Coordinated guidance of autonomous uavs via nominal belief-state optimization

Scott A. Miller, Zachary A. Harris, Edwin K. P. Chong
2009 2009 American Control Conference  
We apply the theory of partially observable Markov decision processes (POMDPs) to the design of guidance algorithms for controlling the motion of unmanned aerial vehicles (UAVs) with on-board sensors for tracking multiple ground targets. While POMDPs are intractable to optimize exactly, principled approximation methods can be devised based on Bellman's principle. We introduce a new approximation method called nominal belief-state optimization (NBO). We show that NBO, combined with other
more » ... with other application-specific approximations and techniques within the POMDP framework, produces a practical design that coordinates the UAVs to achieve good longterm mean-squared-error tracking performance in the presence of occlusions and dynamic constraints.
doi:10.1109/acc.2009.5159963 dblp:conf/amcc/MillerHC09 fatcat:s4iccy2yg5bt7kcqbq2agikotq