Computational Robotics: An Alternative Approach for Predicting Terrorist Networks

Abstract

Increasing terrorist activities globally have attracted the attention of many researchers, policy makers and security agencies towards counterterrorism. The clandestine nature of terrorist networks have made them difficult for detection. Existing works have failed to explore computational characterization to design an efficient threat-mining surveillance system. In this paper, a computationally-aware surveillance robot that auto-generates threat information, and transmit same to the cloud-analytics engine is developed. The system offers hidden intelligence to security agencies without any form of interception by terrorist elements. A miniaturized surveillance robot with Hidden Markov Model (MSRHMM) for terrorist computational dissection is then derived. Also, the computational framework for MERHMM is discussed while showing the adjacency matrix of terrorist network as a determinant factor for its operation. The model indicates that the terrorist network have a property of symmetric adjacency matrix while the social network have both asymmetric and symmetric adjacency matrix. Similarly, the characteristic determinant of adjacency matrix as an important operator for terrorist network is computed to be -1 while that of a symmetric and an asymmetric in social network is 0 and 1 respectively. In conclusion, it was observed that the unique properties of terrorist networks such as symmetric and idempotent property conferred a special protection for the terrorist network resilience. Computational robotics is shown to have the capability of utilizing the hidden intelligence in attack prediction of terrorist elements. This concept is expected to contribute in national security challenges, defense and military intelligence.

Opposition-Based Learning Equilibrium Optimizer with Application in Mobile Robot Path Planning

“…In Eq. ( 3), C eq (1) , C eq (2) , C eq (3) , and C eq(4) represent the four best particles identified thus far, with C eq(ave) denoting their average. The utilization of these top four particles contributes to enhanced exploration capabilities for the EO process, while the incorporation of their average values fosters exploitation.…”

“…Path planning constitutes a vital component of automated mobile robot systems, tasked with generating feasible, safe, and smooth routes from starting to destination points within known or unknown environments. Intelligent path planning serves as an indispensable tool across various domains, including robot path planning, unmanned combat vehicles (UCVs), vehicle routing problems (VRPs), transportation system navigation, military command systems, cruise missile trajectory planning, unmanned aerial vehicle (UAV) trajectory planning, fire escape, and automated guided vehicles (AGVs) [2]. Given their extensive use, MRPP problems have garnered significant attention from researchers, leading to the application of numerous optimization algorithms to address the challenges.…”