scholarly journals Path Planning for Autonomous Ships: A Hybrid Approach Based on Improved APF and Modified VO Methods

2021 ◽  
Vol 9 (7) ◽  
pp. 761
Author(s):  
Liang Zhang ◽  
Junmin Mou ◽  
Pengfei Chen ◽  
Mengxia Li
Keyword(s):  
Path Planning ◽  
Local Minimum ◽  
Hybrid Approach ◽  
Artificial Potential Field ◽  
Whole Process ◽  
International Regulations ◽  
Local Path ◽  
Velocity Obstacle ◽  
Global And Local

In this research, a hybrid approach for path planning of autonomous ships that generates both global and local paths, respectively, is proposed. The global path is obtained via an improved artificial potential field (APF) method, which makes up for the shortcoming that the typical APF method easily falls into a local minimum. A modified velocity obstacle (VO) method that incorporates the closest point of approach (CPA) model and the International Regulations for Preventing Collisions at Sea (COLREGS), based on the typical VO method, can be used to get the local path. The contribution of this research is two-fold: (1) improvement of the typical APF and VO methods, making up for previous shortcomings, and integrated COLREGS rules and good seamanship, making the paths obtained more in line with navigation practice; (2) the research included global and local path planning, considering both the safety and maneuverability of the ship in the process of avoiding collision, and studied the whole process of avoiding collision in a relatively entirely way. A case study was then conducted to test the proposed approach in different situations. The results indicate that the proposed approach can find both global and local paths to avoid the target ship.

scholarly journals Path planning for unmanned surface vehicle based on predictive artificial potential field

2020 ◽  
Vol 17 (2) ◽  
pp. 172988142091846
Author(s):  
Jia Song ◽  
Ce Hao ◽  
Jiangcheng Su
Keyword(s):  
Path Planning ◽  
Potential Field ◽  
High Speed ◽  
Local Minimum ◽  
Artificial Potential Field ◽  
Minimum Problem ◽  
Unmanned Surface Vehicle ◽  
Local Path ◽  
Save Energy ◽  
Global And Local

The path planning for high-speed unmanned surface vehicle raises more complicated requirements to reduce sailing time and save energy. In this article, a new predictive artificial potential field is proposed using time information and predictive potential to plan a smoother path. The principle of artificial potential field with vehicle dynamics and reachability in local minimum is studied. According to global and local path planning, the most state-of-the-art traditional artificial potential field and its drawback are analysed at first. Then we proposed predictive artificial potential field with three modifications: angle limit, velocity adjustment and predictive potential to improve the feasibility and flatness of the generated path. In addition, we compare the performance between traditional artificial potential field and predictive artificial potential field, where predictive artificial potential field successfully restricts the maximum turning angle, cuts short sailing time and intelligently avoids obstacle. From the simulation results, we also verify that predictive artificial potential field can solve concave local minimum problem and enhance the reachability in special scenario. Therefore, the more reasonable path generated by predictive artificial potential field reduces sailing time and helps conserve more energy for unmanned surface vehicle.

scholarly journals Improvements on the virtual obstacle method

2020 ◽  
Vol 17 (2) ◽  
pp. 172988142091176
Author(s):  
Yuan Zheng ◽  
Xueming Shao ◽  
Zheng Chen ◽  
Jing Zhang
Keyword(s):  
Path Planning ◽  
Potential Field ◽  
Local Minimum ◽  
Artificial Potential Field ◽  
Complex Environments ◽  
Severe Problem ◽  
Local Minimum Point ◽  
Switching Condition ◽  
Local Path ◽  
Virtual Obstacle

While the artificial potential field has been widely employed to design path planning algorithms, it is well-known that artificial potential field-based algorithms suffer a severe problem that a robot may sink into a local minimum point. To address such problems, a virtual obstacle method has been developed in the literature. However, a robot may be blocked by virtual obstacles generated during performing the virtual obstacle method if the environments are complex. In this article, an improved virtual obstacle method for local path planning is designed via proposing a new minimum criterion, a new switching condition, and a new exploration force. All the three new contributions allow to overcome the drawbacks of the artificial potential field-based algorithms and the virtual obstacle method. As a consequence, feasible collision-free paths can be found in complex environments, as illustrated by final numerical simulations.

scholarly journals Efficient Local Path Planning Algorithm Using Artificial Potential Field Supported by Augmented Reality

Energies ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6642
Author(s):  
Rafal Szczepanski ◽  
Artur Bereit ◽  
Tomasz Tarczewski
Keyword(s):  
Augmented Reality ◽  
Path Planning ◽  
Mobile Robot ◽  
Potential Field ◽  
Local Minimum ◽  
Artificial Potential Field ◽  
Local Path Planning ◽  
Planning Algorithm ◽  
Local Path ◽  
Path Planning Algorithm

Mobile robots in industry are commonly used in warehouses and factories. To achieve the highest production rate, requirements for path planning algorithms have caused researchers to pay significant attention to this problem. The Artificial Potential Field algorithm, which is a local path planning algorithm, has been previously modified to obtain higher smoothness of path, to solve the stagnation problem and to jump off the local minimum. The last itemized problem is taken into account in this paper—local minimum avoidance. Most of the modifications of Artificial Potential Field algorithms focus on a mechanism to jump off a local minimum when robots stagnate. From the efficiency point of view, the mobile robot should bypass the local minimum instead of jumping off it. This paper proposes a novel Artificial Potential Field supported by augmented reality to bypass the upcoming local minimum. The algorithm predicts the upcoming local minimum, and then the mobile robot’s perception is augmented to bypass it. The proposed method allows the generation of shorter paths compared with jumping-off techniques, due to lack of stagnation in a local minimum. This method was experimentally verified using a Husarion ROSbot 2.0 PRO mobile robot and Robot Operating System in a laboratory environment.

A joint global and local path planning optimization for UAV task scheduling towards crowd air monitoring

2021 ◽  
Vol 193 ◽  
pp. 107913
Author(s):  
Yuan Tang ◽  
Yiming Miao ◽  
Ahmed Barnawi ◽  
Bander Alzahrani ◽  
Reem Alotaibi ◽  
...  
Keyword(s):  
Path Planning ◽  
Task Scheduling ◽  
Air Monitoring ◽  
Local Path Planning ◽  
Planning Optimization ◽  
Local Path ◽  
Global And Local

scholarly journals Improved Artificial Potential Field and Dynamic Window Method for Amphibious Robot Fish Path Planning

2021 ◽  
Vol 11 (5) ◽  
pp. 2114
Author(s):  
Wenlin Yang ◽  
Peng Wu ◽  
Xiaoqi Zhou ◽  
Haoliang Lv ◽  
Xiaokai Liu ◽  
...  
Keyword(s):  
Path Planning ◽  
Potential Field ◽  
Local Minimum ◽  
Field Method ◽  
Artificial Potential Field ◽  
Evaluation Function ◽  
Potential Field Method ◽  
Robot Fish ◽  
Amphibious Robot ◽  
Artificial Potential Field Method

Aiming at the problems of “local minimum” and “unreachable target” existing in the traditional artificial potential field method in path planning, an improved artificial potential field method was proposed after analyzing the fundamental causes of the above problems. The method solved the problem of local minimum by modifying the direction and influence range of the gravitational field, increasing the virtual target and evaluation function, and the problem of unreachable targets is solved by increasing gravity. In view of the change of motion state of robot fish in amphibious environments, the improved artificial potential field method was fused with a dynamic window algorithm, and a dynamic window evaluation function of the optimal path was designed on the basis of establishing the dynamic equations of land and underwater. Then, the simulation experiment was designed under the environment of Matlab2019a. Firstly, the improved and traditional artificial potential field methods were compared. The results showed that the improved artificial potential field method could solve the above two problems well, shorten the operation time and path length, and have high efficiency. Secondly, the influence of different motion modes on path planning is verified, and the result also reflects that the amphibious robot can avoid obstacles flexibly and reach the target point accurately according to its own motion ability. This paper provides a new way of path planning for the amphibious robot.

A method for avoiding obstacles of a self-driving carin the dynamic environment based on model predictive control

2021 ◽  
Author(s):  
N.P. Demenkov ◽  
Kai Zou
Keyword(s):  
Path Planning ◽  
Model Predictive Control ◽  
Urban Environment ◽  
Predictive Control ◽  
Potential Field ◽  
Dynamic Environment ◽  
Field Method ◽  
Artificial Potential Field ◽  
Physical Constraints ◽  
Local Path

The paper discusses the problem of obstacle avoidance of a self-driving car in urban road conditions. The artificial potential field method is used to simulate traffic lanes and cars in a road environment. The characteristics of the urban environment, as well as the features and disadvantages of existing methods based on the structure of planning-tracking, are analyzed. A method of local path planning is developed, based on the idea of an artificial potential field and model predictive control in order to unify the process of path planning and tracking to effectively cope with the dynamic urban environment. The potential field functions are introduced into the path planning task as constraints. Based on model predictive control, a path planning controller is developed, combined with the physical constraints of the vehicle, to avoid obstacles and execute the expected commands from the top level as the target for the task. A joint simulation was performed using MATLAB and CarSim programs to test the feasibility of the proposed path planning method. The results show the effectiveness of the proposed method.

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