COLREGS-Constrained Real-time Path Planning for Autonomous Ships Using Modified Artificial Potential Fields

2018 ◽  
Vol 72 (3) ◽  
pp. 588-608 ◽  
Author(s):  
Hongguang Lyu ◽  
Yong Yin
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Potential Field ◽  
Human Interaction ◽  
Navigation Systems ◽  
Field Function ◽  
Emergency Situations ◽  
Virtual Forces ◽  
International Regulations ◽  
Stationary Obstacles

This paper presents a real-time and deterministic path planning method for autonomous ships or Unmanned Surface Vehicles (USV) in complex and dynamic navigation environments. A modified Artificial Potential Field (APF), which contains a new modified repulsion potential field function and the corresponding virtual forces, is developed to address the issue of Collision Avoidance (CA) with dynamic targets and static obstacles, including emergency situations. Appropriate functional and safety requirements are added in the corresponding virtual forces to ensure International Regulations for Preventing Collisions at Sea (COLREGS)-constrained behaviour for the own ship's CA actions. Simulations show that the method is fast, effective and deterministic for path planning in complex situations with multiple moving target ships and stationary obstacles and can account for the unpredictable strategies of other ships. The authors believe that automatic navigation systems operated without human interaction could benefit from the development of path planning algorithms.

scholarly journals Improved Artificial Potential Field Method Applied for AUV Path Planning

2020 ◽  
Vol 2020 ◽  
pp. 1-21 ◽  
Author(s):  
Xiaojing Fan ◽  
Yinjing Guo ◽  
Hui Liu ◽  
Bowen Wei ◽  
Wenhong Lyu
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Potential Field ◽  
Moving Objects ◽  
Dynamic Environment ◽  
Planning Method ◽  
Artificial Potential Field ◽  
Local Minima ◽  
Field Function ◽  
Time Path

With the topics related to the intelligent AUV, control and navigation have become one of the key researching fields. This paper presents a concise and reliable path planning method for AUV based on the improved APF method. AUV can make the decision on obstacle avoidance in terms of the state of itself and the motion of obstacles. The artificial potential field (APF) method has been widely applied in static real-time path planning. In this study, we present the improved APF method to solve some inherent shortcomings, such as the local minima and the inaccessibility of the target. A distance correction factor is added to the repulsive potential field function to solve the GNRON problem. The regular hexagon-guided method is proposed to improve the local minima problem. Meanwhile, the relative velocity method about the moving objects detection and avoidance is proposed for the dynamic environment. This method considers not only the spatial location but also the magnitude and direction of the velocity of the moving objects, which can avoid dynamic obstacles in time. So the proposed path planning method is suitable for both static and dynamic environments. The virtual environment has been built, and the emulation has been in progress in MATLAB. Simulation results show that the proposed method has promising feasibility and efficiency in the AUV real-time path planning. We demonstrate the performance of the proposed method in the real environment. Experimental results show that the proposed method is capable of avoiding the obstacles efficiently and finding an optimized path.

Multi-Robots Path Planning Based on Improved Artificial Potential Field Method

2012 ◽  
Vol 562-564 ◽  
pp. 937-940 ◽  
Author(s):  
Yu Lan Hu ◽  
Qi Song Zhang
Keyword(s):  
Gravitational Field ◽  
Path Planning ◽  
Potential Field ◽  
Field Method ◽  
Artificial Potential Field ◽  
Target Point ◽  
Field Function ◽  
Dynamic Movement ◽  
Potential Field Method ◽  
Artificial Potential Field Method

Mobile path planning is a focus area and the key to intelligent technologies in robot. As one of the most basic and important topics the problem of mobile robot path planning solve the trouble that the robot avoid obstacles in the environment and how to successfully reach the destination. On the emergence of case that is the robot can not reach the target point and easy to fall into local minimum .This will be optimized by improving the way repulsive field function, When the robot close to the target point, not only the gravity of the gravitational field continue to reduce but also the repulsion of the repulsive force field has also been decreasing. This would solve the problem that when the robot reach the target point but easy to fall into local minimal solution. In traditional artificial potential field method, the target is static, but due to prey (i.e. target) is dynamic in this article, the traditional artificial potential field of gravitational field function is not suitable for the situation discussed. Therefore this paper puts forward a dynamic movement is based on the goal of the gravitational field of new functions.

scholarly journals 3D Path Planning Method for UAV Based on Improved Artificial Potential Field

2020 ◽  
Vol 38 (5) ◽  
pp. 977-986
Author(s):  
Yicong Guo ◽  
Xiaoxiong Liu ◽  
Weiguo Zhang ◽  
Yue Yang
Keyword(s):  
Path Planning ◽  
Optimization Algorithm ◽  
Potential Field ◽  
Local Minimum ◽  
Field Method ◽  
Artificial Potential Field ◽  
Field Calculation ◽  
Field Function ◽  
Potential Field Method ◽  
Artificial Potential Field Method

Path planning is the key technology for UAV to achieve autonomous flight. Considering the shortcomings of path planning based on the conventional potential field method, this paper proposes an improved optimization algorithm based on the artificial potential field method and extends it to three-dimensional space to better achieve flight constrained 3D online path planning for UAVs. The algorithm is improved and optimized aiming at the three problems of goal nonreachable with obstacle nearby (GNWON), easy to fall into local minimum, and path oscillation in traditional artificial potential field method. First, an improved potential field function with relative distance is used to solve the GNWON, and an optimized repulsive potential field calculation method based on different obstacles or threat models is proposed to optimize the planned path. Secondly, in order to make the drone jump out of the local minimum trap, a method of setting heuristic sub-target points is proposed. For local path oscillation, a method using memory sum force was proposed to improve the oscillation. The simulation results show that the improved optimization algorithm in this paper effectively makes up for the shortcomings of the traditional artificial potential field method, and the designed 3D online path planning algorithm for the UAV is practical and feasible.

scholarly journals A Path Planning Algorithm for a Dynamic Environment Based on Proper Generalized Decomposition

Mathematics ◽  
2020 ◽  
Vol 8 (12) ◽  
pp. 2245
Author(s):  
Antonio Falcó ◽  
Lucía Hilario ◽  
Nicolás Montés ◽  
Marta C. Mora ◽  
Enrique Nadal
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Real Time ◽  
Potential Field ◽  
Dynamic Environment ◽  
Artificial Potential Field ◽  
Proper Generalized Decomposition ◽  
Partial Differential ◽  
Planning Algorithm ◽  
Path Planning Algorithm

A necessity in the design of a path planning algorithm is to account for the environment. If the movement of the mobile robot is through a dynamic environment, the algorithm needs to include the main constraint: real-time collision avoidance. This kind of problem has been studied by different researchers suggesting different techniques to solve the problem of how to design a trajectory of a mobile robot avoiding collisions with dynamic obstacles. One of these algorithms is the artificial potential field (APF), proposed by O. Khatib in 1986, where a set of an artificial potential field is generated to attract the mobile robot to the goal and to repel the obstacles. This is one of the best options to obtain the trajectory of a mobile robot in real-time (RT). However, the main disadvantage is the presence of deadlocks. The mobile robot can be trapped in one of the local minima. In 1988, J.F. Canny suggested an alternative solution using harmonic functions satisfying the Laplace partial differential equation. When this article appeared, it was nearly impossible to apply this algorithm to RT applications. Years later a novel technique called proper generalized decomposition (PGD) appeared to solve partial differential equations, including parameters, the main appeal being that the solution is obtained once in life, including all the possible parameters. Our previous work, published in 2018, was the first approach to study the possibility of applying the PGD to designing a path planning alternative to the algorithms that nowadays exist. The target of this work is to improve our first approach while including dynamic obstacles as extra parameters.

Grid Based Path Planning Using CNN & Artificial Potential Field Method

2013 ◽  
Vol 392 ◽  
pp. 830-836 ◽  
Author(s):  
Shamina Akter ◽  
Deok Jin Lee ◽  
Shin Taek Lim ◽  
Kil To Chong
Keyword(s):  
Image Processing ◽  
Path Planning ◽  
Potential Field ◽  
Planning Process ◽  
Vision System ◽  
Field Method ◽  
Planning Method ◽  
Artificial Potential Field ◽  
Field Function ◽  
Potential Value

This proposed path planning method combines cellular neural network (CNN) with artificial potential field approach. The fundamental operation based on CNN gray scale image processing and artificial potential is the additional approach for global path-planning. Every point of the environment has a potential value with respect to start and destination position. In the trajectory planning process, a minimum search of potential value of every surrounding neighbor points around a point is done and the neighbor point with the least minimum value is selected as the next location. This procedure is repeated until the goal point is reached. The advantage of using CNN based image processing with artificial potential field function in a vision system is its effectiveness in robot localization while the use of minimum potential value gives a simple yet efficient path planning method. Their feedback criterion is similar to a procedure in filtering the image and it frequently updates the information about obstacles and free path. The parallel processing properties of CNN makes the proposed method robust for real time application.

Predictive Artificial Potential Field Method and its Application on Robot Soccer Path Planning

2011 ◽  
Vol 48-49 ◽  
pp. 840-843 ◽  
Author(s):  
Peng Huang ◽  
Chang Yun Miao ◽  
Li Jin Guo ◽  
Ying Li
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Potential Field ◽  
Field Method ◽  
Artificial Potential Field ◽  
Robot Soccer ◽  
Uncertain Environment ◽  
Field Approach ◽  
Potential Field Method ◽  
Artificial Potential Field Method

This paper presents a new predictive artificial potential field approach for robot soccer path planning under complex and uncertain environment. By predicting and analyzing the future position and attitude of concerned object, the position and attitude of the object is controlled by demonstration algorithm. The proposed method is successfully used in the robor soccer shooting and is realized on the MiroSot 3vs3 simulating platform. Experiment results show that this algorithm has good real-time ability and adaptability to environment.

A Novel Approach for Path Planning Based on Reactive Behavior-Artificial Potential Filed

2014 ◽  
Vol 529 ◽  
pp. 646-649 ◽  
Author(s):  
Long Xiang Yang ◽  
Zai Xin Liu ◽  
Hao Tang
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Real Time ◽  
Potential Field ◽  
Artificial Potential Field ◽  
Optimal Selection ◽  
Local Minima ◽  
Novel Approach ◽  
Reactive Behavior ◽  
Real Time Application

The main objective of this paper is to focus on the local minima and the GNRON issues encountered in path planning by the artificial potential field (APF) method, and propose a novel approach to plan a trajectory adaptive for the environment that the obstacles are randomly distributed. By introducing the ideas of reactive behaviors (RB), the RB-APF method is presented, which combines the efficiency of the APF with the simplicity of the RB, so it can be suitable for real-time application in mobile robots. In this algorithm (RB-APF), the switch conditions and optimal selection equations are reasonably designed with the consideration of the different circumstances of the robot located in. Simulations are presented and the results further demonstrate that the proposed approach is applicable for the environment that obstacles are randomly distributed.

Motion planning approach for car-like robots in unstructured scenario

2021 ◽  
pp. 014233122199439
Author(s):  
Xuehao Sun ◽  
Shuchao Deng ◽  
Tingting Zhao ◽  
Baohong Tong
Keyword(s):  
Motion Planning ◽  
Real Time ◽  
Obstacle Avoidance ◽  
Potential Field ◽  
Optimal Path ◽  
Conversion Function ◽  
Elastic Band ◽  
Emergency Situations ◽  
Time Motion ◽  
Planning Approach

When a car-like robot travels in an unstructured scenario, real-time motion planning encounters the problem of unstable motion state in obstacle avoidance planning. This paper presents a hybrid motion planning approach based on the timed-elastic-band (TEB) approach and artificial potential field. Different potential fields in an unstructured scenario are established, and the real-time velocity of the car-like robot is planned by using the conversion function of the virtual potential energy of the superimposed potential field and the virtual kinetic energy of the robot. The optimized TEB approach plans the local optimal path and solves the problems related to the local minimum region and non-reachable targets. The safety area of the dynamic obstacle is constructed to realize turning or emergency stop obstacle avoidance, thereby effectively ensuring the safety of the car-like robot in emergency situations. The simulation experiments show that the proposed approach has superior kinematic characteristics and satisfactory obstacle avoidance planning effects and can improve the motion comfort and safety of the car-like robot. In the practical test, the car-like robot moves stably in a dynamic scenario, and the proposed approach satisfies the actual application requirements.

Sign in / Sign up

Export Citation Format

Share Document