A Safety Filter for Realizing Safe Robot Navigation in Crowds

Abstract

It is challenging to realize the safe navigation of mobile robots in crowds. Most of the previous studies may lead to unsafe robot navigation in crowds, as safety guarantee is lacked. To solve this problem, we devise a safety filter (SF) that enables realization of safe robot navigation in crowds, and provides safety guarantees by verifying whether the optimal action recommended by an unsafe method is safe and, if not, corrects the action. The three main processes performed by the SF applied to given robot are (1) construction of the safe state constraints of the robot using a safe set; (2) construction of the safe action constraints of the robot based on discrete-time generalized velocity obstacles (DGVOs); and (3) determination of a feasible solution of the SF design problem, or, if none can be found, replacement of the above hard constraints with heuristic soft constraints. We used the SF with a reaction-based method and three learning-based methods in simulation experiments of random and non-random crowds, and the results showed that the SF decreases the collision rates and danger rates and thereby increases the success rates of these methods. We also deployed the SF with three learning-based methods on an mr1000 robot in real-world experiments, and the results showed that the SF enabled the robot using learning-based methods to navigate to its goal without colliding with humans.