UAV Path Planning in a Dynamic Environment via Partially Observable Markov Decision Process

A path-planning algorithm to guide unmanned aerial vehicles (UAVs) for tracking multiple ground targets based on the theory of partially observable Markov decision processes (POMDPs) is presented. A variety of features of interest are shown to be easy to incorporate into the framework by plugging in the appropriate models, which demonstrates the power and flexibility of the POMDP framework. Specifically, it is shown how to incorporate the following features by appropriately formulating the POMDP action space, transition law, and objective function: 1) control UAVs with both forward acceleration and bank angle subject to constraints; 2) account for the effect of wind disturbance on UAVs; 3) avoid collisions between UAVs and obstacles and among UAVs; 4) track targets while evading threats; 5) track evasive targets; and 6) mitigate track swaps.