAVs) increased their adoption in various applications, such as wildfire tracking, border monitoring, etc. In many critical applications, UAVs capture images and other sensory data and then send the captured data to remote servers for inference and data processing tasks. However, this approach is not always practical in real-time applications due to the connection instability, limited bandwidth, and end-to-end latency. One promising solution is to divide the inference requests into multiple parts (layers or segments), with each part being executed in a different UAV based on the available resources. Furthermore, some applications require the UAVs to traverse certain areas and capture incidents; thus, planning their paths becomes critical particularly, to reduce the latency of making the collaborative inference process. Specifically, planning the UAVs trajectory can reduce the data transmission latency by communicating with devices in the same proximity while mitigating the transmission interference. This work aims to design a model for distributed collaborative inference requests and path planning in a UAV swarm while respecting the resource constraints due to the computational load and memory usage of the inference requests. The model is formulated as an optimization problem and aims to minimize latency. The formulated problem is NP-hard so finding the optimal solution is quite complex; thus, this paper introduces a real-time and dynamic solution for online applications using deep reinforcement learning. We conduct extensive simulations and compare our results to the-state-of-the-art studies demonstrating that our model outperforms the competing models.