Robotic path planning using multi neuron heuristic search

Authors:
Rahul Kala;Anupam Shukla;Ritu Tiwari
Affiliations:
MTech, Indian Institute of Information Technology and Management Gwalior, Gwalior, India;Indian Institute of Information Technology and Management Gwalior, Gwalior, India;Indian Institute of Information Technology and Management Gwalior, Gwalior, India
Venue:
Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human
Year:
2009

Citing 4
Cited 3

Navigating Mobile Robots: Systems and Techniques

Navigating Mobile Robots: Systems and Techniques
Artificial Intelligence

Artificial Intelligence
Hybrid Control for Autonomous Mobile Robot Navigation Using Neural Network Based Behavior Modules and Environment Classification

Autonomous Robots
Mobile Robot Navigation Control in Moving Obstacle Environment Using Genetic Algorithm, Artificial Neural Networks and A* Algorithm

CSIE '09 Proceedings of the 2009 WRI World Congress on Computer Science and Information Engineering - Volume 04

Multi robot exploration using a modified a algorithm

ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part I
Efficient strategy for co-ordinated multirobot exploration

Proceedings of the Second International Conference on Computational Science, Engineering and Information Technology
Multi-Level Planning for Semi-autonomous Vehicles in Traffic Scenarios Based on Separation Maximization

Journal of Intelligent and Robotic Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

Robotics is a highly multi-disciplinary field which attracts the attention of many researchers from diverse fields. One of the major studied problems in Robotics is the problem of Robotic Path Planning. The problem deals with finding of the path that can be used by the robot for navigation purpose without any collision. The output of this algorithm is then implemented for physically moving the real robot on the desired path. In this paper we have used Multi-Neuron Heuristic Search (MNHS) which is an advanced form of A* algorithm. The MNHS was earlier proposed by the authors for special cases where the heuristics changes sharply and it was shown to be a powerful algorithm in the same context. In this paper we apply the MNHS for the Robot Path Planning. The motivation is to make the problem robust against the uncertainties that might arise like the sudden discovery that the path being followed does not lead to the goal. The MNHS has better capabilities to solve maze-like maps where the uncertainty is extremely high. Another such area is when the robot enters into a highly chaotic area. Here it might be better to go with a path that is less chaotic or has lesser number of obstacles. We tested the algorithm for numerous test cases. In all the cases, the MNHS was able to solve the problem of path planning well.