The obstacle avoidance planning of USV based on improved artificial potential field

Xie, Shaorong; Wu, Peng; Peng, Yan; Luo, Jun; Qu, Dong; Li, Qingmei

doi:10.1109/icinfa.2014.6932751

Cited by 35 publications

(49 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…is the shortest distance be-tween the robot and obstacles in the planar space, and G 0 represents a safe distance from the obstacles [24]. According to the kinetic theory, the relation G 0 ≥ V MAX / 2A MAX is used, where V MAX denotes the maximum speed of the robot and A MAX represents the maximum speed of the acceleration (negative acceleration).…”

Section: Methodsmentioning

confidence: 99%

“…Then, both the repulsion components in the direction of the Xaxis and the Yaxis can be obtained. Given θ is the angle between the Xaxis and the line from the point of the robot to the target, the attraction components in the Xaxis and the Yaxis are considered as the following equations [24]:…”

Section: Methodsmentioning

confidence: 99%

“…The two components of repulsion and attraction in the direction of the x-axis and the y-axis can be obtained as follows [24]:…”

Section: Modified Artificial Potential Field Modelmentioning

confidence: 99%

See 2 more Smart Citations

Obstacle avoidance of mobile robots using modified artificial potential field algorithm

Rostami

Sangaiah

Wang

et al. 2019

J Wireless Com Network

171

View full text Add to dashboard Cite

In recent years, topics related to robotics have become one of the researching fields. In the meantime, intelligent mobile robots have great acceptance, but the control and navigation of these devices are very difficult, and the lack of dealing with fixed obstacles and avoiding them, due to safe and secure routing, is the basic requirement of these systems. In this paper, the modified artificial potential field (APF) method is proposed for that robot avoids collision with fixed obstacles and reaches the target in an optimal path; using this algorithm, the robot can run to the target in optimal environments without any problems by avoiding obstacles, and also using this algorithm, unlike the APF algorithm, the robot does not get stuck in the local minimum. We are looking for an appropriate cost function, with restrictions that we have, and the goal is to avoid obstacles, achieve the target, and do not stop the robot in local minimum. The previous method, APF algorithm, has advantages, such as the use of a simple math model, which is easy to understand and implement. However, this algorithm has many drawbacks; the major drawback of this problem is at the local minimum and the inaccessibility of the target when the obstacles are in the vicinity of the target. Therefore, in order to obtain a better result and to improve the shortcomings of the APF algorithm, this algorithm needs to be improved. Here, the obstacle avoidance planning algorithm is proposed based on the improvement of the artificial potential field algorithm to solve this local minimum problem. In the end, simulation results are evaluated using MATLAB software. The simulation results show that the proposed method is superior to the existing solution.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Obstacle avoidance of mobile robots using modified artificial potential field algorithm

Rostami

Sangaiah

Wang

et al. 2019

J Wireless Com Network

171

View full text Add to dashboard Cite

show abstract

“…An optimal path planning method has been shown to generate a feasible path using a constrained A* algorithm for a USV in a confined maritime environment, where dynamic obstacles are a concern (Singh et al, 2019). Several other methods have been used for path planning for marine vessels, including artificial potential field (Xie et al, 2014), fast marching (FM) (Liu and Bucknall, 2015), real-time R* (RTR*), and partitioned learning real-time A* (PLRTA*) (Cannon et al, 2012). A modified A* algorithm is applied in this study using a grid map and heuristic cost.…”

Section: Introductionmentioning

confidence: 99%

Modified A* Algorithm for Obstacle Avoidance for Unmanned Surface Vehicle

Yoon

Ryu

et al. 2019

J. Ocean Eng. Technol.

View full text Add to dashboard Cite

Efficient path planning is essential for unmanned surface vehicle (USV) navigation. The A* algorithm is an effective algorithm for identifying a safe path with optimal distance cost. In this study, a modified version of the A* algorithm is applied for planning the path of a USV in a static and dynamic obstacle environment. The current study adopts the A* approach while maintaining a safe distance between the USV and obstacles. Two important parameters-path length and computational time-are considered at various start times. The results demonstrate that the modified approach is effective for obstacle avoidance by a USV that is compliant with the International Regulations for Preventing Collision at Sea (COLREGs).This is an open access article distributed under the terms of the creative commons attribution non-commercial license (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

show abstract

“…The grid-based world model should be updated as the sensor data change. Xie et al [7] proposed a method of the obstacle avoidance planning of USV based on improved artificial potential field; this method overcomes the problems of local minimum, destination unreachable, and poor accuracy of algorithm by using the traditional artificial potential field. Larson et al [8], Bandyophadyay et al [9], and Krishnamurthy et al [10] used behaviour-based reactive obstacle avoidance approaches to deal with the local obstacle avoidance for USV.…”

Section: State Of the Artmentioning

confidence: 99%

Simulation on Local Obstacle Avoidance Algorithm for Unmanned Surface Vehicle

Wang¹,

Mao²,

Du³

2016

Int. j. simul. model.

View full text Add to dashboard Cite

Unmanned surface vehicle (USV) is an important autonomous marine vehicle. The safe navigation of USV is directly determined by the local obstacle avoidance because it must avoid real-time local obstacle in the global path planning in a three-dimensional environment. Therefore, efficient algorithms of real-time local obstacle avoidance for USV are a critical issue. In this study, a new threelayered architecture for local obstacle avoidance algorithm was proposed to solve the local obstacle avoidance problem. First, real environment and obstacle models were established in the polar coordinate. The known static path-planning method was conducted based on particle swarm optimization (PSO). Second, the method was integrated with marine rules based on PSO. Third, an obstacle avoidance method under unknown environment was created based on rolling windows. Finally, a simulation experimental platform was developed to verify the feasibility and effectiveness of the aforementioned measure. Result shows that the proposed algorithm can effectively avoid local obstacles of USV at a computation time of less than 2 s. The USV avoids the obstacles smoothly and reaches the desired destination with complex requirements. The simulation results also demonstrate the promising application of the proposed method in studying the path planning of USV. This method can address the issues of real-time local obstacle avoidance of USV.

show abstract

The obstacle avoidance planning of USV based on improved artificial potential field

Cited by 35 publications

References 29 publications

Obstacle avoidance of mobile robots using modified artificial potential field algorithm

Obstacle avoidance of mobile robots using modified artificial potential field algorithm

Modified A* Algorithm for Obstacle Avoidance for Unmanned Surface Vehicle

Simulation on Local Obstacle Avoidance Algorithm for Unmanned Surface Vehicle

Contact Info

Product

Resources

About