2.5D Navigation Graph and Improved A-Star Algorithm for Path Planning in Ship inside Virtual Environment

Xie, Wei; Fang, Xiongbing; Wu, Sing-Yung

doi:10.1109/phm-besancon49106.2020.00057

Cited by 13 publications

(8 citation statements)

References 11 publications

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“…The map that describes the surface of the mobile robot can be defined as 2.5D. An example of building such a map and determining the robot's path can be found in [35]. The article [36] describes an algorithm for navigating a robot in an uneven environment in a confined space with ramps and stairs.…”

Section: Traversabilty Estimationmentioning

confidence: 99%

Semantic-Aware Path Planning with Hexagonal Grids and Vehicle Dynamic Constraints

et al. 2023

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The article presents a navigation system that utilizes a semantic map created on a hexagonal grid. The system plans the path by incorporating semantic and metric information while considering the vehicle’s dynamic constraints. The article concludes by discussing a low-level control algorithm used in the system. This solution’s advantages include using a semantic map on a hexagonal grid, which enables more efficient and accurate navigation. Creating a map of allowable speeds based on the semantic map provides an additional layer of information that can help optimize the vehicle’s trajectory. Incorporating both semantic and metric information in the path-planning process leads to a more precise and tailored navigation solution that accounts for the vehicle’s capabilities and the environment it is operating in. Finally, the low-level control algorithm ensures that the vehicle follows the planned trajectory while considering real-time sensor data and other factors affecting its movement. Through this article, we aim to provide insights into the cutting-edge advancements in path planning techniques and shed light on the potential of combining hexagonal grids, vehicle dynamics constraints, and semantic awareness. These innovations have the potential to revolutionize autonomous navigation systems, enabling vehicles to navigate complex environments with greater efficiency, safety, and adaptability.

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Section: Traversabilty Estimationmentioning

confidence: 99%

Semantic-Aware Path Planning with Hexagonal Grids and Vehicle Dynamic Constraints

et al. 2023

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“…The classical A

algorithm is a graph search algorithm typically used for global path planning [ 27 ]. It can achieve the shortest path for a robot to move between two prescribed positions in a known static environment.…”

Section: Path Planning Algorithm For Mobile Robotmentioning

confidence: 99%

Path Planning for Wheeled Mobile Robot in Partially Known Uneven Terrain

Zhang

Zheng

et al. 2022

Sensors

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Path planning for wheeled mobile robots on partially known uneven terrain is an open challenge since robot motions can be strongly influenced by terrain with incomplete environmental information such as locally detected obstacles and impassable terrain areas. This paper proposes a hierarchical path planning approach for a wheeled robot to move in a partially known uneven terrain. We first model the partially known uneven terrain environment respecting the terrain features, including the slope, step, and unevenness. Second, facilitated by the terrain model, we use A☆ algorithm to plan a global path for the robot based on the partially known map. Finally, the Q-learning method is employed for local path planning to avoid locally detected obstacles in close range as well as impassable terrain areas when the robot tracks the global path. The simulation and experimental results show that the designed path planning approach provides satisfying paths that avoid locally detected obstacles and impassable areas in a partially known uneven terrain compared with the classical A☆ algorithm and the artificial potential field method.

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“…For example, collision avoidance, rules on the sea, and the motion characteristics of ships were taken as examples [9]. Moreover, some researchers even considered the space characteristics of the ship [10].…”

Section: Introductionmentioning

confidence: 99%

A Novel Algorithm for Ship Route Planning Considering Motion Characteristics and ENC Vector Maps

Hou

Zhu

2023

JMSE

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Global route planning is a pivotal function of unmanned surface vehicles (USVs). For ships, the safety of navigation is the priority. This paper presents the VK-RRT* algorithm as a way of designing the planned route automatically. Different from other algorithms or studies, this study employs electronic navigation chart (ENC) vector data instead of grid maps as the basis of the search, which reduces data error when converting the vector map into the grid map. In addition, Delaunay triangulation is employed to organize vector data, in which the depth value is taken as a factor to ensure the safety of the planning route. Furthermore, the initial planned route is not suitable for ship tracking as it does not consider the ship motion characteristics. Therefore, the planned route needs to be further optimized. In the final part, we also conducted experiments to verify the effectiveness and advantages of the proposed algorithm. The results show that the proposed algorithm could reduce the lengths of paths by about 23% on average and save planning time; these are largely dependent on the environment.

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2.5D Navigation Graph and Improved A-Star Algorithm for Path Planning in Ship inside Virtual Environment

Cited by 13 publications

References 11 publications

Semantic-Aware Path Planning with Hexagonal Grids and Vehicle Dynamic Constraints

Semantic-Aware Path Planning with Hexagonal Grids and Vehicle Dynamic Constraints

Path Planning for Wheeled Mobile Robot in Partially Known Uneven Terrain

A Novel Algorithm for Ship Route Planning Considering Motion Characteristics and ENC Vector Maps

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