Evolutionary artificial potential fields and their application in real time robot path planning

2002 ◽  
Author(s):  
P. Vadakkepat ◽  
Kay Chen Tan ◽  
Wang Ming-Liang
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Potential Fields ◽  
Robot Path Planning ◽  
Artificial Potential Fields ◽  
Robot Path

A Hybrid Algorithm to Solute the Problem of the Robot Path Planning

2011 ◽  
Vol 383-390 ◽  
pp. 385-389
Author(s):  
Quan Bo Yuan ◽  
Hui Juan Wang ◽  
Peng Hua Zhu ◽  
Hui Zhao
Keyword(s):  
Genetic Algorithm ◽  
Path Planning ◽  
Hybrid Algorithm ◽  
Local Minimum ◽  
Potential Fields ◽  
Robot Path Planning ◽  
Robot Simulation ◽  
Artificial Potential Fields ◽  
Simulation Results ◽  
Robot Path

This paper analyzes the problems of artificial potential fields in robot path planning. A hybrid algorithm for robot path planning is proposed. Robot searching for path in artificial potential fields method, it is possible that the robot can’t reach the goal because of local minimum, when it is best to use a genetic algorithm for robot. Simulation results show the effectiveness of the models, and it can effectively solve the problem caused by defects of artificial potential fields in the path planning.

Real-time robot path planning from simple to complex obstacle patterns via transfer learning of options

2019 ◽  
Vol 43 (8) ◽  
pp. 2071-2093 ◽  
Author(s):  
Olimpiya Saha ◽  
Prithviraj Dasgupta ◽  
Bradley Woosley
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Transfer Learning ◽  
Robot Path Planning ◽  
Robot Path

Optimal Robot Path Planning with Cellular Neural Network

2013 ◽  
pp. 19-38
Author(s):  
Yongmin Zhong ◽  
Bijan Shirinzadeh ◽  
Xiaobu Yuan
Keyword(s):  
Neural Network ◽  
Path Planning ◽  
State Space ◽  
Real Time ◽  
Cellular Neural Network ◽  
Neural Dynamics ◽  
Robot Path Planning ◽  
Local Connectivity ◽  
Target Activity ◽  
Robot Path

This paper presents a new methodology based on neural dynamics for optimal robot path planning by drawing an analogy between cellular neural network (CNN) and path planning of mobile robots. The target activity is treated as an energy source injected into the neural system and is propagated through the local connectivity of cells in the state space by neural dynamics. By formulating the local connectivity of cells as the local interaction of harmonic functions, an improved CNN model is established to propagate the target activity within the state space in the manner of physical heat conduction, which guarantees that the target and obstacles remain at the peak and the bottom of the activity landscape of the neural network. The proposed methodology cannot only generate real-time, smooth, optimal, and collision-free paths without any prior knowledge of the dynamic environment, but it can also easily respond to the real-time changes in dynamic environments. Further, the proposed methodology is parameter-independent and has an appropriate physical meaning.

Optimal Robot Path Planning With Cellular Neural Network

2019 ◽  
pp. 491-511
Author(s):  
Yongmin Zhong ◽  
Bijan Shirinzadeh ◽  
Xiaobu Yuan
Keyword(s):  
Neural Network ◽  
Path Planning ◽  
State Space ◽  
Real Time ◽  
Cellular Neural Network ◽  
Neural Dynamics ◽  
Robot Path Planning ◽  
Local Connectivity ◽  
Target Activity ◽  
Robot Path

This paper presents a new methodology based on neural dynamics for optimal robot path planning by drawing an analogy between cellular neural network (CNN) and path planning of mobile robots. The target activity is treated as an energy source injected into the neural system and is propagated through the local connectivity of cells in the state space by neural dynamics. By formulating the local connectivity of cells as the local interaction of harmonic functions, an improved CNN model is established to propagate the target activity within the state space in the manner of physical heat conduction, which guarantees that the target and obstacles remain at the peak and the bottom of the activity landscape of the neural network. The proposed methodology cannot only generate real-time, smooth, optimal, and collision-free paths without any prior knowledge of the dynamic environment, but it can also easily respond to the real-time changes in dynamic environments. Further, the proposed methodology is parameter-independent and has an appropriate physical meaning.

scholarly journals Robot Path Planning using Dynamic Programming with Accelerating Nodes

2012 ◽  
Vol 3 (1) ◽  
Author(s):  
Rahul Kala ◽  
Anupam Shukla ◽  
Ritu Tiwari
Keyword(s):  
Dynamic Programming ◽  
Path Planning ◽  
Real Time ◽  
Dynamic Environments ◽  
Experimental Results ◽  
Robot Path Planning ◽  
Optimal Paths ◽  
Additional Processing ◽  
Update Frequency ◽  
Robot Path

AbstractWe solve the problem of robot path planning using Dynamic Programming (DP) designed to perform well in case of a sudden path blockage. A conventional DP algorithm works well for real time scenarios only when the update frequency is high i.e. changes can be readily propagated. In case updates are costly, for a sudden blockage the robot continues moving along the wrong path or stands stationary. We propose a modified DP that has nodes with additional processing (called accelerating nodes) to enable different segments of the map to become informed about the blockage rapidly. We further quickly compute an alternative path in case of a blockage. Experimental results verify that usage of accelerating nodes makes the robot follow optimal paths in dynamic environments.

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