This video highlights our system that enables a Micro Aerial Vehicle (MAV) to autonomously explore and map unstructured and unknown GPS-denied environments. While mapping and exploration solutions are now well-established for ground vehicles, air vehicles face unique challenges which have hindered the development of similar capabilities. Although there has been recent progress toward sensing, control, and navigation techniques for GPS-denied flight, there have been few demonstrations of stable, goal-directed flight in real-world environments. Our system leverages a multi-level sensing and control hierarchy that matches the computational complexity of the component algorithms with the real-time needs of a MAV to achieve autonomy in unconstrained environments.

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. 1 Finally, we show experimental results demonstrating the helicopter's ability to navigate accurately and autonomously in unknown environments. INTRODUCTIONMicro Aerial Vehicles (MAVs) are increasingly being used in military and civilian domains, including surveillance operations, weather observation, and disaster relief coordination. Enabled by GPS and MEMS inertial sensors, MAVs that can fly in outdoor environments without human intervention have been developed [2,3,4,5].Unfortunately, most indoor environments and many parts of the urban canyon remain without access to external positioning systems such as GPS. Autonomous MAVs today are thus limited in their ability to fly through these areas. Traditionally, unmanned vehicles operating in GPS-denied environments can rely on dead reckoning for localization, but these measurements drift over time. Alternatively, simultaneous localization and mapping (SLAM) algorithms build a map of the environment around the vehicle while simultaneously using it to estimate the vehicle's position. Although there have been significant advances in developing accurate, drift-free SLAM algorithms for large-scale environments, these algorithms have focused almost exclusively on ground or underwater vehicles. In contrast, attempts to achieve the same results with MAVs have not been as successful due to a combination of limited payloads for sensing and computation, coupled with the fast, unstable dynamics of the air vehicles.In this work, we present our quadrotor helicopter system, shown in Figure 1, that is capable of autonomous flight in unstructured indoor environments, such as the one shown in Figure 2. The system employs a multi-level sensor processing hierarchy designed to meet the requirements for controlling a helicopter. The key contribution of this paper is the development of a fully autonomous quadrotor that relies only on onboard sensors for stable control without requiring prior maps of the environment.After discussing related work in Section 2, we begin in Section 3 by analyzing the key challenges MAVs face when attempting to perform SLAM. We then give an overview of the algorithms employed by our system. Finally, we demonstrate our helicopter navigating autonomo...

This paper presents our solution for enabling a quadrotor helicopter to autonomously navigate unstructured and unknown indoor environments. We compare two sensor suites, specifically a laser rangefinder and a stereo camera. Laser and camera sensors are both well-suited for recovering the helicopter's relative motion and velocity. Because they use different cues from the environment, each sensor has its own set of advantages and limitations that are complimentary to the other sensor. Our eventual goal is to integrate both sensors on-board a single helicopter platform, leading to the development of an autonomous helicopter system that is robust to generic indoor environmental conditions. In this paper, we present results in this direction, describing the key components for autonomous navigation using either of the two sensors separately.

RGB-D cameras provide both color images and per-pixel depth estimates. The richness of this data and the recent development of low-cost sensors have combined to present an attractive opportunity for mobile robotics research. In this paper, we describe a system for visual odometry and mapping using an RGB-D camera, and its application to autonomous flight. By leveraging results from recent stateof-the-art algorithms and hardware, our system enables 3D flight in cluttered environments using only onboard sensor data. All computation and sensing required for local position control are performed onboard the vehicle, reducing the dependence on unreliable wireless links. However, even with accurate 3D sensing and position estimation, some parts of the environment have more perceptual structure than others, leading to state estimates that vary in accuracy across the environment. If the vehicle plans a path without regard to how well it can localize itself along that path, it runs the risk of becoming lost or worse. We show how the Belief Roadmap (BRM) algorithm (Prentice and Roy, 2009), a belief space extension of the Probabilistic Roadmap algorithm, can be used to plan vehicle trajectories that incorporate the sensing model of the RGB-D camera. We evaluate the effectiveness of our system for controlling a quadrotor micro air vehicle, demonstrate its use for constructing detailed 3D maps of an indoor environment, and discuss its limitations.Abraham Bachrach and Samuel Prentice contributed equally to this work.

Deciding how to act in partially observable environments remains an active area of research. Identifying good sequences of decisions is particularly challenging when good control performance requires planning multiple steps into the future in domains with many states. Towards addressing this challenge, we present an online, forward-search algorithm called the Posterior Belief Distribution (PBD). PBD leverages a novel method for calculating the posterior distribution over beliefs that result after a sequence of actions is taken, given the set of observation sequences that could be received during this process. This method allows us to efficiently evaluate the expected reward of a sequence of primitive actions, which we refer to as macro-actions. We present a formal analysis of our approach, and examine its performance on two very large simulation experiments: scientific exploration and a target monitoring domain. We also demonstrate our algorithm being used to control a real robotic helicopter in a target monitoring experiment, which suggests that our approach has practical potential for planning in real-world, large partially observable domains where a multi-step lookahead is required to achieve good performance.

This paper addresses the problem of autonomous navigation of a micro air vehicle (MAV) in GPS-denied environments. We present experimental validation and analysis for our system that enables a quadrotor helicopter, equipped with a laser range finder sensor, to autonomously explore and map unstructured and unknown environments. The key challenge for enabling GPS-denied flight of a MAV is that the system must be able to estimate its position and velocity by sensing unknown environmental structure with sufficient accuracy and low enough latency to stably control the vehicle. Our solution overcomes this challenge in the face of MAV payload limitations imposed on sensing, computational, and communication resources. We first analyze the requirements to achieve fully autonomous quadrotor helicopter flight in GPS-denied areas, highlighting the differences between ground and air robots that make it difficult to use algorithms developed for ground robots. We report on experiments that validate our solutions to key challenges, namely a multilevel sensing and control hierarchy that incorporates a high-speed laser scan-matching algorithm, data fusion filter, high-level simultaneous localization and mapping, and a goal-directed exploration module. These experiments illustrate the quadrotor helicopter's ability to accurately and autonomously navigate in a number of large-scale unknown environments, both indoors and in the urban canyon. The system was further validated in the field by our winning entry in the 2009 International Aerial Robotics Competition, which required the quadrotor to autonomously enter a hazardous unknown environment through a window, explore the indoor structure without GPS, and search for a visual target. C 2011 Wiley Periodicals, Inc.

No abstract

A pair of enantiomers and a pair of 2,3-dihydro-1H-indene epimers, rac-indidene A (rac-1), indidenes B and C (2, 3); four new coumarin glucosides (4-7); and four known coumarin glucosides (8-11) were isolated from the bark of Streblus indicus (Bur.) Corner. The structures of 1-11 were defined by physical data analyses, including MS, NMR, and single-crystal X-ray diffraction. The absolute configurations of the 2,3-dihydro-1H-indene derivatives were defined via experimental and calculated ECD data. rac-Indidene A and indidenes B and C showed inhibitory activity against A549 and MCF-7 tumor cells with IC values in the range of 2.2 ± 0.1 to 7.2 ± 0.9 μM.