Work place: Symbiosis Institute of Technology, Symbiosis International (Deemed University) Maharashtra, Pune 412115, India
Research Interests: Computational Science and Engineering, Computer Architecture and Organization, Data Structures and Algorithms
Himanshu Agrawal received Bachelor of Engineering in Electronics in 1994, Master of Technology in Computer Science in 2003 from Devi Ahilya University India and PhD, in Electrical and Computer Engineering, RMIT University, Melbourne, Victoria, Australia in 2010.
During 1995-1998, he had served as an Instrumentation Engineer in India. Since 1999, he is working as an academician. During past 16 years, he served in academics holding different positions in India and Australia. Currently associated with Symbiosis International University as an Associate Professor since August 2011, he had authored papers in peer-reviewed journals and International conferences. His research interest includes Internet routing, Internet of Things, intrusion detection and security issues in Smart grid and future Internet design issues. Dr Agrawal is a regular reviewer of Journal of Network and Computer Applications.
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.[...] Read more.
Subscribe to receive issue release notifications and newsletters from MECS Press journals