Integrating Unmanned and Manned UAVs data network based on combined Bayesian Belief network and Multi-objective reinforcement learning algorithm

This paper presents and assesses the feasibility and potential of a novel concept: the operation of multiple unmanned vehicles (UAV) commanded and supported by a manned “Tender” air vehicle carrying a pilot and flight manager(s). The "Tender" is equipped to flexibly and economically monitor and manage multiple diverse UAVs over otherwise inaccessible terrain through wireless communication. Further, this paper seeks to find the optimal trajectories for UAVs to collect data from sensors in a predefined continuous space. We formulate the path-planning problem for a cooperative, and a diverse swarm of UAVs tasked with optimizing multiple objectives simultaneously with the goal of maximizing accumulated data within a given flight time within cloud data processing constraints as well as minimizing the probable imposed risk during UAVs mission. To this end, as the problem is formulated as a convex optimization model, and we propose a low complexity Multi-Objective Reinforcement Learning (MORL) algorithm with a provable performance guarantee to solve the problem efficiently. We show that the MORL architecture can be successfully trained and allows each UAV to map each observation of the network state to an action to make optimal movement decisions.