Poorva Agrawal

Work place: Symbiosis Institute of Technology, Symbiosis International (Deemed University) Maharashtra, Pune 412115, India

E-mail: poorva.agrawal@sitpune.edu.in


Research Interests: Computer Science & Information Technology, Computational Science and Engineering, Computer systems and computational processes, Theoretical Computer Science


Poorva Agrawal was born in Pipariya, Madhya Pradesh State, India in 1987. She received her B.E degree in Computer Science from Rajiv Gandhi University, Bhopal in the year 2009, Masters in Computer Science Engineering from SGSITS, Indore in 2012 and currently pursuing her PhD in Computer Science from Symbiosis International University.

Author Articles
Adaptive Algorithm Design for Cooperative Hunting in Multi-Robots

By Poorva Agrawal Himanshu Agrawal

DOI: https://doi.org/10.5815/ijisa.2018.12.05, Pub. Date: 8 Dec. 2018

The multi-robot cooperative planning is gained significant attention in recent past mainly for the evaders hunting task. In evaders hunting, the robot nodes required to recognize their other team members and considering their current positions and capabilities to catch the stationary or moving evaders effectively through the cooperating path planning approach. The primary challenge to design cooperative multi-robot evader hunting system is efficient and adaptive coordination of multiple autonomous mobile robots with less delay and communication overhead in presence of big-size obstacles. The current solutions suffered from repeated hunting problem under the inaccessible network conditions due to the presence of big-size obstacles and ineffective utilization of known nodes information. In this paper, to alleviate the problem of repeated hunting and inefficient catching of all evaders in the network, we proposed the adaptive Bio-inspired Neural Network (ABNN) using the new shunting equation with the capability of adaptive hunting of all evaders in the system. We design ABNN based on the implicit robot to predict the next path to catch evaders efficiently by real robots. The use of implicit robot helps to prevent the big sized evaders and efficiently utilize the evader’s information. The simulation results demonstrate that ABNN performs efficient evaders hunting under the presence of big size obstacles.

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