Vision‐based navigation through urban canyons

2010 IEEE/RSJ International Conference on Intelligent Robots and Systems

et al. 2010

Abstract-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.

Section: Related Workmentioning

confidence: 99%

Natural language command of an autonomous micro-air vehicle

Huang

Tellex

2010 IEEE/RSJ International Conference on Intelligent Robots and Systems

et al. 2010

“…This includes both fixed wing vehicles [11], as well as larger scale helicopters [4], [10], [8]. While vision based approaches warrant further study, the authors do not address the challenge of agile flight.…”

Section: Resultsmentioning

confidence: 99%

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

Bry

2012 IEEE International Conference on Robotics and Automation

Roy

2012

147

104

Abstract-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 fixedwing 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.

“…This includes both fixed-wing vehicles [29], as well as larger scale helicopters [10,28,24]. While vision-based approaches warrant further study, the authors do not address the challenge of agile flight.…”

Section: State Estimationmentioning

confidence: 99%

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

Bry

Richter

The International Journal of Robotics Research

et al. 2015

123

105

In this paper, we describe trajectory planning and state estimation algorithms for aggressive flight of micro aerial vehicles in known, obstacledense 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 time scales for real vehicles and sensors. We first build on previous work of van Nieuwstadt and Murray [51] and Mellinger and Kumar [38], 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 range finder 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.