This paper discusses the application of 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 onboard sensors to improve tracking of multiple ground targets. While POMDP problems are intractable to solve exactly, principled approximation methods can be devised based on the theory that characterizes optimal solutions. A new approximation method called nominal belief-state optimization (NBO), combined with other application-specific approximations and techniques within the POMDP framework, produces a practical design that coordinates the UAVs to achieve good long-term mean-squared-error tracking performance in the presence of occlusions and dynamic constraints. The flexibility of the design is demonstrated by extending the objective to reduce the probability of a track swap in ambiguous situations.

No abstract

The public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing aaia SOUIUHS. y^m^niy a,,u maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection ot information, including suggestions for reducing the burden, to the Department of Defense, Executive Service Directorate (0704-0188) Respondents should be aware that notwithstanding any other provision of law. no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number. PLEASE DO NOT RETURN YOUR SPONSOR/MONITOR'S ACRONYM(S) AFOSR SPONSOR/MONITOR'S REPORT NUMBER(S) DISTRIBUTION/AVAILABILITY STATEMENTApproved for public release: distribution is unlimited. SUPPLEMENTARY NOTES ABSTRACTThe research conducted under this grant concerned the application of 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 to improve tracking of multiple ground targets. While POMDP problems are intractable to solve exactly, principled approximation methods can be devised based on the theory that characterizes optimal solutions. A new approximation method called nominal belief-state optimization (NBO) was proposed. When combined with other application-specific approximations and techniques within the POMDP framework, NBO produced a practical design that coordinated the UAVs to achieve good long-term mean-squared-error tracking performance in the presence of occlusions and dynamic constraints The flexibility of the design was demonstrated by extending the objective to reduce the probability of a track swap in ambiguous situations, with the positive side-effect of improving the mean-squared-error tracking performance as well.