A. James Clark School of Engineering
Permanent URI for this communityhttp://hdl.handle.net/1903/1654
The collections in this community comprise faculty research works, as well as graduate theses and dissertations.
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Item 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.Item Distributed Task Allocation Algorithms for Multi-Agent Systems with Very Low Communication(IEEE, 2022-11-23) Bapat, Akshay; Bora, Bharath Reddy; Herrmann, Jeffrey W.; Azarm, Shapour; Xu, Huan; Otte, Michael W.In this paper we explore the problem of task allocation when communication is very low, e.g., when the probability of a successful message between agents is ≪0.01 . Such situations may occur when agents choose not to communicate for reasons of stealth or when agent-to-agent communication is actively jammed by an adversary. In such cases, agents may need to divide tasks without knowing the locations of each other. Given the assumption that agents know the total number of agents in the workspace, we investigate solutions that ensure all tasks are eventually completed—even if some of the agents are destroyed. We present two task allocation algorithms that assume communication may not happen, but that benefit whenever communications are successful. (1) The Spatial Division Playbook Algorithm divides task among agents based on an area decomposition. (2) The Traveling Salesman Playbook Algorithm considers mission travel distance by leveraging Christofides’ 3/2 approximation algorithm. These algorithms have task completion runtime complexity of O(mlogm) and O(m3) , respectively, where m refers to the total number of tasks. We compare both algorithms to four state-of-the-art task allocation algorithms — ACBBA, DHBA, PIA and GA — across multiple communication levels and multiple numbers of targets, and using three different communication models. The new algorithms perform favorably, in terms of the time required to ensure all targets are visited, in the special case when communication is very low.Item 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.