Tracking and data association
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Stochastic dynamic programming with factored representations
Artificial Intelligence
Reinforcement Learning
Bayesian Multiple Target Tracking
Bayesian Multiple Target Tracking
Neuro-Dynamic Programming
Rollout Algorithms for Stochastic Scheduling Problems
Journal of Heuristics
Convex Optimization
Point-Based Value Iteration for Continuous POMDPs
The Journal of Machine Learning Research
Dynamic Programming and Optimal Control, Vol. II
Dynamic Programming and Optimal Control, Vol. II
Approximate stochastic dynamic programming for sensor scheduling to track multiple targets
Digital Signal Processing
Online planning algorithms for POMDPs
Journal of Artificial Intelligence Research
Partially Observable Markov Decision Process Approximations for Adaptive Sensing
Discrete Event Dynamic Systems
Coordinated guidance of autonomous UAVs via nominal belief-state optimization
ACC'09 Proceedings of the 2009 conference on American Control Conference
Efficient planning under uncertainty with macro-actions
Journal of Artificial Intelligence Research
A Bayesian nonparametric approach to modeling motion patterns
Autonomous Robots
On the upper bound of the number of modes of a multivariate normal mixture
Journal of Multivariate Analysis
Decentralized Guidance Control of UAVs with Explicit Optimization of Communication
Journal of Intelligent and Robotic Systems
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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.