Mechanical Engineering

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Multipass Target Search in Natural Environments
(MDPI, 2017-11-02) Kuhlman, Michael J.; Otte, Michael W.; Sofge, Donald; Gupta, Satyandra K.
Consider 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 𝜖-admissible heuristics to speed up the search. To the authors’ knowledge this is the first attempt at efficiently solving multipass target search problems of such long duration. The proposed algorithm is based on best first branch and bound and is benchmarked against state of the art algorithms adapted to the problem in natural Simplex environments, gathering the most information in the given search time.
Trajectory Planning for Autonomous Vehicles Performing Information Gathering Tasks
(2018) Kuhlman, Michael Joseph; Gupta, Satyandra K; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
This dissertation investigates mission scenarios for autonomous vehicles in which the objective is to gather information. This includes minimizing uncertainty of a target's estimated location, generating coverage plans to cover an area, or persistent monitoring tasks such as generating informative patrol routes. Information gathering tasks cannot be solved with shortest path planning algorithms since the rewards are path-dependent. Further, in order to deploy such algorithms effectively in the real world, generated plans must safely avoid obstacles, account for the motion uncertainty (e.g. due to swift currents), and constraints on the vehicle's dynamics such as maximum speed/acceleration. This work extends state-of-the-art information gathering algorithms by generating dynamically feasible trajectories for autonomous vehicles that are able to exploit the environment to find higher quality solutions, reducing mission costs. We also reduce mission risk without sacrificing the amount of information gathered. The focus of this dissertation will be to solve three related information gathering tasks that require generating dynamically feasible trajectories for reliable plan execution. When searching for targets, minimizing target location uncertainty with autonomous vehicles improves the effectiveness of ground relief crews. We investigate the use of mutual information for efficiently generating long duration multi-pass trajectories to minimize target location uncertainty in natural environments. We develop epsilon-admissible heuristics to create the epsilon-admissible Branch and Bound algorithm to gather the most information. Next, we investigate coordination techniques for underwater vehicle teams conducting large-scale geospatial tasks such as adaptive sampling or coverage planning. It is advantageous to exploit the currents of the ocean to increase endurance, which requires accounting for forecast uncertainty. We adapt Monte Carlo Tree Search and Cross Entropy Method to maximize path-dependent reward, and introduce an iterative greedy solver that outperforms state-of-the-art planners. Finally, we investigate persistent monitoring tasks such as sentry patrol routes and monitoring of harmful algae blooms in a littoral environment. Such an automated planner needs to generate collision-free coverage paths by moving waypoints to locations that both minimize path traversal costs and maximize the amount of information gathered along the path. We extend previous Lloyd's based algorithms by factoring in the ocean currents and introduce greedy methods that minimize mission risk while maximizing information gathered.
Multipass Target Search in Natural Environments
(MDPI, 2017-11-02) Kuhlman, Michael J.; Otte, Michael W.; Sofge, Donald; Gupta, Satyandra K.
Consider 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 e-admissible heuristics to speed up the search. To the authors’ knowledge this is the first attempt at efficiently solving multipass target search problems of such long duration. The proposed algorithm is based on best first branch and bound and is benchmarked against state of the art algorithms adapted to the problem in natural Simplex environments, gathering the most information in the given search time.

Mechanical Engineering

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