Safety Assurance for Quadrotor Kinodynamic Motion Planning

TL;DR

This paper presents a method combining kinodynamic motion planning with runtime safety assurance to ensure safe operation of autonomous drones, specifically addressing safety constraints during deployment in a simulated environment.

Contribution

It introduces a novel safety assurance framework that integrates a high-level geometric planner with a low-level safety filter for kinodynamic drone navigation.

Findings

Successfully guarantees safety constraints during drone navigation

Integrates high-level planning with low-level safety filtering

Demonstrates effectiveness in a 3D simulation environment

Abstract

Autonomous drones have gained considerable attention for applications in real-world scenarios, such as search and rescue, inspection, and delivery. As their use becomes ever more pervasive in civilian applications, failure to ensure safe operation can lead to physical damage to the system, environmental pollution, and even loss of human life. Recent work has demonstrated that motion planning techniques effectively generate a collision-free trajectory during navigation. However, these methods, while creating the motion plans, do not inherently consider the safe operational region of the system, leading to potential safety constraints violation during deployment. In this paper, we propose a method that leverages run time safety assurance in a kinodynamic motion planning scheme to satisfy the system's operational constraints. First, we use a sampling-based geometric planner to determine a high-level collision-free path within a user-defined space. Second, we design a low-level safety assurance filter to provide safety guarantees to the control input of a Linear Quadratic Regulator (LQR) designed with the purpose of trajectory tracking. We demonstrate our proposed approach in a restricted 3D simulation environment using a model of the Crazyflie 2.0 drone.