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Vision-Based Aggressive Flight with a Small Quadrotor

A 740g quadrotor flies fully autonomously with speed up to 4m/s using only onboard sensing and computation. The quadrotor is equipped with two cameras, an IMU, and an 1.6GHz Intel Atom processor

Avian-Inspired Grasping For Quadrotor Micro Aerial Vehicles

Collaborative mapping of an earthquake-damaged building via ground and aerial robots

Vision-based state estimation for autonomous rotorcraft

State Estimation for Indoor and Outdoor Operation with a Micro-Aerial Vehicle

We present a methodology for estimating the state of a micro-aerial vehicle (MAV) as it transitions between different operating environments with varying applicable sensors. We ensure that the estimate is smooth and continuous throughout and provide an associated quality measure of the state estimate. The resulting onboard state estimate is directly applied for feedback control. This video shows experimental results of a MAV autonomously flies through indoor and outdoor environments. Work done by Shaojie Shen and Nathan Michael at the GRASP Lab at the University of Pennsylvania.

3D Indoor Exploration with a Computationally Constrained MAV

We present a methodology that enables a quadrotor aerial robot to autonomously explore single- or multi- floor indoor environments without any human interaction.The quadrotor is purchased from ascending technologies. It comes with an IMU and low level attitude stabilization. We outfitted the robot with a laser scanner, Microsoft Kinect sensor, and deflective mirrors to create a fully autonomous platform. We developed a navigation system that enables realtime localization, mapping, planning and control of the robot in confined indoor environments. All computations are done onboard the 1.6GHz atom processor with no requirements of external infrastructure. The exploration algorithm interacts with the planner and controller and provides continuous guidance to the robot.

Autonomous Aerial Navigation in Confined Indoor Environments

This video presents experimental results of autonomous navigation in confined indoor environments using an aerial robot. The robot is equipped with an IMU, camera, and laser scanner with deflective mirrors. All computations are performed onboard using a 1.6GHz atom processor. The robot is able to navigate autonomously in indoor or outdoor, GPS-denied environments. A SLAM module with vision based loop closure allows the robot to map large-scale, multi-floor environments. A sparse 3D map is generated on the robot based on sensor data, enabling high-level planning and visualization.

Autonomous Multi-Floor Indoor Navigation with a Computationally Constrained MAV

This video shows our results on autonomous multi-floor indoor navigation with a quadrotor. We designed a system that is capable of autonomous navigation with real-time performance on a mobile processor using only onboard sensors. Specifically, we address multi-floor mapping with loop closure, localization, planning, and autonomous control, including adaptation to aerodynamic effects during traversal through spaces with low vertical clearance or strong external disturbances. All of the computation is done onboard the 1.6Ghz Intel Atom processor and uses ROS for interprocess communication. Human interaction is limited to provide high-level goals to the robot.

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Scalable sWarms of Autonomous Robots and Mobile Sensors (SWARMS) project.

The SWARMS project brings together experts in artificial intelligence, control theory, robotics, systems engineering and biology with the goal of understanding swarming behaviors in nature and applications of biologically-inspired models of swarm behaviors to large networked groups of autonomous vehicles.

Aerial Robots for Remote Autonomous Exploration and Mapping
We are interested in exploring the possibility of leveraging an autonomous quadrotor in earthquake-damaged environments through field experiments that focus on cooperative mapping using both ground and aerial robots. Aerial robots offer several advantages over ground robots, including the ability to maneuver through complex three-dimensional (3D) environments and gather data from vantages inaccessible to ground robots. Read More