Large scale 3D mapping using LIDAR
In this project, we used the Iterative Closest Point (ICP) algorithm to build a mapping pipeline that can construct 3D maps from LIDAR data. This pipeline has been tested on data from Velodyne and RIEGL LIDARs. The Velodyne LIDAR (VLP16) was mounted on a car and was driven around, while GPS was NOT used. Final trajectories and maps were constructed from this LIDAR data.
Vision based GPS-denied Object Tracking and Following for UAV
In this project, we present a vision based control strategy for tracking and following objects using an Unmanned Aerial Vehicle. We have developed an image based visual servoing method that uses only a forward looking camera for tracking and following user-specified objects from a multi-rotor UAV continuously while maintaining a fixed distance from the object and also simultaneously keeping it in the center of the image plane; without any dependence on GPS systems. The algorithm is validated using a Parrot AR Drone 2.0 in outdoor conditions while tracking and following people and other static or fast moving objects, while showing the robustness of the proposed system against perturbations and illumination changes and occlusions. Please visit the project page for more details.
Ars Robotica is a collaboration between Unmanned Systems Lab and the School of Film, Dance and Theatre at Arizona State University. Using the Rethink Robotics Baxter as a test platform, Ars Robotica aims to investigate the possibility of defining and achieving a human quality of movement through robots, and validating it through the idea of viewing a robot as a performer in theater. Training data is obtained through various modes of sensing ranging from simple devices such as a Microsoft Kinect; to high speed precise tracking setups such as a 12 camera Optitrack system; which is then used for defining a vocabulary of human motion primitives, thus helping create a framework for autonomous interpretation and expression of human-like motion through Baxter. Please visit the project page for more details.
Terrain Mapping using UAVs
Three dimensional mapping is an extremely important aspect of geological surveying. Current methods of doing this, however, often poses pragmatic challenges. We introduce a technique for performing terrain mapping using unmanned aerial vehicles (UAVs) and standard digital cameras. Using a photogrammetric process called structure from motion (SFM), aerial images can be used to infer 3-dimensional data. Please visit the project page for more details.
Autonomous Kite Plane for Aerial Surveillance
The development of an autonomous fixed-wing motorized kite plane, Autokite, offers a unique approach to aerial photography in the field. The inexpensive and lightweight nature of the Autokite makes it ideal for deployment in environments that are remote and or extreme.
Autonomous ship board landing of a VTOL UAV
The autonomous landing of Vertical Take Off and Landing (VTOL) Unmanned Aerial Vehicles (UAVs) is a very important capability for autonomous systems. Autonomously landing on a ship deck platform continues to be studied and has only recently been solved for very favorable weather conditions.
Our challenge is to provide the UAV with the capabilities of autonomously landing on ship deck platforms in extreme weather conditions.
EGGS (Exploration Geology & Geophysics Sensors)
The EGGS Project, Exploration Geology & Geophysics Sensors, aims to develop a diverse set of robust self-righting multi-purpose data-collection platforms capable of assisting scientists/explorers in the field on Earth or through remote deployments to near-by asteroids. With an integrated camera, microscope, accelerometer, magnetometer, and configurations for adding other instruments, EGGS are a low-cost 3D printable option for students, researchers, and enthusiasts who want to learn more about an environment remotely.
Using UAVs to Assesses Signal Strength Patterns for Radio Telescopes
In this work the design of flight hardware for detecting the signal strength field pattern of an array of Radio Telescopes is considered. Utilizing the ultra-stable and robust aerial platform offered by a multi-rotor craft makes this task possible.
Change Detection using airborne Lidar
In the course of this project, we worked with geologists on developing algorithms for finding the local displacements on topographies during earth quakes. Algorithms use the Digital Elevation Models of earthquake sites (before and after the earthquake) obtained from Lidar scanners mounted on aerial vehicles. Please visit the project page for more details.
The objective of the NIR project involved constructing an equivalent MER PANCAM from readily available commercial parts for use of science and study of Earth’s atmosphere and geological features. Please visit the project page for more details.
Path Planning for Ground Vehicles
The objective of this project was to study and devise new means for motion planning for ground vehicles, using a rover named Raven as the prototype vehicle. More specifically, we try to determine smooth paths for Raven to follow, as it traverses waypoints; such paths have wide use in applications, for instance in following an astronaut as (s)he walks along a random path. Please visit the Project Page for more details.
The objective of this work was to autonomously detect manually verified features (plumes) in images under onboard conditions. Success enables these methods to be applied to future outer solar system missions and facilitates onboard autonomous detection of transient events and features regardless of viewing and illumination effects, electronic interference, and physical image artifacts. Autonomous detection allows the maximization of the spacecrafts memory capacity and downlink bandwidth by prioritizing data of utmost scientific significance. Please visit the project page for more details.
RAVEN (Robotic Assist Vehicle for Extraterrestrial Navigation) was designed for the 2010 Revolutionary Aerospace Systems Concepts Academic Linkage (RASC-AL) contest. Please visit the project page for more details.
Road Detection from UAV Aerial Imagery
Using aerial images taken from UAV photography to detect the presence of roads. In this work, we developed variations of algorithms suitable for different types of roads and detection. Please visit the project page for more details.
Autonomous Underwater Vehicles have proven themselves to be indispensable tools for mapping and sampling aquatic environments. However these sensing platforms can only travel as far as their stored energy capacities allow them to. Thus we have researched both offline and online adaptive sampling strategies that optimize both the estimation accuracy of the models derived from sampling and the energy consumption of the vehicle through. Please visit the project page for more details.