A multiple constraints modified artificial potential field algorithm for complex dynamic environment

2019 ◽  
Author(s):  
Yongdan Li ◽  
Chaobo Chen ◽  
Tianli Ma ◽  
Hongli Wei ◽  
Qiongnan Yang ◽  
...  
Keyword(s):  
Potential Field ◽  
Dynamic Environment ◽  
Artificial Potential Field ◽  
Multiple Constraints ◽  
Complex Dynamic

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.

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.

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.

scholarly journals Artificial Potential Field Algorithm for Obstacle Avoidance in UAV Quadrotor for Dynamic Environment

2021 ◽  
Author(s):  
Alfian Ma'Arif ◽  
Wahyu Rahmaniar ◽  
Marco Antonio Marquez Vera ◽  
Aninditya Anggari Nuryono ◽  
Rania Majdoubi ◽  
...  
Keyword(s):  
Obstacle Avoidance ◽  
Potential Field ◽  
Dynamic Environment ◽  
Artificial Potential Field

Collision avoidance method of autonomous vehicle based on improved artificial potential field algorithm

2021 ◽  
pp. 095440702110143
Author(s):  
Song Feng ◽  
Yubin Qian ◽  
Yan Wang
Keyword(s):  
Collision Avoidance ◽  
Potential Field ◽  
Autonomous Vehicle ◽  
Dynamic Environment ◽  
Artificial Potential Field ◽  
Fuzzy Pid Control ◽  
Emergency Braking ◽  
Safety Distance ◽  
On The Road ◽  
Safe Path

Both emergency braking and active steering are possible choices for collision avoidance manoeuvres, and any obstacle avoidance strategy aims to design a control algorithm preventing accidents. However, the real-time path needs to consider the motion state of surrounding participants on the road. This work presents a collision avoidance algorithm containing the path-planning and the tracking controller. Firstly, the lateral lane-changing spacing model and the longitudinal braking distance model are presented, describing the vehicle to reactively process dynamic scenarios in real environments. Then, we introduce the safety distance into the artificial potential field algorithm (APF), thereby generating a safe path in a simulated traffic scene. Redesigning the influence range of obstacles based on the collision areas and corresponding safety distance compared with the classic APF. Besides, based on the threat level, the repulsion is divided into the force of the position repulsion and the speed repulsion. The former is related to the relative position and prevents the vehicle from approaching the obstacle. The latter is opposite to the relative speed vector and decelerates the ego vehicle. Simultaneously, the attraction is improved to apply a dynamic environment. Finally, we design a model predictive control (MPC) to track the lateral motion through steering angle and a Fuzzy-PID control to track the longitudinal speed, turning the planned path into an actual trajectory with stable vehicle dynamics. To verify the performance of the proposed method, three cases are simulated to obtain the vehicle responding curves. The simulation results prove that the active collision avoidance algorithm can generate a safe path with comfort and stability.

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