Danilo Tardioli

2papers

2 Papers

8.2ROApr 29
Neural-Geometric Tunnel Traversal: Localization-free UAV Flight with Tilted LiDARs

Lorenzo Cano, Alejandro R. Mosteo, Danilo Tardioli

Navigation of UAVs in challenging environments like tunnels or mines, where it is not possible to use GNSS methods to self-localize, illumination may be uneven or nonexistent, and wall features are likely to be scarce, is a complex task, especially if the navigation has to be done at high speed. In this paper we propose a novel proof-of-concept navigation technique for UAVs based on the use of LiDAR information through the joint use of geometric and machine-learning algorithms. The perceived information is processed by a deep neural network to establish the yaw of the UAV with respect to the tunnel's longitudinal axis, in order to adjust the direction of navigation. Additionally, a geometric method is used to compute the safest location inside the tunnel (i.e. the one that maximizes the distance to the closest obstacle). This information proves to be sufficient for simple yet effective navigation in straight and curved tunnels.

NIJul 20, 2017
Pound: A ROS node for Reducing Delay and Jitter in Wireless Multi-Robot Networks

Danilo Tardioli, Ramviyas Parasuraman, Petter Ögren

The Robot Operating System (ROS) is rapidly becoming the de facto framework for building robotics systems, thanks to its flexibility and the large acceptance that it has received in the robotics community. With the growth of its popularity, it has started to be used in multi-robot systems as well. However, the TCP connections that the platform relies on for connecting the so-called ROS nodes, presents several issues in terms of limited-bandwidth, delays and jitter, when used in wireless ad-hoc networks. In this paper, we present a thorough analysis of the problem and propose a new ROS node called Pound to improve the wireless communication performance. Pound allows the use of multiple ROS cores and introduces a priority scheme favoring more important flows over less important ones, thus reducing delay and jitter over single-hop and multihop networks. We compare Pound to the state-of-the-art solutions and show that it performs equally well, or better in all the test cases, including a control-over-network example.