Adaptive task allocation for search area coverage

Meuth, Ryan; Saad, Emad; Wunsch, Donald C.; Vian, John

doi:10.1109/tepra.2009.5339643

Cited by 20 publications

(22 citation statements)

References 19 publications

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“…In addition, existing methods plan paths that do not satisfy the robotic agents' dynamics and thus are not feasible system trajectories. This raises the need for an additional step where the path is treated as a series of waypoints and the agent is separately controlled to visit them all [4], [19], [20]. This double-step process-first, path planning and then, robot control-might result in hard-to-accomplish maneuvers for the robotic system.…”

Section: A Area Search and Coveragementioning

confidence: 99%

“…The Receding-Horizon Ergodic Exploration (RHEE) process for a dynamically varying Φ(x) is given in Algorithm 2. The re-initialization of RHEE when a new Φ(x) is available serves two purposes: first, it allows for the new coefficients φ k to be calculated 4 is particularly important for the algorithm performance because it regulates how far back in the past the algorithm should look when comparing the spatial statistics of the trajectory (parameterized by c k ) to the input spatial distribution (parameterized by φ k ). If the spatial distribution is regularly changing to incorporate new data (for example in the case that the distribution represents expected information density in target localization as we will see in Section V), it is undesirable for the algorithm to use state trajectory data all the way since the beginning of exploration.…”

Section: Algorithm 1 Receding-horizon Ergodic Exploration (Rhee)mentioning

confidence: 99%

“…Nonlinear agent dynamics: As opposed to geometric pointto-point approaches [4], [19], [20], the proposed algorithm controls agents with complex nonlinear dynamics, such as robotic fish [21], [22], 12-dimensional unmanned aerial vehicles (UAVs) [23], [24], etc., taking advantage of their dynamical features to achieve efficient area exploration.…”

Section: Introductionmentioning

confidence: 99%

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Real-Time Area Coverage and Target Localization Using Receding-Horizon Ergodic Exploration

et al. 2018

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Abstract-Although a number of solutions exist for the problems of coverage, search and target localization-commonly addressed separately-whether there exists a unified strategy that addresses these objectives in a coherent manner without being application-specific remains a largely open research question. In this paper, we develop a receding-horizon ergodic control approach, based on hybrid systems theory, that has the potential to fill this gap. The nonlinear model predictive control algorithm plans real-time motions that optimally improve ergodicity with respect to a distribution defined by the expected information density across the sensing domain. We establish a theoretical framework for global stability guarantees with respect to a distribution. Moreover, the approach is distributable across multiple agents, so that each agent can independently compute its own control while sharing statistics of its coverage across a communication network. We demonstrate the method in both simulation and in experiment in the context of target localization, illustrating that the algorithm is independent of the number of targets being tracked and can be run in real-time on computationally limited hardware platforms.

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Section: A Area Search and Coveragementioning

confidence: 99%

Section: Algorithm 1 Receding-horizon Ergodic Exploration (Rhee)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Real-Time Area Coverage and Target Localization Using Receding-Horizon Ergodic Exploration

et al. 2018

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“…With the notable exception of the binary-valued ART1 algorithm, most of these algorithms work upon real-valued preprocessed feature datasets via the use of fuzzy feature membership [19][20][21][22][23]. In contrast, the Gram-ART algorithm was designed for the meta-optimization of genetic algorithms, and thus, is designed to work with variable-length symbolic datasets [24], but it too has its shortcomings when tackling the large numbers of terminal symbols encountered in medical disease datasets.…”

Section: Introductionmentioning

confidence: 99%

Analyzing Biomedical Datasets with Symbolic Tree Adaptive Resonance Theory

Petrenko,

Hier,

Bone

et al. 2024

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Biomedical datasets distill many mechanisms of human diseases, linking diseases to genes and phenotypes (signs and symptoms of disease), genetic mutations to altered protein structures, and altered proteins to changes in molecular functions and biological processes. It is desirable to gain new insights from these data, especially with regard to the uncovering of hierarchical structures relating disease variants. However, analysis to this end has proven difficult due to the complexity of the connections between multi-categorical symbolic data. This article proposes symbolic tree adaptive resonance theory (START), with additional supervised, dual-vigilance (DV-START), and distributed dual-vigilance (DDV-START) formulations, for the clustering of multi-categorical symbolic data from biomedical datasets by demonstrating its utility in clustering variants of Charcot–Marie–Tooth disease using genomic, phenotypic, and proteomic data.

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“…M ULTI-ROBOT Task Allocation (MRTA) has been used in a wide range of applications such as defense [4], search-and-rescue [5], [6], agricultural spraying [7], and surveillance [8]. MRTA can be viewed as a generalized version of the Traveling Salesman Problem or Vehicle Routing Problem, both of which are shown to be NP-Hard [9], [10].…”

Section: Introductionmentioning

confidence: 99%

Communication-Aware Consensus-Based Decentralized Task Allocation in Communication Constrained Environments

2022

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This paper presents a deep reinforcement learning framework in which agents learn how to schedule and censor themselves amongst the other agents competing for access to a limited communication medium. In particular, the process learns to schedule the communication between agents to improve the performance of task allocation in environments with constrained communication in terms of limited bandwidth and message collision. The proposed approach, called Communication-Aware Consensus-Based Bundle Algorithm (CA-CBBA), extends the previous CBBA [1] in that the learned communication policy enables efficient utilization of the shared medium by prioritizing agents with messages that are important for the mission. Furthermore, agents in denser parts of the network are censored appropriately to alleviate the message collision and hidden node problems. We evaluate our approach in various task assignment scenarios, and the results show that CA-CBBA outperforms CBBA in terms of convergence time, rate of conflict resolution, and task allocation reward. Moreover, we show that CA-CBBA yields a policy that generalizes beyond the training set to handle larger team sizes. Finally, the results on time-critical problems, such as a search-and-rescue mission, show that CA-CBBA also outperforms the baselines considered (e.g., CBBA, MCDGA [2], and ACBBA [3]) in terms of number of unassigned and conflicted tasks in most of the scenarios.

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Adaptive task allocation for search area coverage

Cited by 20 publications

References 19 publications

Real-Time Area Coverage and Target Localization Using Receding-Horizon Ergodic Exploration

Real-Time Area Coverage and Target Localization Using Receding-Horizon Ergodic Exploration

Analyzing Biomedical Datasets with Symbolic Tree Adaptive Resonance Theory

Communication-Aware Consensus-Based Decentralized Task Allocation in Communication Constrained Environments

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