An energy-efficient path planning algorithm for unmanned surface vehicles

Niu, Hongmei; Yu, Lu; Chai, Runqi; Tsourdos, Antonios

doi:10.1016/j.oceaneng.2018.01.025

Cited by 104 publications

(54 citation statements)

References 14 publications

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“…e energy consumption of the USV while sailing is derived from the propulsion system. e resistance determines the effective power of the vessel [40]. e hydrodynamic drag F d is calculated as…”

Section: Optimization Model Of Local Pathmentioning

confidence: 99%

“…Calculate the particle's fitness value according to (26); (8) Update pbest ij (t) according to (39); (9) Update gbest(t) according to (40) and 41; (10) Calculate θ c ij (t) according to (44); (11) Update θ ij (t + 1) according to (45); (12) Determine the new position of particle i according to (46); (13) Output the optimal solution gbest. ALGORITHM 1: MQPSO.…”

Section: Performance Evaluation Of Mqpsomentioning

confidence: 99%

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Local Path Planning for Unmanned Surface Vehicle Collision Avoidance Based on Modified Quantum Particle Swarm Optimization

et al. 2020

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An unmanned surface vehicle (USV) plans its global path before the mission starts. When dynamic obstacles appear during sailing, the planned global path must be adjusted locally to avoid collision. This study proposes a local path planning algorithm based on the velocity obstacle (VO) method and modified quantum particle swarm optimization (MQPSO) for USV collision avoidance. The collision avoidance model based on VO not only considers the velocity and course of the USV but also handles the variable velocity and course of an obstacle. According to the collision avoidance model, the USV needs to adjust its velocity and course simultaneously to avoid collision. Due to the kinematic constraints of the USV, the velocity window and course window of the USV are determined by the dynamic window approach (DWA). In summary, local path planning is transformed into a multiobjective optimization problem with multiple constraints in a continuous search space. The optimization problem is to obtain the USV’s optimal velocity variation and course variation to avoid collision and minimize its energy consumption under the rules of the International Regulations for Preventing Collisions at Sea (COLREGs) and the kinematic constraints of the USV. Since USV local path planning is completed in a short time, it is essential that the optimization algorithm can quickly obtain the optimal value. MQPSO is primarily proposed to meet that requirement. In MQPSO, the efficiency of quantum encoding in quantum computing and the optimization ability of representing the motion states of the particles with wave functions to cover the whole feasible solution space are combined. Simulation results show that the proposed algorithm can obtain the optimal values of the benchmark functions and effectively plan a collision-free path for a USV.

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Section: Optimization Model Of Local Pathmentioning

confidence: 99%

Section: Performance Evaluation Of Mqpsomentioning

confidence: 99%

Local Path Planning for Unmanned Surface Vehicle Collision Avoidance Based on Modified Quantum Particle Swarm Optimization

et al. 2020

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“…(1) At present, most USV global path planning algorithms only search for a feasible path for one objective [3][4][5][6]. In this paper, path planning was considered with multiple simultaneous objectives, which were path length, energy consumption, path smoothness, and path safety.…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

“…Since the environmental loads such as wind, waves, and ocean currents influence the performance of the USV, the calculation of the USV's energy consumption is complex. Niu et al [4] considered the effect of the ocean current on the energy consumption of USVs. Lee et al [5] found a more economical path by considering the shallow water effect as well as tidal currents and wind for surface ship navigation.…”

Section: Introductionmentioning

confidence: 99%

Global Path Planning for Unmanned Surface Vehicle Based on Improved Quantum Ant Colony Algorithm

Xia

Han

Zhao

et al. 2019

Mathematical Problems in Engineering

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As a tool to monitor marine environments and to perform dangerous tasks instead of manned vessels, unmanned surface vehicles (USVs) have extensive applications. Because most path planning algorithms have difficulty meeting the mission requirements of USVs, the purpose of this study was to plan a global path with multiple objectives, such as path length, energy consumption, path smoothness, and path safety, for USV in marine environments. A global path planning algorithm based on an improved quantum ant colony algorithm (IQACA) is proposed. The improved quantum ant colony algorithm is an algorithm that benefits from the high efficiency of quantum computing and the optimization ability of the ant colony algorithm. The proposed algorithm can plan a path considering multiple objectives simultaneously. The simulation results show that the proposed algorithm’s obtained minimum was 2.1–6.5% lower than those of the quantum ant colony algorithm (QACA) and ant colony algorithm (ACA), and the number of iterations required to converge to the minimum was 11.2–24.5% lower than those of the QACA and ACA. In addition, the optimized path for the USV was obtained effectively and efficiently.

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“…To address a vehicle's path planning, many kinds of path-planning algorithms have been proposed by researchers [10]. Zeng et al presented an online dynamic path re-planning system for an autonomous underwater vehicle [11].…”

Section: Introductionmentioning

confidence: 99%

A Path-Planning Strategy for Unmanned Surface Vehicles Based on an Adaptive Hybrid Dynamic Stepsize and Target Attractive Force-RRT Algorithm

Zhang

et al. 2019

JMSE

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It is well known that path planning has always been an important study area for intelligent ships, especially for unmanned surface vehicles (USVs). Therefore, it is necessary to study the path-planning algorithm for USVs. As one of the basic algorithms for USV path planning, the rapidly-exploring random tree (RRT) is popular due to its simple structure, high speed and ease of modification. However, it also has some obvious drawbacks and problems. Designed to perfect defects of the basic RRT and improve the performance of USVs, an enhanced algorithm of path planning is proposed in this study, called the adaptive hybrid dynamic stepsize and target attractive force-RRT(AHDSTAF-RRT). The ability to pass through a narrow area and the forward speed in open areas of USVs are improved by adopting the AHDSTAF-RRT in comparison to the basic RRT algorithm. The improved algorithm is also applied to an actual gulf map for simulation experiments, and the experimental data is collected and organized. Simulation experiments show that the proposed AHDSTAF-RRT in this paper outperforms several existing RRT algorithms, both in terms of path length and calculating speed.

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An energy-efficient path planning algorithm for unmanned surface vehicles

Cited by 104 publications

References 14 publications

Local Path Planning for Unmanned Surface Vehicle Collision Avoidance Based on Modified Quantum Particle Swarm Optimization

Local Path Planning for Unmanned Surface Vehicle Collision Avoidance Based on Modified Quantum Particle Swarm Optimization

Global Path Planning for Unmanned Surface Vehicle Based on Improved Quantum Ant Colony Algorithm

A Path-Planning Strategy for Unmanned Surface Vehicles Based on an Adaptive Hybrid Dynamic Stepsize and Target Attractive Force-RRT Algorithm

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