scholarly journals A New Online Random Particles Optimization Algorithm for Mobile Robot Path Planning in Dynamic Environments

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Behrang Mohajer ◽  
Kourosh Kiani ◽  
Ehsan Samiei ◽  
Mostafa Sharifi
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Optimization Algorithm ◽  
Potential Field ◽  
Optimal Path ◽  
Target Position ◽  
Path Selection ◽  
Artificial Potential Field ◽  
Planning Problem ◽  
Robot Path

A new algorithm named random particle optimization algorithm (RPOA) for local path planning problem of mobile robots in dynamic and unknown environments is proposed. The new algorithm inspired from bacterial foraging technique is based on particles which are randomly distributed around a robot. These particles search the optimal path toward the target position while avoiding the moving obstacles by getting help from the robot’s sensors. The criterion of optimal path selection relies on the particles distance to target and Gaussian cost function assign to detected obstacles. Then, a high level decision making strategy will decide to select best mobile robot path among the proceeded particles, and finally a low level decision control provides a control signal for control of considered holonomic mobile robot. This process is implemented without requirement to tuning algorithm or complex calculation, and furthermore, it is independent from gradient base methods such as heuristic (artificial potential field) methods. Therefore, in this paper, the problem of local mobile path planning is free from getting stuck in local minima and is easy computed. To evaluate the proposed algorithm, some simulations in three various scenarios are performed and results are compared by the artificial potential field.

scholarly journals An Improved Path Planning Method Based on Artificial Potential Field for a Mobile Robot

2015 ◽  
Vol 15 (2) ◽  
pp. 181-191 ◽  
Author(s):  
Wenbai Chen ◽  
Xibao Wu ◽  
Yang Lu
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Potential Field ◽  
Field Method ◽  
Artificial Potential Field ◽  
Improved Method ◽  
Chaos Optimization ◽  
Potential Field Method ◽  
Artificial Potential Field Method ◽  
Robot Path

Abstract To solve the problem of local minima and unreachable destination of the traditional artificial potential field method in mobile robot path planning, chaos optimization is introduced to improve the artificial potential field method. The potential field function was adopted as a target function of chaos optimization, and a kind of “two-stage” chaos optimization was used. The corresponding movement step and direction of the robot were achieved by chaos search. Comparison of the improved method proposed in this paper and the traditional artificial potential field method is performed by simulation. The simulation results show that the improved method gets rid of the drawbacks, such as local minima and unreachable goal. Furthermore, the improved method is also verified by building up a physical platform based on “Future Star” robot. The success of the physical experiment indicates that the improved algorithm is feasible and efficient for mobile robot path planning.

scholarly journals Mobile Robot Path Planning Using Polyclonal-Based Artificial Immune Network

2013 ◽  
Vol 2013 ◽  
pp. 1-13
Author(s):  
Lixia Deng ◽  
Xin Ma ◽  
Jason Gu ◽  
Yibin Li
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Potential Field ◽  
Artificial Potential Field ◽  
Immune Network ◽  
Local Minima ◽  
Robot Path Planning ◽  
Artificial Immune ◽  
Artificial Immune Network ◽  
Robot Path

Polyclonal based artificial immune network (PC-AIN) is utilized for mobile robot path planning. Artificial immune network (AIN) has been widely used in optimizing the navigation path with the strong searching ability and learning ability. However, artificial immune network exists as a problem of immature convergence which some or all individuals tend to the same extreme value in the solution space. Thus, polyclonal-based artificial immune network algorithm is proposed to solve the problem of immature convergence in complex unknown static environment. Immunity polyclonal algorithm (IPCA) increases the diversity of antibodies which tend to the same extreme value and finally selects the antibody with highest concentration. Meanwhile, immunity polyclonal algorithm effectively solves the problem of local minima caused by artificial potential field during the structure of parameter in artificial immune network. Extensive experiments show that the proposed method not only solves immature convergence problem of artificial immune network but also overcomes local minima problem of artificial potential field. So, mobile robot can avoid obstacles, escape traps, and reach the goal with optimum path and faster convergence speed.

Vision Based Path Planning for Mobile Robot Using Extrapolated Artificial Potential Field and Probabilistic Obstacle Avoidance

2002 ◽  
Author(s):  
Waqar A. Malik ◽  
Jae-Yong Lee ◽  
Sooyong Lee
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Collision Detection ◽  
Potential Field ◽  
Artificial Potential Field ◽  
Robot Path Planning ◽  
Moving Obstacles ◽  
Novel Method ◽  
Robot Path

Mobile robots are increasingly being used to do tasks in unknown environment. The potential of robots to undertake such tasks lies on their ability to intelligently and efficiently locate and interact with objects in their environment. This paper describes a novel method to plan paths for mobile robots in a partially known environment observed by an overhead camera. The environment consists of dynamic obstacles and targets. A new methodology, Extrapolated Artificial Potential Field is proposed for real time robot path planning. The proposed Extrapolated Artificial Potential Field is capable of navigating robots situated among moving obstacles and target. An algorithm for probabilistic collision detection is introduced. The paper summarizes this approach, and discusses the results of path planning experiments using an Amigobot. The result shows that our method is effective.

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