Research on the Design of Police Robot Dog Utility Enhancement Based on STS Theory

Abstract

This study focuses on the utility dilemma of police robotic dogs in real-world applications, and systematically analyzes the synergistic imbalance between their technical and social subsystems based on the Science, Technology and Society (STS) theory. By integrating the feedback from police users and the STS theoretical framework, it reveals that there are core contradictions between the functional design of the current police robotic dog and the police demand, weak deterrence and arrest ability, and lagging system adaptation. The study proposes a two-way optimization scheme for technology and society: at the technical level, the modular hardware design, multimodal sensing system upgrade and deep reinforcement learning decision engine development improve the robotic arm’s arrest accuracy (by 37%) and the stability of complex terrain movement (by 42%); at the social level, it builds a hierarchical specification for the use of force, and a human-machine collaborative tactical system, which reduces the risk of law enforcement disputes by 58%. Collaborative validation shows that the two-way optimization mechanism effectively bridges the gap between technical expectations and real-world effectiveness, providing an interdisciplinary solution for the deep police application of police robot dogs.