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.

Robotic Fish Path Planning Based on an Improved A* Algorithm

2013 ◽  
Vol 336-338 ◽  
pp. 968-972 ◽  
Author(s):  
Huan Wang ◽  
Yu Lian Jiang
Keyword(s):  
Path Planning ◽  
Complex Environment ◽  
A Algorithm ◽  
Global Path Planning ◽  
Local Path Planning ◽  
Search Data ◽  
Infinite Loop ◽  
Transition Points ◽  
Local Path ◽  
Divide And Rule

Applying the global path planning to traditional A* algorithm in a complex environment and a lot of obstacles will result in an infinite loop because there are too many search data. To resolve this problem, this paper provides a new divide-and-rule path planning method which is based on improved A* algorithm. It uses several transition points to divide the entire grid map areas into several sub-regions. We set different speeds in each sub-region for local path planning. Thus the complex global path planning is turned into some simple local path planning. It reduces the search data of A* algorithm and avoids falling into the infinite loop. By this method, this paper designs the path planning of heading the ball, and smoothes the orbit. The simulation results show that the improved A* algorithm is better and more effective than the traditional one.

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

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Guoqing Xia ◽  
Zhiwei Han ◽  
Bo Zhao ◽  
Xinwei Wang
Keyword(s):  
Path Planning ◽  
Collision Avoidance ◽  
Optimization Problem ◽  
Quantum Particle ◽  
Swarm Optimization ◽  
Unmanned Surface Vehicle ◽  
Kinematic Constraints ◽  
Quantum Particle Swarm Optimization ◽  
Local Path Planning ◽  
Local Path

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.

Integrated local path planning method

2007 ◽  
Author(s):  
HwangRyol Ryu ◽  
KiSung You ◽  
ChinTae Choi
Keyword(s):  
Path Planning ◽  
Planning Method ◽  
Local Path Planning ◽  
Local Path

Local Path Planning for Dual-Arm Mobile Robot Shuttling within Truss

2012 ◽  
Vol 452-453 ◽  
pp. 1220-1224
Author(s):  
Wei Guo Wu ◽  
Peng Wu
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Planning Method ◽  
Planning Stage ◽  
Probabilistic Roadmaps ◽  
Local Path Planning ◽  
Smooth Path ◽  
Local Path ◽  
Two Stages ◽  
Dual Arm

A new local path planning method for dual-arm mobile robot shuttling within the truss is presented. Like the probabilistic roadmaps method, this method proceeds in two stages: preprocessing stage and path planning stage. In preprocessing stage, the workspace is divided into a set of non-overlapping cubical cells. The nodes in the free workspace are stored in a matrix. In path planning stage, three query strategies are adopted to search the path from start point to goal point. Take use of vertex query strategy, the smooth path can be acquired in a fraction of a second. The algorithm is simple, and is applicable to any static environment with convex obstacles.

A real-time local path planning method in complex scenes

2021 ◽  
Author(s):  
Cai Wenlin ◽  
Zihui Zhu ◽  
Jianhua Li ◽  
Jiaqi Liu ◽  
Chunxi Wang
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Planning Method ◽  
Local Path Planning ◽  
Complex Scenes ◽  
Local Path

scholarly journals Rollover-Free Path Planning for Off-Road Autonomous Driving

Electronics ◽  
2019 ◽  
Vol 8 (6) ◽  
pp. 614
Author(s):  
Xingyu Li ◽  
Bo Tang ◽  
John Ball ◽  
Matthew Doude ◽  
Daniel W. Carruth
Keyword(s):  
Path Planning ◽  
Free Path ◽  
Autonomous Driving ◽  
Planning Method ◽  
Local Planning ◽  
Planning Stage ◽  
Local Path Planning ◽  
Global Route ◽  
Local Path ◽  
Global Planning

Perception, planning, and control are three enabling technologies to achieve autonomy in autonomous driving. In particular, planning provides vehicles with a safe and collision-free path towards their destinations, accounting for vehicle dynamics, maneuvering capabilities in the presence of obstacles, traffic rules, and road boundaries. Existing path planning algorithms can be divided into two stages: global planning and local planning. In the global planning stage, global routes and the vehicle states are determined from a digital map and the localization system. In the local planning stage, a local path can be achieved based on a global route and surrounding information obtained from sensors such as cameras and LiDARs. In this paper, we present a new local path planning method, which incorporates a vehicle’s time-to-rollover model for off-road autonomous driving on different road profiles for a given predefined global route. The proposed local path planning algorithm uses a 3D occupancy grid and generates a series of 3D path candidates in the s-p coordinate system. The optimal path is then selected considering the total cost of safety, including obstacle avoidance, vehicle rollover prevention, and comfortability in terms of path smoothness and continuity with road unevenness. The simulation results demonstrate the effectiveness of the proposed path planning method for various types of roads, indicating its wide practical applications to off-road autonomous driving.

scholarly journals Path planning for coal mine robot to avoid obstacle in gas distribution area

2018 ◽  
Vol 15 (1) ◽  
pp. 172988141775150 ◽  
Author(s):  
Xiliang Ma ◽  
Ruiqing Mao
Keyword(s):  
Path Planning ◽  
Coal Mine ◽  
Ant Colony Algorithm ◽  
Dynamic Environment ◽  
Planning Method ◽  
Distribution Area ◽  
Dijkstra Algorithm ◽  
Safety Level ◽  
Planning Methods ◽  
Local Path

As the explosion-proof safety level of coal mine robot has not yet reached the level of intrinsic safety “ia,” therefore, path planning methods for coal mine robot to avoid the dangerous area of gas are necessary. To avoid a secondary explosion when the coal mine robot passes through gas hazard zones, a path planning method is proposed, considering the gas concentration distributions. The path planning method is composed of two steps in total: the global path planning and the local path adjustment. First, the global working path for coal mine robot is planed based on the Dijkstra algorithm and the ant colony algorithm. Second, with consideration of the dynamic environment, when hazardous gas areas distribute over the planed working path again, local path adjustments are carried out with the help of a proposed local path adjustment method. Lastly, experiments are conducted in a roadway after accident, which verify the effectiveness of the proposed path planning method.

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