Early Warning Obstacle Avoidance-Enabled Path Planning for Multi-AUV-Based Maritime Transportation Systems

Han, Guangjie; Qi, Xingyue; Peng, Yan; Lin, Chuan; Zhang, Yu; Lu, Qi

doi:10.1109/tits.2022.3157436

Cited by 22 publications

(6 citation statements)

References 39 publications

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“…With respect to collision avoidance solutions, Han et al introduce an early warning, obstacle avoidance path planning method for multi-AUV networks in underwater IoT-enhanced marine technology systems (Han, et al, 2023). Utilizing Software Defined Networking (SDN) to improve scalability and control, the proposed Software Defined multi-AUV-based Underwater Network (SD-UWN) architecture employs flexible topology and artificial potential field theories for network control.…”

Section: Related Researchmentioning

confidence: 99%

Integrated Autonomous Underwater Vehicle Path Planning and Collision Avoidance Algorithms

Danielis,

Brekenfelder,

Parzyjegla

et al. 2024

Trans. Marit. Sci.

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This paper addresses the growing need for efficient autonomous underwater vehicle (AUV) path planning, used in environmental monitoring, underwater surveillance, and search and rescue operations. The aim was to develop robust path planning strategies that ensure safe and efficient AUV navigation in complex and unpredictable underwater environments. Our approach combines path planning algorithms with the OMNeT++ network simulator. The procedures for the implementation of these algorithms and AUV motion simulation have been outlined. The algorithms were applied not only to single AUV missions but also to scenarios involving multiple AUVs, allowing exploration of cooperative and coordinated mission planning in diverse underwater settings. The results of the study demonstrate the potential of our approach to address real-world challenges encountered by AUVs. AUV behavior was observed in different simulated mission scenarios and environmental conditions. Our findings shed light on the adaptability of AUVs in the face of unexpected obstacles and dynamic ocean currents. In conclusion, this study contributes to the field of AUV applications, offering a path planning and simulation strategy adaptable to diverse AUV mission requirements. By utilizing simulation models, we illustrate AUVs' ability to autonomously adapt their mission plan and avoid obstacles during missions, improving operational efficiency and safety.

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Section: Related Researchmentioning

confidence: 99%

Integrated Autonomous Underwater Vehicle Path Planning and Collision Avoidance Algorithms

Danielis,

Brekenfelder,

Parzyjegla

et al. 2024

Trans. Marit. Sci.

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show abstract

“…With the growing interest in futuristic ocean technology concepts proposed in recent years, such as the oceanix city [1], smart coastal [2], underwater smart city [3], etc., the intelligent control technology for marine vehicles is becoming one of the most important issues in the implementation of the Internet of Underwater Things (IoUT) technologies. Accordingly, autonomous marine vehicles (AMVs) are rapidly developing and are widely used in practical tasks, typically the autonomous surface vehicle (ASV), autonomous underwater glider (AUG), autonomous underwater vehicle (AUV), etc., which are commonly used in seabed exploration to collect information on the distribution of underwater resources [4][5][6]. Accordingly, technical issues for controlling the AMVs, such as multi-vehicle collaboration, path planning, obstacle avoidance, etc., have been introduced in recent years to enable practical underwater exploration, and researchers have studied the innovation in terms of algorithm design and system frameworks to find optimal solutions for efficient, safe and energy-saving marine exploration tasks [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Heuristic Surface Path Planning Method for AMV-Assisted Internet of Underwater Things

et al. 2023

Self Cite

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Ocean exploration is one of the fundamental issues for the sustainable development of human society, which is also the basis for realizing the concept of the Internet of Underwater Things (IoUT) applications, such as the smart ocean city. The collaboration of heterogeneous autonomous marine vehicles (AMVs) based on underwater wireless communication is known as a practical approach to ocean exploration, typically with the autonomous surface vehicle (ASV) and the autonomous underwater glider (AUG). However, the difference in their specifications and movements makes the following problems for collaborative work. First, when an AUG floats to a certain depth, and an ASV interacts via underwater wireless communication, the interaction has a certain time limit and their movements to an interaction position have to be synchronized; secondly, in the case where multiple AUGs are exploring underwater, the ASV needs to plan the sequence of surface interactions to ensure timely and efficient data collection. Accordingly, this paper proposes a heuristic surface path planning method for data collection with heterogeneous AMVs (HSPP-HA). The HSPP-HA optimizes the interaction schedule between ASV and multiple AUGs through a modified shuffled frog-leaping algorithm (SFLA). It applies a spatial-temporal k-means clustering in initializing the memeplex group of SFLA to adapt time-sensitive interactions by weighting their spatial and temporal proximities and adopts an adaptive convergence factor which varies by algorithm iterations to balance the local and global searches and to minimize the potential local optimum problem in each local search. Through simulations, the proposed HSPP-HA shows advantages in terms of access rate, path length and data collection rate compared to recent and classic path planning methods.

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“…However, when faced with high-efficiency, large-scale task requirements, a single AUV cannot meet the requirements. A new approach for resolving intricate marine tasks is offered using multi-AUV systems [2]. Some scholars have recently shown interest in consensus control [3], formation control [4,5], flocking control [6], and containment control [7] of multi-agent systems (MASs).…”

Section: Introductionmentioning

confidence: 99%

Multiple Autonomous Underwater Vehicle Formation Obstacle Avoidance Control Using Event-Triggered Model Predictive Control

Wang,

Xu,

Han

et al. 2023

JMSE

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In this paper, multiple autonomous underwater vehicle (multi-AUV) formation control with obstacle avoidance ability in 3D complex underwater environments based on an event-triggered model predictive control (EMPC) is proposed. Firstly, multi-AUV motion model systems are developed. The navigation reference trajectory of the follower AUVs can be obtained using a multi-AUV relative motion model. Secondly, in order to overcome the speed jump and obstacle avoidance problem in multi-AUV systems, compatibility constraints are presented in MPC that limit the uncertainty deviation of each AUV. The event-triggered mechanism (ET) is designed to decrease the computational load, which is based on the error between the optimal predicted and current state of the AUV. Finally, the effectiveness and superiority of the proposed algorithm are confirmed via simulation and compared with those of other algorithms.

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Early Warning Obstacle Avoidance-Enabled Path Planning for Multi-AUV-Based Maritime Transportation Systems

Cited by 22 publications

References 39 publications

Integrated Autonomous Underwater Vehicle Path Planning and Collision Avoidance Algorithms

Integrated Autonomous Underwater Vehicle Path Planning and Collision Avoidance Algorithms

Heuristic Surface Path Planning Method for AMV-Assisted Internet of Underwater Things

Multiple Autonomous Underwater Vehicle Formation Obstacle Avoidance Control Using Event-Triggered Model Predictive Control

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