Effective Combination of Search Policy Based on Probability and Entropy for Heterogeneous Mobile Sensors

Imai, K.; Ushio, Toshimitsu

doi:10.1109/smc.2013.340

Cited by 2 publications

(5 citation statements)

References 13 publications

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“…The work is later expanded in Hsu et al [137]. A similar approach is taken by Imai and Ushio [138] to tracking multiple non-evasive targets. Another approach for an evasive single target is presented by Chin et al [139].…”

Section: Heuristic Controlmentioning

confidence: 97%

“…Both [83] Searching [140,144,118] Tracking [28,134,101,27,116,46,117] Single Target Both [] Searching [30,133] Tracking [24,26,42,47,48,70,125,111,110,112,113,124,115,135,107] Fig. 5: Classification of centralized approaches presented in this survey Distributed Multiple Targets Both [72,34,142,127,126,128,84] Searching [130,121,138] Tracking [53,119,129,82,122,94,95,29,81,106,…”

Section: Centralized Multiple Targetsmentioning

confidence: 99%

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Survey: mobile sensor networks for target searching and tracking

Albert

Imsland

2018

Cyber-Physical Systems

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Mobile sensor networks can be employed in multiple applications, such as search and rescue, border patrol, battle scenarios, and environmental monitoring. In this survey, we review the literature utilizing mobile sensor networks in applications classified as target searching and/or tracking. Our contribution is threefold. First, we focus on the diverse types of filters applied to estimating the state of the targets. Second, we present the most common approaches to high-level trajectory planning for the sensors in the network to do target searching and/or tracking. Finally, we classify the literature based on the problem formulation used and solution characteristics. At the end of the survey, we discuss the current state of the literature and possible directions for future research efforts.

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Section: Heuristic Controlmentioning

confidence: 97%

Section: Centralized Multiple Targetsmentioning

confidence: 99%

Survey: mobile sensor networks for target searching and tracking

Albert

Imsland

2018

Cyber-Physical Systems

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“…ap L(x )), i.e., all the environmental beliefs of the atomic propositions converge to the appropriate values. From (14), it then follows that B alph (x |= σ ) → 1 if σ = L(x ) and 0 otherwise. From (15), we then obtain B en (x |= σ ) → 1 if σ = L(x ) and 0 otherwise.…”

Section: A Proof Of Theoremmentioning

confidence: 99%

“…To describe the erroneous observations, we use a Bernoulli-type sensor model (see, e.g. [11][12][13][14]) as follows. Suppose that the rover's position is 𝑥 ∈ 𝑋 , and we would like the rover to know whether an atomic proposition 𝑎𝑝 ∈ 𝐴𝑃 𝑟 is satisfied at the position 𝑥 ′ ∈ 𝑋 .…”

Section: Sensor Modelmentioning

confidence: 99%

“…Roughly speaking, the objective of the copter is to actively explore the environment and reduce its uncertainties by updating the environmental beliefs of the atomic propositions. We first describe the observations by employing a Bernoulli-type sensor model, see, e.g., [11][12][13][14]. The Bernoulli sensor abstracts the complexity of image processing into binary observations.…”

Section: Introductionmentioning

confidence: 99%

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Collaborative Rover-copter Path Planning and Exploration with Temporal Logic Specifications Based on Bayesian Update Under Uncertain Environments

Hashimoto

Tsumagari

Ushio

2022

ACM Trans. Cyber-Phys. Syst.

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This paper investigates a collaborative rover-copter path planning and exploration with temporal logic specifications under uncertain environments. The objective of the rover is to complete a mission expressed by a syntactically co-safe linear temporal logic (scLTL) formula, while the objective of the copter is to actively explore the environment and reduce its uncertainties, aiming at assisting the rover and enhancing the efficiency of the mission completion. To formalize our approach, we first capture the environmental uncertainties by environmental beliefs of the atomic propositions, under an assumption that it is unknown which properties (or, atomic propositions) are satisfied in each area of the environment. The environmental beliefs of the atomic propositions are updated according to the Bayes rule based on the Bernoulli-type sensor measurements provided by both the rover and the copter. Then, the optimal policy for the rover is synthesized by maximizing a belief of the satisfaction of the scLTL formula through an implementation of an automata-based model checking. An exploration policy for the copter is then synthesized by employing the notion of an entropy that is evaluated based on the environmental beliefs of the atomic propositions, and a path that the rover intends to follow according to the optimal policy. As such, the copter can actively explore regions whose uncertainties are high and that are relevant to the mission completion. Finally, some numerical examples illustrate the effectiveness of the proposed approach.

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Effective Combination of Search Policy Based on Probability and Entropy for Heterogeneous Mobile Sensors

Cited by 2 publications

References 13 publications

Survey: mobile sensor networks for target searching and tracking

Survey: mobile sensor networks for target searching and tracking

Collaborative Rover-copter Path Planning and Exploration with Temporal Logic Specifications Based on Bayesian Update Under Uncertain Environments

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