Fast, Autonomous Flight in GPS-Denied and Cluttered Environments

One of the most challenging tasks for a flying robot is to autonomously navigate between target locations quickly and reliably while avoiding obstacles in its path, and with little to no a-priori knowledge of the operating environment. This challenge is addressed in the present paper. We describe the system design and software architecture of our proposed solution, and showcase how all the distinct components can be integrated to enable smooth robot operation. We provide critical insight on hardware and software component selection and development, and present results from extensive experimental testing in real-world warehouse environments. Experimental testing reveals that our proposed solution can deliver fast and robust aerial robot autonomous navigation in cluttered, GPS-denied environments.Comment: Pre-peer reviewed version of the article accepted in Journal of Field Robotic

Autonomous Flight in Unknown Indoor Environments

http://multi-science.metapress.com/content/80586kml376k2711/This paper presents our solution for enabling a quadrotor helicopter, equipped with a laser rangefinder sensor, to autonomously explore and map unstructured and unknown indoor environments. While these capabilities are already commodities on ground vehicles, air vehicles seeking the same performance face unique challenges. In this paper, we describe the difficulties in achieving fully autonomous helicopter flight, highlighting the differences between ground and helicopter robots that make it difficult to use algorithms that have been developed for ground robots. We then provide an overview of our solution to the key problems, including a multilevel sensing and control hierarchy, a high-speed laser scan-matching algorithm, an EKF for data fusion, a high-level SLAM implementation, and an exploration planner. Finally, we show experimental results demonstrating the helicopter's ability to navigate accurately and autonomously in unknown environments.National Science Foundation (U.S.) (NSF Division of Information and Intelligent Systems under grant # 0546467)United States. Army Research Office (ARO MAST CTA)Singapore. Armed Force

Effective Target Aware Visual Navigation for UAVs

In this paper we propose an effective vision-based navigation method that allows a multirotor vehicle to simultaneously reach a desired goal pose in the environment while constantly facing a target object or landmark. Standard techniques such as Position-Based Visual Servoing (PBVS) and Image-Based Visual Servoing (IBVS) in some cases (e.g., while the multirotor is performing fast maneuvers) do not allow to constantly maintain the line of sight with a target of interest. Instead, we compute the optimal trajectory by solving a non-linear optimization problem that minimizes the target re-projection error while meeting the UAV's dynamic constraints. The desired trajectory is then tracked by means of a real-time Non-linear Model Predictive Controller (NMPC): this implicitly allows the multirotor to satisfy both the required constraints. We successfully evaluate the proposed approach in many real and simulated experiments, making an exhaustive comparison with a standard approach.Comment: Conference paper at "European Conference on Mobile Robotics" (ECMR) 201

High-Performance Testbed for Vision-Aided Autonomous Navigation for Quadrotor UAVs in Cluttered Environments

This thesis presents the development of an aerial robotic testbed based on Robot Operating System (ROS). The purpose of this high-performance testbed is to develop a system capable of performing robust navigation tasks using vision tools such as a stereo camera. While ensuring the computation of robot odometery, the system is also capable of sensing the environment using the same stereo camera. Hence, all the navigation tasks are performed using a stereo camera and an inertial measurement unit (IMU) as the main sensor suite. ROS is used as a framework for software integration due to its capabilities to provide efficient communication and sensor interfaces. Moreover, it also allows us to use C++ which is efficient in performance especially on embedded platforms. Combining together ROS and C++ provides the necessary computation efficiency and tools to handle fast, real-time image processing and planning which are the vital parts of navigation and obstacle avoidance on such scale. The main application of this work revolves around proposing a real-time and efficient way to demonstrate vision-based navigation in UAVs. The proposed approach is developed for a quadrotor UAV which is capable of performing defensive maneuvers in case any obstacles are in its way, while constantly moving towards a user-defined final destination. Stereo depth computation adds a third axis to a two dimensional image coordinate frame. This can be referred to as the depth image space or depth image coordinate frame. The idea of planning in this frame of reference is utilized along with certain precomputed action primitives. The formulation of these action primitives leads to a hybrid control law for feasible trajectory generation. Further, a proof of stability of this system is also presented. The proposed approach keeps in view the fact that while performing fast maneuvers and obstacle avoidance simultaneously, many of the standard optimization approaches might not work in real-time on-board due to time and resource limitations. This leads to a need for the development of real-time techniques for vision-based autonomous navigation