An Memorandum of Understanding between the University of Nevada, Reno and Queensland University of Technology was signed. The MOU establishes a collaborative relationship to facilitate research and the development of opportunities for new technologies, robotics and autonomous vehicles, air quality, exhaust emissions and hydrologic sciences.

On Friday 16 June, The autonomous vehicles bill AB 69 was signed. AB69 allows the use of fully autonomous vehicles for commercial use. The bill is considered key in helping Nevada play a key role in the development and use of autonomous vehicles. It will have important implications for the University's Intelligent Mobility initiative that our lab participates.

Below the coverage from KTVN which also shows segments of previous demonstrations of our lab: ​http://www.ktvn.com/story/35699801/lawmakers-approve-most-of-sandovals-requests 

The presentations of the ICUAS2017 Workshop entitled "Robots in the Wild" are provided below. 

The presentations of the ICRA 2017 Workshop on Autonomous Structural Monitoring and Maintenance using Aerial Robots are now available online at: ​http://www.aerial-monitoring-maintenance-workshop.com/agenda--presentations.html

Open source code: https://github.com/unr-arl/rhem_planner

​Autonomous exploration and reliable mapping of unknown environments corresponds to a major challenge for mobile robotic systems. For many important application domains, such as industrial inspection or search and rescue, this task is further challenged from the fact that such operations often have to take place in GPS-denied environments and possibly visually-degraded conditions. 

During this ICRA conference we presented our work on "Uncertainty-aware Receding Horizon Exploration and Mapping using Aerial Robots" which aims to reliably address this problem. In this work, we move away from deterministic approaches on autonomous exploration and we propose a localization uncertainty-aware autonomous receding horizon exploration and mapping planner verified using aerial robots. This planner follows a two-step optimization paradigm. At first, in an online computed random tree the algorithm finds a finite-horizon branch that optimizes the amount of space expected to be explored. The first viewpoint configuration of this branch is selected, but the path towards it is decided through a second planning step. Within that, a new tree is sampled, admissible branches arriving at the reference viewpoint are found and the robot belief about its state and the tracked landmarks of the environment is propagated. The branch that minimizes the expected localization uncertainty is selected, the corresponding path is executed by the robot and the whole process is iteratively repeated.