Fernando Vanegas

Enabling UAV Navigation with Sensor and Environmental Uncertainty in Cluttered and GPS-Denied Environments

Unmanned Aerial Vehicles (UAV) can navigate with low risk in obstacle-free environments using ground control stations that plan a series of GPS waypoints as a path to follow. This GPS waypoint navigation does however become dangerous in environments where the GPS signal is faulty or is only present in some places and when the airspace is filled with obstacles. UAV navigation then becomes challenging because the UAV uses other sensors, which in turn generate uncertainty about its localisation and motion systems, especially if the UAV is a low cost platform. Additional uncertainty affects the mission when the UAV goal location is only partially known and can only be discovered by exploring and detecting a target. This navigation problem is established in this research as a Partially-Observable Markov Decision Process (POMDP), so as to produce a policy that maps a set of motion commands to belief states and observations. The policy is calculated and updated on-line while flying with a newly-developed system for UAV Uncertainty-Based Navigation (UBNAV), to navigate in cluttered and GPS-denied environments using observations and executing motion commands instead of waypoints. Experimental results in both simulation and real flight tests show that the UAV finds a path on-line to a region where it can explore and detect a target without colliding with obstacles. UBNAV provides a new method and an enabling technology for scientists to implement and test UAV navigation missions with uncertainty where targets must be detected using on-line POMDP in real flight scenarios.

A Novel Methodology for Improving Plant Pest Surveillance in Vineyards and Crops Using UAV-Based Hyperspectral and Spatial Data

Recent advances in remote sensed imagery and geospatial image processing using unmanned aerial vehicles (UAVs) have enabled the rapid and ongoing development of monitoring tools for crop management and the detection/surveillance of insect pests. This paper describes a (UAV) remote sensing-based methodology to increase the efficiency of existing surveillance practices (human inspectors and insect traps) for detecting pest infestations (e.g., grape phylloxera in vineyards). The methodology uses a UAV integrated with advanced digital hyperspectral, multispectral, and RGB sensors. We implemented the methodology for the development of a predictive model for phylloxera detection. In this method, we explore the combination of airborne RGB, multispectral, and hyperspectral imagery with ground-based data at two separate time periods and under different levels of phylloxera infestation. We describe the technology used—the sensors, the UAV, and the flight operations—the processing workflow of the datasets from each imagery type, and the methods for combining multiple airborne with ground-based datasets. Finally, we present relevant results of correlation between the different processed datasets. The objective of this research is to develop a novel methodology for collecting, processing, analysing and integrating multispectral, hyperspectral, ground and spatial data to remote sense different variables in different applications, such as, in this case, plant pest surveillance. The development of such methodology would provide researchers, agronomists, and UAV practitioners reliable data collection protocols and methods to achieve faster processing techniques and integrate multiple sources of data in diverse remote sensing applications.

Uncertainty based online planning for UAV target finding in cluttered and GPS-denied environments

There are some scenarios in which Unmmaned Aerial Vehicle (UAV) navigation becomes a challenge due to the occlusion of GPS systems signal, the presence of obstacles and constraints in the space in which a UAV operates. An additional challenge is presented when a target whose location is unknown must be found within a confined space. In this paper we present a UAV navigation and target finding mission, modelled as a Partially Observable Markov Decision Process (POMDP) using a state-of-the-art online solver in a real scenario using a low cost commercial multi rotor UAV and a modular system architecture running under the Robotic Operative System (ROS). Using POMDP has several advantages to conventional approaches as they take into account uncertainties in sensor information. We present a framework for testing the mission with simulation tests and real flight tests in which we model the system dynamics and motion and perception uncertainties. The system uses a quad-copter aircraft with an board downwards looking camera without the need of GPS systems while avoiding obstacles within a confined area. Results indicate that the system has 100% success rate in simulation and 80% rate during flight test for finding targets located at different locations.

UAV based target finding and tracking in GPS-denied and cluttered environments

In this paper we describe and flight test a novel system architecture for low cost muti-rotor unmanned aerial vehicles (UAVs) for searching, tracking and following a ground target. The UAV uses only on-board sensors for localisation within a GPS-denied space with obstacles. This mission is formulated as a Partially Observable Markov Decision Process (POMDP) and uses a modular framework that runs on the Robotic Operating System (ROS). This system computes a policy for executing actions instead of way-points to navigate and avoid obstacles. Results indicate that the system is robust to overcome uncertainties in localisation of both, the aircraft and the target and avoids collisions with the obstacles.

UAV tracking and following a ground target under motion and localisation uncertainty

Unmanned Aerial Vehicles (UAVs) are increasingly being used in numerous applications, such as remote sensing, environmental monitoring, ecology and search and rescue missions. Effective use of UAVs depends on the ability of the system to navigate in the mission scenario, especially if the UAV is required to navigate autonomously. There are particular scenarios in which UAV navigation faces challenges and risks. This creates the need for robust motion planning capable of overcoming different sources of uncertainty. One example is a UAV flying to search, track and follow a mobile ground target in GPS-denied space, such as below canopy or in between buildings, while avoiding obstacles. A UAV navigating under these conditions can be affected by uncertainties in its localization and motion due to occlusion of GPS signals and the use of low cost sensors. Additionally, the presence of strong winds in the airspace can disturb the motion of the UAV. In this paper, we describe and flight test a novel formulation of a UAV mission for searching, tracking and following a mobile ground target. This mission is formulated as a Partially Observable Markov Decision Process (POMDP) and implemented in real flight using a modular framework. We modelled the UAV dynamic system, the uncertainties in motion and localization of both the UAV and the target, and the wind disturbances. The framework computes a motion plan online for executing motion commands instead of flying to way-points to accomplish the mission. The system enables the UAV to plan its motion allowing it to execute information gathering actions to reduce uncertainty by detecting landmarks in the scenario, while making predictions of the mobile target trajectory and the wind speed based on observations. Results indicate that the system overcomes uncertainties in localization of both the aircraft and the target, and avoids collisions into obstacles despite the presence of wind. This research has the potential of use particularly for remote monitoring in the fields of biodiversity and ecology.