Real-time autonomous 3D navigation for tracked vehicles in rescue environments

The paper presents a novel framework for 3D autonomous navigation for tracked vehicles. The framework takes care of clustering and segmentation of point clouds, traversability analysis, autonomous 3D path planning, motion planning and flippers control. Results illustrated in an experiment section show that the framework is promising to face harsh terrains. Robot performance is proved in three main experiments taken in a training rescue area, on fire escape stairs and in a non-planar testing environment, built ad-hoc to prove 3D path planning functionalities. Performance tests are also presented.

I. Introduction

Tracked vehicles are designed for search and rescue applications where terrain conditions are difficult and unpredictable. They are better suited for such domains than wheeled vehicles due to the larger contact area of tracks with the ground, which provides better traction on harsh terrains. These robotic platforms are usually similar to space rovers with two tracks on the sides linked to a central body (see Figure 1). Each track can be extended with two active flippers. Moreover, mechanical differential systems allow the rotation of the tracks around the body. These systems further increase the traction of such robots, thus improving their stability on sloppy surfaces. Several sensors can be installed, such as rotating 2D laser scanners for 3D point cloud acquisition, mapping and localization, vision systems, IMU and GPS for inertial navigation systems. Despite such robots are well-equipped to face all the navigation difficulties of an harsh environment, their level of autonomy is still not sufficient to operate without the supervision of a human operator. The main challenges of autonomous navigation are plan and effective motion of the tracked vehicle to safely traverse the rough surfaces of an unstructured environment, thus leaving flatlandia.