Planning an effective search for locating mobile-targets is essential in a variety of scenarios (e.g., search and rescue, environmental monitoring, law enforcement, etc.). Effective planning is especially important when resources are sparse (i.e., they are insufficient to search a significant fraction of the area of interest). For example, in wilderness search and rescue, where the search area is typically much larger than the search team can cover, effective planning is necessary to locate a lost person as soon as possible and maximize their probability of survival. Most research to-date has investigated the topic of effective search planning that makes use of homogeneous resources. Heterogeneous search teams (e.g., a team employing static sensors, unmanned ground vehicles, and unmanned aerial vehicles) could enhance the variety and overall quantity of resources deployed for search. Additionally, due to the varied nature of heterogeneous search resources, synergies between resource types could be exploited to achieve effectiveness that surpasses what would be achievable when working independently.

This dissertation presents the results of an in-depth investigation into the topic of search planning for locating mobile targets. Specifically, it presents novel methods and strategies for planning a mobile-target search with heterogeneous search resources to maximize the probability of success. The strategies and methods presented herein are:​ (i) a spatiotemporally optimized dynamic static-sensor network deployment planning method, (ii) two static-mobile hybrid search planning methodologies, and (iii) an aerial-ground hybrid search planning method. These methodologies were developed with an application to wilderness search and rescue in mind, assuming sparseness of search resources, the existence of terrain, and limited information regarding the target. However, despite this focus, the problem formulation and solution methods presented herein are applicable to a wide variety of problems in other fields as well. The validity of the methodologies is supported by the results of extensive simulated search experiments.

By developing search planning methodologies for heterogeneous search teams which include static-sensors, mobile ground units, and mobile aerial units, this research has expanded the range of resources and tools available to searchers to improve search success probability in real world mobile-target search.