Autonomous flight in unstructured and unknown indoor environments

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2009.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (p. 119-126).This thesis presents the design, implementation, and validation of a system that enables a micro air vehicle to autonomously explore and map unstructured and unknown indoor environments. Such a vehicle would be of considerable use in many real-world applications such as search and rescue, civil engineering inspection, and a host of military tasks where it is dangerous or difficult to send people. While mapping and exploration capabilities are common for ground vehicles today, air vehicles seeking to achieve these capabilities face unique challenges. While there has been recent progress toward sensing, control, and navigation suites for GPS-denied flight, there have been few demonstrations of stable, goal-directed flight in real environments. The main focus of this research is the development of real-time state estimation techniques that allow our quadrotor helicopter to fly autonomously in indoor, GPS-denied environments. Accomplishing this feat required the development of a large integrated system that brought together many components into a cohesive package. As such, the primary contribution is the development of the complete working system. I show experimental results that illustrate the MAV's ability to navigate accurately in unknown environments, and demonstrate that our algorithms enable the MAV to operate autonomously in a variety of indoor environments.by Abraham Galton Bachrach.S.M

Trajectory bundle estimation For perception-driven planning

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student-submitted PDF version of thesis.Includes bibliographical references (p. 113-122).When operating in unknown environments, autonomous vehicles must perceive and understand the environment ahead in order to make effective navigation decisions. Long range perception can enable a vehicle to choose actions that take it directly toward its goal, avoiding dead ends. In addition, the perception range is critically important for ensuring the safety of vehicles with constrained dynamics. In general, the faster a vehicle moves, the more constrained its dynamics become due to acceleration limits imposed by its actuators. This means that the speed at which an autonomous agent can safely travel is often governed by its ability to perceive and understand the environment ahead. Overall, perception range is one of the most important factors that determines the performance of an autonomous vehicle. Today, autonomous vehicles tend to rely exclusively on metric representations built using range sensors to plan paths. However, such sensors are limited by their maximum range, field of view, and occluding obstacles in the foreground. Together, these limitations make up what we call the metric sensing horizon of the vehicle. The first two limitations are generally determined by the weight, size, power, and cost budget allocated to sensing. However, range sensors will always be limited by occlusions. If we wish to develop autonomous vehicles that are able to navigate directly toward a goal at high speeds through unknown environments, then we must move beyond the simple range-sensor based techniques. We must develop algorithms that enable autonomous agents to harness knowledge about the structure of the world to interpret additional sensor information (such as appearance information provided by cameras), and make inferences about parts of the world that cannot be directly observed. We develop a new representation based around trajectory bundles, that makes this challenging task more tractable. Rather than attempt to explicitly model the geometry of the world in front of the vehicle (which can be incredibly complex), we reason about the world in terms of what the vehicle can and cannot do. Trajectory bundles are designed to capture an abstract concept such as the command "go straight and then turn towards the right" in a concrete and actionable manner. We employ a library of trajectory bundles to reason about the layout of obstacles in the environment based on which bundles in the library are predicted to be feasible. Trajectory bundles provide a lens through which we can look at perception tasks, allowing us to leverage machine learning tools in much more effective ways for navigation. In this thesis we introduce trajectory bundles, and develop algorithms that use them to enable perception-driven planning. We develop a trajectory clustering algorithm that enables us to construct a set of trajectory bundles. We then develop a Bayesian filtering framework that enables us to estimate a belief over which trajectory bundles are feasible based on the history of actions and observations of the vehicle. We test our algorithms by using them to navigate a simulated fixed wing air vehicle at high speeds through an unknown environment using a monocular camera sensor.by Abraham Galton Bachrach.Ph.D

State estimation for aggressive flight in GPS-denied environments using onboard sensing

In this paper we present a state estimation method based on an inertial measurement unit (IMU) and a planar laser range finder suitable for use in real-time on a fixed-wing micro air vehicle (MAV). The algorithm is capable of maintaing accurate state estimates during aggressive flight in unstructured 3D environments without the use of an external positioning system. Our localization algorithm is based on an extension of the Gaussian Particle Filter. We partition the state according to measurement independence relationships and then calculate a pseudo-linear update which allows us to use 20x fewer particles than a naive implementation to achieve similar accuracy in the state estimate. We also propose a multi-step forward fitting method to identify the noise parameters of the IMU and compare results with and without accurate position measurements. Our process and measurement models integrate naturally with an exponential coordinates representation of the attitude uncertainty. We demonstrate our algorithms experimentally on a fixed-wing vehicle flying in a challenging indoor environment

CELLO: A fast algorithm for Covariance Estimation

We present CELLO (Covariance Estimation and Learning through Likelihood Optimization), an algorithm for predicting the covariances of measurements based on any available informative features. This algorithm is intended to improve the accuracy and reliability of on-line state estimation by providing a principled way to extend the conventional fixed-covariance Gaussian measurement model. We show that in experiments, CELLO learns to predict measurement covariances that agree with empirical covariances obtained by manually annotating sensor regimes. We also show that using the learned covariances during filtering provides substantial quantitative improvement to the overall state estimate. © 2013 IEEE.United States. National Aeronautics and Space AdministrationSiemens Corporate ResearchUnited States. Office of Naval Research. Multidisciplinary University Research InitiativeMicro Autonomous Consortium Systems and Technolog

Efficient Planning under Uncertainty for a Target-Tracking Micro-Aerial Vehicle

A helicopter agent has to plan trajectories to track multiple ground targets from the air. The agent has partial information of each target's pose, and must reason about its uncertainty of the targets' poses when planning subsequent actions. We present an online, forward-search algorithm for planning under uncertainty by representing the agent's belief of each target's pose as a multi-modal Gaussian belief. We exploit this parametric belief representation to directly compute the distribution of posterior beliefs after actions are taken. This analytic computation not only enables us to plan in problems with continuous observation spaces, but also allows the agent to search deeper by considering policies composed of multi-step action sequences; deeper searches better enable the agent to keep the targets well-localized. We present experimental results in simulation, as well as demonstrate the algorithm on an actual quadrotor helicopter tracking multiple vehicles on a road network constructed indoors.National Science Foundation (U.S.) (grant 0546467)United States. Army Research Office (Collaborative Technology Alliances (CTA) and Micro Autonomous Systems and Technology (MAST) )United States. Office of Naval Research (MURI Grant N00014-07-1-0749

Aggressive flight of fixed-wing and quadrotor aircraft in dense indoor environments

In this paper, we describe trajectory planning and state estimation algorithms for aggressive flight of micro aerial vehicles in known, obstacle-dense environments. Finding aggressive but dynamically feasible and collision-free trajectories in cluttered environments requires trajectory optimization and state estimation in the full state space of the vehicle, which is usually computationally infeasible on realistic timescales for real vehicles and sensors. We first build on previous work of van Nieuwstadt and Murray and Mellinger and Kumar, to show how a search process can be coupled with optimization in the output space of a differentially flat vehicle model to find aggressive trajectories that utilize the full maneuvering capabilities of a quadrotor. We further extend this work to vehicles with complex, Dubins-type dynamics and present a novel trajectory representation called a “Dubins–Polynomial trajectory”, which allows us to optimize trajectories for fixed-wing vehicles. To provide accurate state estimation for aggressive flight, we show how the Gaussian particle filter can be extended to allow laser rangefinder localization to be combined with a Kalman filter. This formulation allows similar estimation accuracy to particle filtering in the full vehicle state but with an order of magnitude more efficiency. We conclude with experiments demonstrating the execution of quadrotor and fixed-wing trajectories in cluttered environments. We show results of aggressive flight at speeds of up to 8 m/s for the quadrotor and 11 m/s for the fixed-wing aircraft.Micro Autonomous Consortium Systems and TechnologyUnited States. Office of Naval Research. Multidisciplinary University Research Initiative (Grant N00014-09-1-1052)American Society for Engineering Education. National Defense Science and Engineering Graduate Fellowshi

Co-ordinated Tracking and Planning Using Air and Ground Vehicles

The MAV ’08 competition in Agra, India focused on the problem of using air and ground vehicles to locate and rescue hostages being held in a remote building. Executing this mission required addressing a number of technical challenges. The first such technical challenge was the design and operation of a micro air vehicle (MAV) capable of flying the necessary distance and carrying a sensor payload for localizing the hostages. The second technical challenge was the design and implementation of vision and state estimation algorithms to detect and track ground adversaries guarding the hostages. The third technical challenge was the design and implementation of robust planning algorithms that could co-ordinate with the MAV state estimates and generate tactical motion plans for ground vehicles to reach the hostage location without detection by the ground adversaries. In this paper we describe our solutions to these challenges. Firstly, we summarize the design of our micro air vehicle, focusing on the navigation and sensing payload. Secondly, we describe the vision and state estimation algorithms used to track ground features through a sequence of images from the MAV, including stationary obstacles and moving adversaries. Thirdly, we describe the planning algorithm used to generate motion plans to allow the ground vehicles to approach the hostage building undetected by adversaries tracked from the air. Finally, we provide results of our system’s performance during the mission execution

RANGE - Robust autonomous navigation in GPS-denied environments

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

Natural language command of an autonomous micro-air vehicle

Natural language is a flexible and intuitive modality for conveying directions and commands to a robot but presents a number of computational challenges. Diverse words and phrases must be mapped into structures that the robot can understand, and elements in those structures must be grounded in an uncertain environment. In this paper we present a micro-air vehicle (MAV) capable of following natural language directions through a previously mapped and labeled environment. We extend our previous work in understanding 2D natural language directions to three dimensions, accommodating new verb modifiers such as go up and go down, and commands such as turn around and face the windows. We demonstrate the robot following directions created by a human for another human, and interactively executing commands in the context of surveillance and search and rescue in confined spaces. In an informal study, 71% of the paths computed from directions given by one user terminated within 10 m of the desired destination.United States. Office of Naval Research (MURI N00014-07-1-0749)United States. Office of Naval Research (MURI N00014-09-1-1052

Multiple relative pose graphs for robust cooperative mapping

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 75-78).This thesis describes a new representation and algorithm for cooperative and persistent simultaneous localization and mapping (SLAM) using multiple robots. Recent pose graph representations have proven very successful for single robot mapping and localization. Among these methods, iSAM (incremental smoothing and mapping) gives an exact incremental solution to the SLAM problem by solving a full nonlinear optimization problem in real-time. In this paper, we present a novel extension to iSAM to facilitate multi-robot mapping based on multiple pose graphs. Our main contribution is a relative formulation of the relationship between multiple pose graphs. Our formulation avoids the initialization problem and leads to an efficient solution when compared to a completely global solution. Efficient access to covariances at any time for relative parameters is also provided, facilitating data association and loop closing. Each individual pose graph still uses a global parameterization, so that the overall system provides a globally consistent multi-robot solution. The performance of the technique is illustrated on a publicly available multi-robot data set as well as other data including a helicopter-ground robot combination.by Been Kim.S.M