3D Semantic Heuristic Planning for Safer Aerial Robot Navigation Indoors

This paper presents a 3D safe heuristic planner for aerial robot indoor navigation incorporating semantic information of the environment. Our interest lies in how the inclusion of semantic information in industrial environments or buildings can positively affect planning to ensure 3D safe navigation within the environment. The planner is based on an any-angle path planning algorithm, particularly Lazy Theta* algorithm. There are many works in the literature that include non-uniform costs related to distance to the closest obstacles in order to achieve a safe planner, but they generally do not consider the semantic information which is relevant in factories. Semantic-aware planning opens the door to safer plans, adapting the distance to obstacles depending on the category of the objects. Our Semantic Heuristic planner considers the semantic information of all the nodes along the line of sight between an initial and final node. Tests in a 3D environment with corridors, stairs, doors, columns and walls are performed to evaluate the proposed planner with respect to Lazy Theta* algorithms and a cost-aware Lazy-Theta* over Euclidean distance functions. The results show advantages of the Semantic Heuristic planner in terms of safety depending on the type of obstacle, the length of the path and efficiency with respect to the cost-aware Lazy-Theta* which only considers the distance to obstacles in its cost function.

I. Introduction

The inclusion of robots in factories is already a reality. Robots play an important role to successfully enable key improvements in the manufacturing technology. There are many works with mobile robots or AGV (Automated Guided Vehicles) in the literature [1] [2], but not as many with aerial robots or drones [3] [4]. Aerial robots can navigate and access areas that are usually deemed unsafe and hazardous for humans, so they effectively minimize risks and slash operational costs. The range of applications with aerial robots covering more action space could be greater, but it also implies considering more safety requirements. A safe and robust autonomous navigation system is crucial in these 3D applications. Among the tasks to consider for implementing an autonomous navigation system, path planning stands out. Path planning generally refers to finding the shortest path con-necting two positions, while avoiding collision with obstacles, but the today's challenging robotic applications as [5] [6] is to implement algorithms that directly generate safe paths in order to maintain a safety margin with respect to obstacles.