Ragi, Shankarachary, authorChong, Edwin K. P., advisorKrapf, Diego, committee memberLuo, J. Rockey, committee memberOprea, Iuliana, committee member2007-01-032007-01-032014http://hdl.handle.net/10217/82529We develop guidance algorithms to control mobile sensor platforms, for both centralized and decentralized settings, in dynamic environments for various applications. More precisely, we develop control algorithms for the following mobile sensor platforms: unmanned aerial vehicles (UAVs) with on-board sensors for multitarget tracking, autonomous amphibious vehicles for flood-rescue operations, and directional sensors (e.g., surveillance cameras) for maximizing an information-gain-based objective function. The following is a brief description of each of the above-mentioned guidance control algorithms. We develop both centralized and decentralized control algorithms for UAVs based on the theories of partially observable Markov decision process (POMDP) and decentralized POMDP (Dec-POMDP) respectively. Both POMDPs and Dec-POMDPs are intractable to solve exactly; therefore we adopt an approximation method called nominal belief-state optimization (NBO) to solve (approximately) the control problems posed as a POMDP or a Dec-POMDP. We then address an amphibious vehicle guidance problem for a flood rescue application. Here, the goal is to control multiple autonomous amphibious vehicles while minimizing the average rescue time of multiple human targets stranded in a flood situation. We again pose this problem as a POMDP, and extend the above-mentioned NBO approximation method to solve the guidance problem. In the final phase, we study the problem of controlling multiple 2-D directional sensors while maximizing an objective function based on the information gain corresponding to multiple target locations. This problem is found to be a combinatorial optimization problem, so we develop heuristic methods to solve the problem approximately, and provide analytical results on performance guarantees. We then improve the performance of our heuristics by applying an approximate dynamic programming approach called rollout.born digitaldoctoral dissertationsengCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.sensor fusionpath planning for autonomous vehiclestarget trackingapplications of POMDP and Dec-POMDPdecision making under uncertaintyText