Natural disasters are inescapable and result in severe damage, including the loss of irreplaceable human lives and financial damages. To address this, quadruped robots are increasingly being utilized in search and rescue (SAR) missions due to their enhanced maneuverability in navigating complex and unknown environments. These four-legged robots mimic traditional canines used in SAR, allowing them to traverse uneven surfaces, climb stairs, and search through rubble. Nonetheless, most advanced quadruped robots are exorbitantly priced, making them inaccessible to developing countries and research. This study aimed to evaluate a cost-effective option (DeepRobotics Lite 3) for SAR missions and identified key security vulnerabilities through penetration testing, since these vulnerabilities compromise effectiveness and hinder their usage in rescue missions. This paper also explored the integration of Decision Intelligence (DI) to enhance decision-making in AI-capable quadrupeds. This work focused on security concerns and aims to deliver a safe, secure, and affordable SAR quadruped. Results identified key security vulnerabilities, recommended corrections, and offered risk clarifications.

In this paper, we discuss the Scenario Function, proposed by Fumio Negoro, as a bug-free development method and evaluate its effectiveness in software safety and security. The Scenario Function helps reduce bugs related to variable sequencing by treating each variable as a set, and it also enhances software safety. In terms of software security, managing variable states with flags may enable the detection of unintended variable processing through inconsistencies in flag states. This approach allows for the correction of abnormal processing, ensuring proper execution can resume. Although the Scenario Function has been patented for its potential to eliminate latent bugs and neutralize computer viruses, its definitions have largely been conceptual, with no fully established academic procedures. In this paper, we define the identity formula for a complete Scenario Function by identifying its 12 component vectors. We also explore its contributions to software safety and security, as well as its potential role in decision AI (Naturally Decision Intelligence: NDI).