Social distance control for quadruped robots in a gated spike filter neural network framework.

Ensuring safe human-robot interactions is a crucial concern in robotics research and development. However, controlling the social distance between quadruped robots and humans poses a significant challenge due to the large motion range of robots and the lack of specific social distance rules for human safety. This study proposes an innovative framework that integrates proxemic theory and ISO safety strategy to provide a new social distance control mechanism for human-quadruped robot interactions. The proposed framework uses a gated spike filter neural network (GSN) that fuses LiDAR and RGB-D data to estimate human-robot distance and ensures quantified human-robot social distance with continuous velocity limitations, which current approaches do not offer. The experimental results demonstrate that the GSN algorithm offers high accuracy and is well-suited for large-range human-robot distance control of quadruped robots. It has an average prediction error that is 31.4% lower than traditional Kalman filter methods and offers a more efficient and precise solution for real-time social distance control. Additionally, this study includes a detailed use case that demonstrates how to apply the proposed framework to human-quadruped robot interactions. Overall, this study's findings have implications for improving the safety and efficiency of human-robot interactions in various applications. [ABSTRACT FROM AUTHOR]