Expert system for Robotic Path Planning

Robotic planning to find the target our goal point/s is most important subject with the minimum distance and the fastest speed with obstacle avoidance expert system has been proposed. In this paper we try to compare and consider different scenario by taking two or more moving robot figure out the short path from the initial and the final point automatically through the map of many regular and irregular obstacles. Firstly, the adaptive fuzzy expert system is present where the fuzzy rule has been adaptive recursively through the robot moving, and then the potential field algorithm has been compared with the adaptive fuzzy system, the results demonstrated that the adaptive fuzzy is faster than the potential field but the accuracy moving of the potential field robotic path planning is much better. All the algorithms were failed when two robots moving from two different initial points to one final target point the why we have proposed particle swarm optimization (PSO) algorithm to solve such problem.

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

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.562-564.937 ◽

2012 ◽

Vol 562-564 ◽

pp. 937-940 ◽

Cited By ~ 2

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.

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Efficient robotic path planning algorithm based on artificial potential field

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v11i6.pp4840-4849 ◽

2021 ◽

Vol 11 (6) ◽

pp. 4840

Author(s):

Elia Nadira Sabudin ◽

Rosli Omar ◽

Sanjoy Kumar Debnath ◽

Muhammad Suhaimi Sulong

Keyword(s):

Computational Complexity ◽

Path Planning ◽

Potential Field ◽

Path Length ◽

Computation Time ◽

Cell Decomposition ◽

Target Point ◽

Planning Methods ◽

Planning Algorithm ◽

Path Planning Algorithm

<span lang="EN-US">Path planning is crucial for a robot to be able to reach a target point safely to accomplish a given mission. In path planning, three essential criteria have to be considered namely path length, computational complexity and completeness. Among established path planning methods are voronoi diagram (VD), cell decomposition (CD), probability roadmap (PRM), visibility graph (VG) and potential field (PF). The above-mentioned methods could not fulfill all three criteria simultaneously which limits their application in optimal and real-time path planning. This paper proposes a path PF-based planning algorithm called dynamic artificial PF (DAPF). The proposed algorithm is capable of eliminating the local minima that frequently occurs in the conventional PF while fulfilling the criterion of path planning. DAPF also integrates path pruning to shorten the planned path. In order to evaluate its performance, DAPF has been simulated and compared with VG in terms of path length and computational complexity. It is found that DAPF is consistent in generating paths with low computation time in obstacle-rich environments compared to VG. The paths produced also are nearly optimal with respect to VG.</span>

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An Optimized Path Planning for the Mobile Robot Using Potential Field Method and PSO Algorithm

Advances in Intelligent Systems and Computing - Soft Computing for Problem Solving ◽

10.1007/978-981-13-1595-4_11 ◽

2018 ◽

pp. 139-150 ◽

Cited By ~ 5

Author(s):

Ravi Kumar Mandava ◽

Sukesh Bondada ◽

Pandu R. Vundavilli

Keyword(s):

Path Planning ◽

Mobile Robot ◽

Potential Field ◽

Pso Algorithm ◽

Field Method ◽

Potential Field Method

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Simulation research for mobile robot path planning based on improved artificial potential field method recommended by the AsiaSim

International Journal of Modeling Simulation and Scientific Computing ◽

10.1142/s1793962317500465 ◽

2017 ◽

Vol 08 (02) ◽

pp. 1750046 ◽

Cited By ~ 3

Author(s):

Pengqi Hou ◽

Hu Pan ◽

Chen Guo

Keyword(s):

Path Planning ◽

Mobile Robot ◽

Potential Field ◽

Field Method ◽

Repulsive Force ◽

Artificial Potential Field ◽

Target Point ◽

Robot Path Planning ◽

Simulation Research ◽

Robot Path

Mobile robot path planning is an important research branch in the field of mobile robots. The main disadvantage of the traditional artificial potential field (APF) method is prone to local minima problems. Improved artificial potential field (IAPF) method is presented in this paper to solve the problem in the traditional APF method for robot path planning in different conditions. We introduce the distance between the robot and the target point to the function of the original repulsive force field and change the original direction of the repulsive force to avoid the trap problem caused by the local minimum point. The IAPF method is suitable for mobile robot path planning in the complicated environment. Simulation and experiment results at the robot platform illustrated the superiority of the modified IAPF method.

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