Velodyne HDL-64E lidar for unmanned surface vehicle obstacle detection

2010 ◽  
Author(s):  
Ryan Halterman ◽  
Michael Bruch
Keyword(s):  
Obstacle Detection ◽  
Unmanned Surface Vehicle

Real-time obstacle detection for Unmanned Surface Vehicle

2011 ◽  
Author(s):  
Han Wang ◽  
Zhuo Wei ◽  
Sisong Wang ◽  
Chek Seng Ow ◽  
Kah Tong Ho ◽  
...  
Keyword(s):  
Real Time ◽  
Obstacle Detection ◽  
Unmanned Surface Vehicle

scholarly journals Smart Sensor Based Obstacle Detection for High-Speed Unmanned Surface Vehicle

2015 ◽  
Vol 48 (16) ◽  
pp. 190-197 ◽  
Author(s):  
D. Hermann ◽  
R. Galeazzi ◽  
J.C. Andersen ◽  
M. Blanke
Keyword(s):  
High Speed ◽  
Obstacle Detection ◽  
Smart Sensor ◽  
Unmanned Surface Vehicle

A novel method of unmanned surface vehicle autonomous cruise

2016 ◽  
Vol 43 (1) ◽  
pp. 121-130 ◽  
Author(s):  
Shaorong Xie ◽  
Peng Wu ◽  
Hengli Liu ◽  
Peng Yan ◽  
Xiaomao Li ◽  
...  
Keyword(s):  
Path Planning ◽  
Obstacle Detection ◽  
Planning Method ◽  
Dijkstra Algorithm ◽  
Complex Environment ◽  
Unmanned Surface Vehicle ◽  
Content Type ◽  
Global Path Planning ◽  
Local Path Planning ◽  
Local Path

Purpose – This paper aims to propose a new method for combining global path planning with local path planning, to provide an efficient solution for unmanned surface vehicle (USV) path planning despite the changeable environment. Path planning is the key issue of USV navigation. A lot of research works were done on the global and local path planning. However, little attention was given to combining global path planning with local path planning. Design/methodology/approach – A search of shortcut Dijkstra algorithm was used to control the USV in the global path planning. When the USV encounters unknown obstacles, it switches to our modified artificial potential field (APF) algorithm for local path planning. The combinatorial method improves the approach of USV path planning in complex environment. Findings – The method in this paper offers a solution to the issue of path planning in changeable or unchangeable environment, and was confirmed by simulations and experiments. The USV follows the global path based on the search of shortcut Dijkstra algorithm. Both USV achieves obstacle avoidances in the local region based on the modified APF algorithm after obstacle detection. Both the simulation and experimental results demonstrate that the combinatorial path planning method is more efficient in the complex environment. Originality/value – This paper proposes a new path planning method for USV in changeable environment. The proposed method is capable of efficient navigation in changeable and unchangeable environment.

Autonomous obstacle avoidance of an unmanned surface vehicle based on cooperative manoeuvring

2017 ◽  
Vol 44 (1) ◽  
pp. 64-74 ◽  
Author(s):  
Peng Wu ◽  
Shaorong Xie ◽  
Hengli Liu ◽  
Ming Li ◽  
Hengyu Li ◽  
...  
Keyword(s):  
Obstacle Avoidance ◽  
Optimal Path ◽  
Field Trials ◽  
Obstacle Detection ◽  
Local Region ◽  
Local Algorithm ◽  
Unmanned Surface Vehicle ◽  
Content Type ◽  
Conventional Methods ◽  
Sensor Errors

Purpose Autonomous obstacle avoidance is important in unmanned surface vehicle (USV) navigation. Although the result of obstacle detection is often inaccurate because of the inherent errors of LIDAR, conventional methods typically emphasize on a single obstacle-avoidance algorithm and neglect the limitation of sensors and safety in a local region. Conventional methods also fail in seamlessly integrating local and global obstacle avoidance algorithms. This paper aims to present a cooperative manoeuvring approach including both local and global obstacle avoidance. Design/methodology/approach The global algorithm used in our USV is the Artificial Potential Field-Ant Colony Optimization (APF-ACO) obstacle-avoidance algorithm, which plans a relative optimal path on the specified electronic map before the cruise of USV. The local algorithm is a multi-layer obstacle-avoidance framework based on a single LIDAR to present an efficient solution to USV path planning in the case of sensor errors and collision risks. When obstacles are within a layer, the USV uses a corresponding obstacle-avoidance algorithm. Then the USV moves towards the global direction according to fuzzy rules in the fuzzy layer. Findings The presented method offers a solution for obstacle avoidance in a complex environment. The USV follows the global trajectory planed by the APF-ACO algorithm. While, the USV can bypass current obstacle in the local region based on the multi-layer method effectively. This fact was validated by simulations and field trials. Originality/value The method presented in this paper takes advantage of algorithm integration that remedies errors of obstacle detection. Simulation and experiments were also conducted for performance evaluation.

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