Multi-AUV autonomous task planning based on the scroll time domain quantum bee colony optimization algorithm in uncertain environment

Li, Jianjun; Zhang, Rubo; Yu, Yang

doi:10.1371/journal.pone.0188291

Cited by 17 publications

(9 citation statements)

References 17 publications

(15 reference statements)

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“…In this scenario, AUV supports and provides solutions for navigation tasks [22,23]. The operation involves high levels of uncertainty and risks of random obstacle appeared event, off course event and key point unreached event in the vehicle.…”

Section: Test Case Scenariomentioning

confidence: 99%

Studies on situation reasoning approach of autonomous underwater vehicle under uncertain environment

Yin

Chen

et al. 2021

CAAI Trans on Intel Tech

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The purpose of this study is to provide a situation reasoning module for autonomous underwater vehicle (AUV), to make decision-making agents know what is the current situation, what needs to be done in the present situation, and what is the risk of task present situation. This will enhance efficiency for AUV. Situation reasoning for AUV aims to communicate mission assessments to decision-making agents. Thus, in solving this problem, it is important to design a new situation reasoning module for AUV. This article is presenting the situation reasoning module for AUV, which includes uncertain event detection, uncertain event recognition and ontology model of the situation. The article concludes by demonstrating the benefits of the situation reasoning module in a real-world scenario. A situation reasoning simulation is demonstrated in an AUV while performing a complex mission. The approach was validated by marine experiments and simulating experiments for AUV. Results show the effectiveness of situation reasoning approach.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Test Case Scenariomentioning

confidence: 99%

Studies on situation reasoning approach of autonomous underwater vehicle under uncertain environment

Yin

Chen

et al. 2021

CAAI Trans on Intel Tech

Self Cite

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show abstract

“…At the same time, he proposed an improved differential evolution quantum artificial bee colony (DEQABC) optimization algorithm to solve the multi-AUV optimal task allocation method. The simulation results show that the DEQABC algorithm converges faster than the artificial bee colony (ABC) algorithm in terms of the running time and number of iterations and effectively improves the AUV distributed multitasking performance [176]. Mahmoud Zadeh et al [27] used the four evolutionary path planning methods of PSO, BBO (Biogeography-Based Optimization), DE and FA to solve the underwater rendezvous problem.…”

Section: ) Differential Evolution (De)mentioning

confidence: 99%

Path Planning Technologies for Autonomous Underwater Vehicles-A Review

Li¹,

Wang²,

Du³

2019

IEEE Access

129

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An autonomous underwater vehicle (AUV) is an economical and safe tool that is well-suited for search, investigation, identification, and salvage operations on the sea floor. Path planning technology, which primarily includes modeling methods and path search algorithms, is an important technology for AUVs. In recent years, the AUV path planning technology has rapidly developed. Compared with land robots, AUVs must endure complex underwater environments and consider various factors, such as currents, water pressure, and topography. Challenges exist in terms of online obstacle avoidance, three-dimensional environment path planning, and the robustness of the algorithms. Adapting a complex environment and finding a suitable path planning method comprise the main problem that must be solved. In this paper, we summarize the principles, advantages, and disadvantages of modeling and path search technologies for AUVs. The most prominent feature of this paper is to summarize the improvement methods of various technical shortcomings and improve the original methods, such as dynamic obstacle avoidance, optimization path, coverage, and processing speed. In addition to summarizing the characteristics of each algorithm, this paper intuitively demonstrates the experimental environment, the real-time nature, the path planning range of the AUV, and so on. We also discuss the application scenarios of various modeling and path search technologies for AUVs. In addition, we discuss the challenges of AUVs and the direction of future research. INDEX TERMS AUV, path planning, model building, path search.

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“…Once a information sequence defines, a series of quantum binary data will be used to refer specially appointed information (e.g., 1 , , y d d  ) and the context will be used to refer to the information sequence as a whole (e.g., d). …”

Section: Operation Of Quantum Information Systemsmentioning

confidence: 99%

“…In quantum information system, the subnormalized operators of E reply to the basis groups of the classical register B are denoted by 1 …”

Section: Operation Of Quantum Information Systemsmentioning

confidence: 99%

See 1 more Smart Citation

An Entropy Measurement Method of Quantum Information System under Uncertain Environment

Li¹,

Deng²,

Zhou³

et al. 2018

IJIS

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Under uncertain environment, it is very difficult to measure the entropy of quantum information system, because there is no effective method to model the randomness. First, different from the traditional classic uncertainty, a quantum uncertain model is proposed to simulate a quantum information system under uncertain environment, and to simplify the entropy measurement of quantum information system. Second, different from the classic random seed under uncertain environment which is often called as pseudo-random number, here the quantum random is employed to provide us a true random model for the entropy of quantum information system. Third, the complex interaction and entangling activity of uncertain factors of quantum information is modeled as quantum binary instead of classic binary, which can help us to evaluate the entropy of uncertain environment, to analyze the entropy divergence in quantum information system. This work presents a non-classic risk factor measurement for quantum information system and a helpful entropy measurement.

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Multi-AUV autonomous task planning based on the scroll time domain quantum bee colony optimization algorithm in uncertain environment

Cited by 17 publications

References 17 publications

Studies on situation reasoning approach of autonomous underwater vehicle under uncertain environment

Studies on situation reasoning approach of autonomous underwater vehicle under uncertain environment

Path Planning Technologies for Autonomous Underwater Vehicles-A Review

An Entropy Measurement Method of Quantum Information System under Uncertain Environment

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