Optimal Search For Moving Targets
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Optimal Search for Moving Targets
This book begins with a review of basic results in optimal search for a stationary target. It then develops the theory of optimal search for a moving target, providing algorithms for computing optimal plans and examples of their use. Next it develops methods for computing optimal search plans involving multiple targets and multiple searchers with realistic operational constraints on search movement. These results assume that the target does not react to the search. In the final chapter there is a brief overview of mostly military problems where the target tries to avoid being found as well as rescue or rendezvous problems where the target and the searcher cooperate. Larry Stone wrote his definitive book Theory of Optimal Search in 1975, dealing almost exclusively with the stationary target search problem. Since then the theory has advanced to encompass search for targets that move even as the search proceeds, and computers have developed sufficient capability to employ the improved theory. In this book, Stone joins Royset and Washburn to document and explain this expanded theory of search. The problem of how to search for moving targets arises every day in military, rescue, law enforcement, and border patrol operations.
Cooperative search for moving targets with the ability to perceive and evade using multiple UAVs
This paper focuses on the problem of regional cooperative search using multiple unmanned aerial vehicles (UAVs) for targets that have the ability to perceive and evade. When UAVs search for moving targets in a mission area, the targets can perceive the positions and flight direction of UAVs within certain limits and take corresponding evasive actions, which makes the search more challenging than traditional search problems. To address this problem, we first define a detailed motion model for such targets and design various search information maps and their update methods to describe the environmental information based on the prediction of moving targets and the search results of UAVs. We then establish a multi-UAV search path planning optimization model based on the model predictive control, which includes various newly designed objective functions of search benefits and costs. We propose a priority-encoded improved genetic algorithm with a fine-adjustment mechanism to solve this model. The simulation results show that the proposed method can effectively improve the cooperative search efficiency, and more targets can be found at a much faster rate compared to traditional search methods.
Optimal Search for Moving Targets in Continuous Time and Space Using Consistent Approximations
We show how to formulate many continuous time-and-space search problems as generalized optimal control problems, where multiple searchers look for multiple targets. Speci cally, we formulate problems in which we minimize the probability that all of the searchers fail to detect any of the targets during the planning horizon, and problems in which we maximize the expected number of targets detected. We construct discretization schemes to solve these continuous time-and-space problems, and prove that they are consistent approximations. Consistency ensures that global minimizers, local minimizers, and stationary points of the discretized problems converge to global minimizers, local minimizers, and stationary points, respectively, of the original problems. We also investigate the rate of convergence of algorithms based on discretization schemes as a computing budget tends to in nity. We provide numerical results to show that our discretization schemes are computationally tractable, including examples with three searchers and ten targets. We develop three heuristics for real-time search planning, one based on our discretization schemes, and two based on polynomial tting methods, and compare the three methods to determine which solution technique would be best suited for use onboard unmanned platforms for automatic route generation for search missions.