Energy efficient path planning for Unmanned Surface Vehicle in spatially-temporally variant environment

Niu, Hongmei; Ji, Ze; Chai, Runqi; Tsourdos, Antonios

doi:10.1016/j.oceaneng.2019.106766

Cited by 68 publications

(36 citation statements)

References 36 publications

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“…1. USV is commanded by GCS to execute a long range surveillance mission by following a list of waypoints, which are generated by an energy efficient path planning algorithm [28] [29] of the GCS. While USV is following the path, it sends images to the GCS for analysis.…”

Section: Mission Scenariomentioning

confidence: 99%

Model Checking for Decision Making System of Long Endurance Unmanned Surface Vehicle

Niu

Chai

et al. 2021

2021 IEEE/SICE International Symposium on System Integration (SII)

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This work aims to develop a model checking method to verify the decision making system of Unmanned Surface Vehicle (USV) in a long range surveillance mission. The scenario in this work was captured from a long endurance USV surveillance mission using C-Enduro®, an USV manufactured by ASV Ltd. The C-Enduro USV may encounter multiple nondeterministic and concurrent problems including lost communication signals, collision risk and malfunction. The vehicle is designed to utilise multiple energy sources from solar panel, wind turbine and diesel generator. The energy state can be affected by the solar irradiance condition, wind condition, states of the diesel generator, sea current condition and states of the USV. In this research, the states and the interactive relations between environmental uncertainties, sensors, USV energy system, USV and Ground Control Station (GCS) decision making systems are abstracted and modelled successfully using Kripke models. The desirable properties to be verified are expressed using temporal logic statement and finally the safety properties and the long endurance properties are verified using the model checker MCMAS, a model checker for multi-agent systems. The verification results are analyzed and show the feasibility of applying model checking method to retrospect the desirable property of the USV decision making system. This method could assist researcher to identify potential design error of decision making system in advance.

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Section: Mission Scenariomentioning

confidence: 99%

Model Checking for Decision Making System of Long Endurance Unmanned Surface Vehicle

Niu

Chai

et al. 2021

2021 IEEE/SICE International Symposium on System Integration (SII)

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“…When it comes to the path planning problem, it is important that a number of factors should be taken into account, including the depth, the traffic density, the obstacle‐avoidance, the mainstream direction of the traffic flow (Ma et al, 2018). Niu et al (2020) proposed a Voronoi‐Visibility‐GA energy efficient (VVGAEE) energy efficient path planning algorithm, but it is not suitable for real‐time collision avoidance. Different to conventional path searching algorithms, with the recent advance in artificial intelligence (AI), the machine learning (reinforcement learning in particular) based algorithms have been widely used for autonomous navigation for unmanned ground vehicles (UGVs) (Saha & Dasgupta, 2017; Sombolestan et al, 2019), unmanned aerial vehicles (UAVs) (Choi & Cha, 2019).…”

Section: Literature Reviewmentioning

confidence: 99%

Navigating high‐speed unmanned surface vehicles: System approach and validations

Zhuang

Zhang

Wang

et al. 2020

Journal of Field Robotics

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With an increasing interest in the deployment of unmanned surface vehicles (USVs) to support complex ocean operations, high-speed USVs (≥40 knots) have become an important option, especially in accomplishing demanding tasks such as coastal guarding. At present, there is a vast amount of studies focusing on the development of USVs' autonomous navigation systems, and the results of most of them have only been verified by simulations or demonstrations on low-speed experimental USVs (≤20 knots). It remains unclear how high-speed USVs would perform in real applications. In particular, when USVs are navigating at high-speed, reliable obstacle detections and robust motion control become increasingly difficult to achieve than in normal conditions. This paper therefore presents a new autonomous navigation system for high-speed USVs with the system software and hardware been designed on the basis for ensuring a safe and reliable operation. Within such a system, a collision avoidance algorithm based on velocity obstacle able to adjust the USV's heading and speed in real-time has been integrated. A local environment modelling method and a heading maintain method by considering Coast Guard International Regulations for Avoiding Collision at Sea are proposed to improve the reliability and stability of the system. The effectiveness and efficiency of the proposed system has been verified and validated by hardware-in-the-loop simulation tests and sea trials on a high-speed USV ("Tianxing No. 1") in practical maritime environments. Results show that the autonomous navigation system adapts to the characteristics of highspeed USVs and can guide high-speed USVs (≥40 knots) in realising a safe autonomous navigation, which significantly improves the USV's autonomy level.

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“…8 An energy-efficient path planning algorithm was proposed to address the challenges with the presence of the current. 9 A path planning method was proposed for USV under uncertain, inaccurate, and dynamic marine information. 10 An energy-efficient path planning algorithm was proposed to consider the current based on the A* method, and a realistic case of an autonomous underwater glider surveying the Western Mediterranean Sea was considered.…”

Section: Introductionmentioning

confidence: 99%

Online planning low-cost paths for unmanned surface vehicles based on the artificial vector field and environmental heuristics

Wen

Zhang

Liu

et al. 2020

International Journal of Advanced Robotic Systems

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The study is concerned with the problem of online planning low-cost cooperative paths; those are energy-efficient, easy-to-execute, and low collision probability for unmanned surface vehicles (USVs) based on the artificial vector field and environmental heuristics. First, we propose an artificial vector field method by following the global optimally path and the current to maximize the known environmental information. Then, to improve the optimal rapidly exploring random tree (RRT*) based planner by the environment heuristics, a Gaussian sampling scheme is adopted to seek for the likely samples that locate near obstacles. Meanwhile, a multisampling strategy is proposed to choose low-cost path tree extensions locally. The vector field guidance, the Gaussian sampling scheme, and the multisampling strategy are used to improve the efficiency of RRT* to obtain a low-cost path for the virtual leader of USVs. To promote the accuracy of collision detection during the execution process of RRT*, an ellipse function-based bounding box for USVs is proposed with the consideration of the current. Finally, an information consensus scheme is employed to quickly calculate cooperative paths for a fleet of USVs guided by the virtual leader. Simulation results show that our online cooperative path planning method is performed well in the practical marine environment.

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Energy efficient path planning for Unmanned Surface Vehicle in spatially-temporally variant environment

Cited by 68 publications

References 36 publications

Model Checking for Decision Making System of Long Endurance Unmanned Surface Vehicle

Model Checking for Decision Making System of Long Endurance Unmanned Surface Vehicle

Navigating high‐speed unmanned surface vehicles: System approach and validations

Online planning low-cost paths for unmanned surface vehicles based on the artificial vector field and environmental heuristics

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