Multipass Target Search in Natural Environments
AbstractConsider a disaster scenario where search and rescue workers must search difficult to access buildings during an earthquake or flood. Often, finding survivors a few hours sooner results in a dramatic increase in saved lives, suggesting the use of drones for expedient rescue operations. Entropy can be used to quantify the generation and resolution of uncertainty. When searching for targets, maximizing mutual information of future sensor observations will minimize expected target location uncertainty by minimizing the entropy of the future estimate. Motion planning for multi-target autonomous search requires planning over an area with an imperfect sensor and may require multiple passes, which is hindered by the submodularity property of mutual information. Further, mission duration constraints must be handled accordingly, requiring consideration of the vehicle’s dynamics to generate feasible trajectories and must plan trajectories spanning the entire mission duration, something which most information gathering algorithms are incapable of doing. If unanticipated changes occur in an uncertain environment, new plans must be generated quickly. In addition, planning multipass trajectories requires evaluating path dependent rewards, requiring planning in the space of all previously selected actions, compounding the problem. We present an anytime algorithm for autonomous multipass target search in natural environments. The algorithm is capable of generating long duration dynamically feasible multipass coverage plans that maximize mutual information using a variety of techniques such as
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Kuhlman, M.J.; Otte, M.W.; Sofge, D.; Gupta, S.K. Multipass Target Search in Natural Environments. Sensors 2017, 17, 2514.
Kuhlman MJ, Otte MW, Sofge D, Gupta SK. Multipass Target Search in Natural Environments. Sensors. 2017; 17(11):2514.Chicago/Turabian Style
Kuhlman, Michael J.; Otte, Michael W.; Sofge, Donald; Gupta, Satyandra K. 2017. "Multipass Target Search in Natural Environments." Sensors 17, no. 11: 2514.
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