A Locking Sweeping Method Based Path Planning for Unmanned Surface Vehicles in Dynamic Maritime Environments

Unmanned surface vehicles (USVs) are new marine intelligent platforms that can autonomously operate in various ocean environments with intelligent decision-making capability. As one of key technologies enabling such a capability, path planning algorithms underpin the navigation and motion control of USVs by providing optimized navigational trajectories. To accommodate complex maritime environments that include various static/moving obstacles, it is important to develop a computational efficient path planning algorithm for USVs so that real-time operation can be effectively carried out. This paper therefore proposes a new algorithm based on the fast sweeping method, named the locking sweeping method (LSM). Compared with other conventional path planning algorithms, the proposed LSM has an improved computational efficiency and can be well applied in dynamic environments that have multiple moving obstacles. When generating an optimal collision-free path, moving obstacles are modelled with ship domains that are calculated based upon ships’ velocities. To evaluate the effectiveness of the algorithm, particularly the capacity in dealing with practical environments, three different sets of simulations were undertaken in environments built using electronic nautical charts (ENCs). Results show that the proposed algorithm can effectively cope with complex maritime traffic scenarios by generating smooth and safe trajectories.

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Online Local Path Planning on the Campus Environment for Autonomous Driving Considering Road Constraints and Multiple Obstacles

Applied Sciences ◽

10.3390/app11093909 ◽

2021 ◽

Vol 11 (9) ◽

pp. 3909

Author(s):

Changhyeon Park ◽

Seok-Cheol Kee

Keyword(s):

Path Planning ◽

Driving Simulator ◽

Autonomous Driving ◽

Campus Environment ◽

Vehicle Localization ◽

Time Operation ◽

National University ◽

Planning Algorithm ◽

Path Planning Algorithm ◽

Multiple Obstacles

In this paper, an urban-based path planning algorithm that considered multiple obstacles and road constraints in a university campus environment with an autonomous micro electric vehicle (micro-EV) is studied. Typical path planning algorithms, such as A*, particle swarm optimization (PSO), and rapidly exploring random tree* (RRT*), take a single arrival point, resulting in a lane departure situation on the high curved roads. Further, these could not consider urban-constraints to set collision-free obstacles. These problems cause dangerous obstacle collisions. Additionally, for drive stability, real-time operation should be guaranteed. Therefore, an urban-based online path planning algorithm, which is robust in terms of a curved-path with multiple obstacles, is proposed. The algorithm is constructed using two methods, A* and an artificial potential field (APF). To validate and evaluate the performance in a campus environment, autonomous driving systems, such as vehicle localization, object recognition, vehicle control, are implemented in the micro-EV. Moreover, to confirm the algorithm stability in the complex campus environment, hazard scenarios that complex obstacles can cause are constructed. These are implemented in the form of a delivery service using an autonomous driving simulator, which mimics the Chungbuk National University (CBNU) campus.

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An energy-efficient path planning algorithm for unmanned surface vehicles

Ocean Engineering ◽

10.1016/j.oceaneng.2018.01.025 ◽

2018 ◽

Vol 161 ◽

pp. 308-321 ◽

Cited By ~ 19

Author(s):

Hanlin Niu ◽

Yu Lu ◽

Al Savvaris ◽

Antonios Tsourdos

Keyword(s):

Path Planning ◽

Energy Efficient ◽

Unmanned Surface Vehicles ◽

Planning Algorithm ◽

Path Planning Algorithm

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

Journal of Marine Science and Engineering ◽

10.3390/jmse7050132 ◽

2019 ◽

Vol 7 (5) ◽

pp. 132 ◽

Cited By ~ 2

Author(s):

Zhen Zhang ◽

Defeng Wu ◽

Jiadong Gu ◽

Fusheng Li

Keyword(s):

Path Planning ◽

High Speed ◽

Attractive Force ◽

Simulation Experiments ◽

Planning Strategy ◽

Unmanned Surface Vehicles ◽

Planning Algorithm ◽

Pass Through ◽

Path Planning Algorithm ◽

Narrow Area

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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Any-Angle Path Planning

AI Magazine ◽

10.1609/aimag.v34i4.2512 ◽

2013 ◽

Vol 34 (4) ◽

pp. 85-107 ◽

Cited By ~ 31

Author(s):

Alex Nash ◽

Sven Koenig

Keyword(s):

Video Games ◽

Path Planning ◽

Shortest Path ◽

Grid Cells ◽

Grid Graph ◽

Planning Algorithm ◽

Planning Algorithms ◽

Planning Methodology ◽

Path Planning Algorithm

In robotics and video games, one often discretizes continuous terrain into a grid with blocked and unblocked grid cells and then uses path-planning algorithms to find a shortest path on the resulting grid graph. This path, however, is typically not a shortest path in the continuous terrain. In this overview article, we discuss a path-planning methodology for quickly finding paths in continuous terrain that are typically shorter than shortest grid paths. Any-angle path-planning algorithms are variants of the heuristic path-planning algorithm A* that find short paths by propagating information along grid edges (like A*, to be fast) without constraining the resulting paths to grid edges (unlike A*, to find short paths).

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An application-orientated anti-collision path planning algorithm for unmanned surface vehicles

Ocean Engineering ◽

10.1016/j.oceaneng.2021.109298 ◽

2021 ◽

Vol 235 ◽

pp. 109298

Author(s):

Shengke Ni ◽

Zhengjiang Liu ◽

Dengjun Huang ◽

Yao Cai ◽

Xin Wang ◽

...

Keyword(s):

Path Planning ◽

Unmanned Surface Vehicles ◽

Planning Algorithm ◽

Path Planning Algorithm

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Adaptive Path Planning for Cleaning Robots Considering Dust Distribution

Journal of Robotics and Mechatronics ◽

10.20965/jrm.2018.p0005 ◽

2018 ◽

Vol 30 (1) ◽

pp. 5-14 ◽

Cited By ~ 5

Author(s):

Takahiro Sasaki ◽

Guillermo Enriquez ◽

Takanobu Miwa ◽

Shuji Hashimoto ◽

◽

...

Keyword(s):

Path Planning ◽

Entire Space ◽

Human Behaviors ◽

Cleaning Robot ◽

Planning Algorithm ◽

Planning Algorithms ◽

Cleaning Robots ◽

Dust Distribution ◽

Path Planning Algorithm

Path-planning algorithms for cleaning robots typically focus on how the robots can cover an entire space while minimizing overlapping or uncleaned areas. However, when considering actual environments, the distribution of dust and dirt is not uniform and has some specific features according to the shape of the environment and human behaviors. Therefore, if a cleaning robot plans its path while taking this distribution into consideration, it can clean the area more efficiently. In this paper, we present a novel path-planning algorithm for cleaning robots that prioritizes regions with large quantities of dirt and sorts them. The effectiveness of the proposed algorithm was examined through experimental simulations.

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A Survey of Path Planning Algorithms for Mobile Robots

Vehicles ◽

10.3390/vehicles3030027 ◽

2021 ◽

Vol 3 (3) ◽

pp. 448-468

Author(s):

Karthik Karur ◽

Nitin Sharma ◽

Chinmay Dharmatti ◽

Joshua E. Siegel

Keyword(s):

Path Planning ◽

Embedded System ◽

Mobile Robots ◽

Optimal Algorithm ◽

Constrained Systems ◽

Autonomous Cars ◽

Planning Algorithm ◽

Planning Algorithms ◽

Point To Point ◽

Path Planning Algorithm

Path planning algorithms are used by mobile robots, unmanned aerial vehicles, and autonomous cars in order to identify safe, efficient, collision-free, and least-cost travel paths from an origin to a destination. Choosing an appropriate path planning algorithm helps to ensure safe and effective point-to-point navigation, and the optimal algorithm depends on the robot geometry as well as the computing constraints, including static/holonomic and dynamic/non-holonomically-constrained systems, and requires a comprehensive understanding of contemporary solutions. The goal of this paper is to help novice practitioners gain an awareness of the classes of path planning algorithms used today and to understand their potential use cases—particularly within automated or unmanned systems. To that end, we provide broad, rather than deep, coverage of key and foundational algorithms, with popular algorithms and variants considered in the context of different robotic systems. The definitions, summaries, and comparisons are relevant to novice robotics engineers and embedded system developers seeking a primer of available algorithms.

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Research on Dynamic Path Planning of Wheeled Robot Based on Deep Reinforcement Learning on the Slope Ground

Journal of Robotics ◽

10.1155/2020/7167243 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10

Author(s):

Peng Wang ◽

Xiaoqiang Li ◽

Chunxiao Song ◽

Shipeng Zhai

Keyword(s):

Path Planning ◽

Tree Structure ◽

Moving Obstacles ◽

Dynamic Path Planning ◽

Wheeled Robot ◽

Multiple Paths ◽

Planning Algorithm ◽

Simulation And Experiment ◽

Dynamic Path ◽

Path Planning Algorithm

The existing dynamic path planning algorithm cannot properly solve the problem of the path planning of wheeled robot on the slope ground with dynamic moving obstacles. To solve the problem of slow convergence rate in the training phase of DDQN, the dynamic path planning algorithm based on Tree-Double Deep Q Network (TDDQN) is proposed. The algorithm discards detected incomplete and over-detected paths by optimizing the tree structure, and combines the DDQN method with the tree structure method. Firstly, DDQN algorithm is used to select the best action in the current state after performing fewer actions, so as to obtain the candidate path that meets the conditions. And then, according to the obtained state, the above process is repeatedly executed to form multiple paths of the tree structure. Finally, the non-maximum suppression method is used to select the best path from the plurality of eligible candidate paths. ROS simulation and experiment verify that the wheeled robot can reach the target effectively on the slope ground with moving obstacles. The results show that compared with DDQN algorithm, TDDQN has the advantages of fast convergence and low loss function.

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Rapid global path planning algorithm for unmanned surface vehicles in large-scale and multi-island marine environments

PeerJ Computer Science ◽

10.7717/peerj-cs.612 ◽

2021 ◽

Vol 7 ◽

pp. e612

Author(s):

Dong Wang ◽

Jie Zhang ◽

Jiucai Jin ◽

Deqing Liu ◽

Xingpeng Mao

Keyword(s):

Path Planning ◽

Large Scale ◽

Distance Constraint ◽

Marine Environments ◽

Fast Marching ◽

Unmanned Surface Vehicles ◽

Grid Map ◽

Global Path Planning ◽

Planning Algorithm ◽

Path Planning Algorithm

A global path planning algorithm for unmanned surface vehicles (USVs) with short time requirements in large-scale and complex multi-island marine environments is proposed. The fast marching method-based path planning for USVs is performed on grid maps, resulting in a decrease in computer efficiency for larger maps. This can be mitigated by improving the algorithm process. In the proposed algorithm, path planning is performed twice in maps with different spatial resolution (SR) grids. The first path planning is performed in a low SR grid map to determine effective regions, and the second is executed in a high SR grid map to rapidly acquire the final high precision global path. In each path planning process, a modified inshore-distance-constraint fast marching square (IDC-FM2) method is applied. Based on this method, the path portions around an obstacle can be constrained within a region determined by two inshore-distance parameters. The path planning results show that the proposed algorithm can generate smooth and safe global paths wherein the portions that bypass obstacles can be flexibly modified. Compared with the path planning based on the IDC-FM2 method applied to a single grid map, this algorithm can significantly improve the calculation efficiency while maintaining the precision of the planned path.

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