Searching the diffusive source in an unknown obstructed environment by cognitive strategies with forbidden areas

Zhao, Yong; Chen, Bin; Zhu, Zhengqiu; Chen, Feiran; Wang, Yiduo; Ji, Yatai

doi:10.1016/j.buildenv.2020.107349

Cited by 25 publications

(9 citation statements)

References 21 publications

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“…Then, to show the advantage of using the receding horizon-based approach more clearly, the result with a specific scenario is discussed in details in Section V-C. The proposed search strategies are compared with the state-of-the-art source search strategy developed for obstacle-rich environments, named IWFA-2 [30], on a realistic scenario in Section V-D. Lastly, the hardware-in-the-loop simulation in 3D environment is presented in Section V-E.…”

Section: Numerical Simulationsmentioning

confidence: 99%

“…This is termed Infotaxis with RH-path planning. In addition, we compare RHI-BR with three and five horizon steps against state-of-the-art algorithm called IWFA-2 (Infotatxis with forbidden area-2) [30]. We present the outstanding performance of the proposed method, RHI-BR with three and five horizon steps with three sampled measurement sequences, in the obstacle-rich urban environment (ρ 0 = 0.3).…”

Section: Performance Comparison On Realistic Conditionsmentioning

confidence: 99%

“…In general, the search path generated by a one step lookahead decision making, which can be called a greedy algorithm, is inefficient in an obstaclerich environment. Although several existing studies for source search in the obstacle-rich environment is conducted, many of them assumed known environments (i.e., positions of obstacles are given) [25], [26] and used separate obstacle avoidance algorithm in unknown environments that is not tightly integrated 2 with search strategies to obtain better information about the source [27]- [30], leading to inefficient or unsuccessful source search.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Receding Horizon-Based Infotaxis With Random Sampling for Source Search and Estimation in Complex Environments

Park

Ładosz

Kim

et al. 2023

IEEE Trans. Aerosp. Electron. Syst.

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This paper proposes a receding horizon-based information-theoretic source search and estimation strategy for a mobile sensor in an urban environment in which an invisible harmful substance is released into the atmosphere. The mobile sensor estimates the source term including its location and release rate by using sensor observations based on Bayesian inference. The sampling-based sequential Monte Carlo method, particle filter, is employed to estimate the source term state in a highly nonlinear and stochastic system. Infotaxis, the information-theoretic gradient-free search strategy is modified to find the optimal search path that maximizes the reduction of the entropy of the source term distribution. In particular, receding horizon Infotaxis is introduced to avoid falling into the local optima and to find more successful information gathering paths in obstacle-rich urban environments. Besides, a random sampling method is introduced to reduce the computational load of the receding horizon Infotaxis for real-time computation. The random sampling method samples the predicted future measurements based on current estimation of the source term and computes the optimal search path using sampled measurements rather than considering all possible future measurements. To demonstrate the benefit of the proposed approach, comprehensive numerical simulations are performed for various conditions. The proposed algorithm increases the success rate by about 30% and reduces the mean search time by about 40% compared with the existing information-theoretic search strategy.

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Section: Numerical Simulationsmentioning

confidence: 99%

Section: Performance Comparison On Realistic Conditionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Receding Horizon-Based Infotaxis With Random Sampling for Source Search and Estimation in Complex Environments

Park

Ładosz

Kim

et al. 2023

IEEE Trans. Aerosp. Electron. Syst.

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show abstract

“…Informative path planning (IPP) approach considers the search process as an information gain process about the source position and local environment so as to plan an optimal path in terms a specified information measure (Hutchinson, Liu, & Chen, 2018;Hutchinson, Liu, Thomas, & Chen, 2019;Ristic, Morelande, & Gunatilaka, 2010;Ryan, & Hedrick, 2010;Ristic, Skvortsov, & Gunatilaka, 2016;Zhao, Chen, Zhu, Chen, Wang, & Ma, 2020;Zhao, Chen, Zhu, Chen, Wang, & Ji, 2020), which can be thought of as a strategy to explore the environment. Infotaxis (Hutchinson, Liu, & Chen, 2018) has been proposed and implemented in indoor experiment to estimate a source location using a ground mobile robot.…”

Section: Introductionmentioning

confidence: 99%

“…More recently, Zhao, Chen, Zhu, Chen, Wang, & Ma (2020) extended the Entrotaxis based planning approach as Entrotaxis-jump algorithm to search source in large-scale road networks. To solve the random blocking source search problem, an IPP approach combining Infotaxis and Entrotaxis is proposed in Zhao, Chen, Zhu, Chen, Wang, & Ji (2020). Different from control oriented approaches, so far there is no theoretical tool to analyze the convergence of IPP search strategy.…”

Section: Introductionmentioning

confidence: 99%

Multi-step dual control for exploration and exploitation in autonomous search with convergence guarantee

Yuan¹,

Yang²,

Chen³

et al. 2022

Preprint

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Motivated by the recently proposed dual control for exploration and exploitation (DCEE) concept, this paper presents a Multi-Step DCEE (MS-DCEE) framework with guaranteed convergence for autonomous search of a source of airborne dispersion. Different from the existing stochastic model predictive control (SMPC) algorithm and informative path planning (IPP) approaches, the proposed MS-DCEE approach uses the current and future input to not only drive the agent towards the estimated source location (exploitation) but also reduce its estimation uncertainty (exploration) by actively learning the operational environment. Unknown source target position, together with unknown environment, impose significant challenges in establishing the recursive feasibility and the convergence of the proposed algorithm. To address them, with the help of the property of Bayesian estimation, we develop a two-step approach where the unbiasedness of the mean estimation is assumed first and then the randomness of the mean estimate under each collected information sequence is accounted. Based on that, we develop a MS-DCEE scheme with suitable terminal ingredients where recursive feasibility and convergence are guaranteed. Two simulation scenarios are conducted, which show that the proposed MS-DCEE algorithm outperforms the SMPC, the IPP and the single-step DCEE approaches in terms of searching successful rates and efficiency.

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Information-Theoretic Autonomous Source Search and Estimation of Mobile Sensors

Park,

An,

Jang

et al. 2023

Advances in Industrial Control

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Searching the diffusive source in an unknown obstructed environment by cognitive strategies with forbidden areas

Cited by 25 publications

References 21 publications

Receding Horizon-Based Infotaxis With Random Sampling for Source Search and Estimation in Complex Environments

Receding Horizon-Based Infotaxis With Random Sampling for Source Search and Estimation in Complex Environments

Multi-step dual control for exploration and exploitation in autonomous search with convergence guarantee

Information-Theoretic Autonomous Source Search and Estimation of Mobile Sensors

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