Study of Human Safety-centric Strategies for Human-Robot Coexistence

Abstract

With the increasing integration of robots into various domains and their coexistence with humans, ensuring human safety has become a critical concern. This thesis highlights the challenges associated with human safety in human-robot coexistence and provides an overview of the solutions and approaches proposed to address these challenges. The coexistence of humans and robots introduces unique safety considerations due to human behavior's dynamic and unpredictable nature and the potential risks posed by robotic systems. Risk assessment involves several steps, including identifying potential hazards, risk analysis, and implementation of safety measures. Safety standards are crucial in ensuring robots' safe and responsible deployment in human-centric environments. Safety standards prioritize human well-being by reducing the risk of physical harm, ensuring safe operation, and establishing guidelines for safe design and behavior. Compliance with these standards enhances public trust in robotic systems, encouraging widespread acceptance and adoption. This thesis explores the standards organizations and regulatory bodies associated with robotics and related industries. Some prominent organizations include the International Organization for Standardization, the International Electrotechnical Commission (IEC), the American National Standards Institute (ANSI), and industry-specific associations like the Robotic Industries Association (RIA).

Human trajectory prediction anticipates individuals' positions and movements in dynamic environments. Data-driven methods can be applied to predict the human trajectory. These methods leverage historical trajectory data, environmental context, and other relevant features to learn patterns and make predictions. The thesis work aims to make the robots navigate in a collaborative environment without invading human space. Robot navigation duties must be carried out fast and safely in the presence of humans, which necessitates making predictions about their intended trajectories. This work explores the usage of probabilistic models relating to human behavior perceived from the environment for the motion planning of Autonomous Mobile Robots. We study the use of a) Maximum-likelihood Human Trajectory Prediction and b) Gibbs-sampled Human Trajectory Prediction. The forecasted human trajectory is used to determine a safe path for robots. We study the efficacy of including the human motion predicted during the motion planning in terms of the effective speed of the robots, with and without human-aware planning.