RANGE - robust autonomous navigation in GPS-denied environments

Bachrach, Abraham; Winter, Anton de; He, Ruijie; Hemann, Garrett; Prentice, Samuel; Roy, Nicholas

doi:10.1109/robot.2010.5509990

Cited by 148 publications

(195 citation statements)

References 5 publications

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“…As such, current applications are limited to situations such as maintenance and building inspection, where the vehicle visits previously explored areas. To enable its use in unknown environments, we are integrating our work in autonomous goal-directed exploration [22], direction understanding with partial information [1], and online object recognition.…”

Section: Discussion and Future Workmentioning

confidence: 99%

Natural language command of an autonomous micro-air vehicle

Huang

Tellex

Bachrach

et al. 2010

2010 IEEE/RSJ International Conference on Intelligent Robots and Systems

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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.

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Section: Discussion and Future Workmentioning

confidence: 99%

Natural language command of an autonomous micro-air vehicle

Huang

Tellex

Bachrach

et al. 2010

2010 IEEE/RSJ International Conference on Intelligent Robots and Systems

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“…This platform was used mostly to perform autonomous exploration and navigation tasks such as the work from Shen et al [22,23], Weiss et al [24], Bachrach et al [4,6,5] and Pravitra et al [21]. The platform offers high performance on-board processors with around 600-650 grams payload.…”

Section: Related Workmentioning

confidence: 99%

Infrastructure Mapping in Well-Structured Environments Using MAV

Fan

Aramrattana

Shahbandi

et al. 2016

Towards Autonomous Robotic Systems

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Abstract. In this paper, we present a design of a surveying system for warehouse environment using low cost quadcopter. The system focus on mapping the infrastructure of surveyed environment. As a unique and essential parts of the warehouse, pillars from storing shelves are chosen as landmark objects for representing the environment. The map are generated based on fusing the outputs of two different methods, point cloud of corner features from Parallel Tracking and Mapping (PTAM) algorithm with estimated pillar position from a multi-stage image analysis method. Localization of the drone relies on PTAM algorithm. The system is implemented in Robot Operating System(ROS) and MATLAB, and has been successfully tested in real-world experiments. The result map after scaling has a metric error less than 20 cm.

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“…This evaluation was performed in a known GPS-denied indoor environment with obstacles (the second floor atrium in the Stata Center at MIT). The hardware platform consisted of Ascending Technologies Pelican quadrotors 1 , each outfitted with a Hokuyo 2 UTM-30LX laser range-finder and 1.6Ghz Intel Atom processor (for details see [17]). Each vehicle performs onboard state estimation and control enabling completely autonomous flight.…”

Section: Hardware Experimentsmentioning

confidence: 99%

Decentralized Control for Optimizing Communication with Infeasible Regions

Gil

Prentice

Roy

et al. 2016

Springer Tracts in Advanced Robotics

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In this paper we present a decentralized gradient-based controller that optimizes communication between mobile aerial vehicles and stationary ground sensor vehicles in an environment with infeasible regions. The formulation of our problem as a MIQP is easily implementable, and we show that the addition of a scaling matrix can improve the range of attainable converged solutions by influencing trajectories to move around infeasible regions. We demonstrate the robustness of the controller in 3D simulation with agent failure, and in 10 trials of a multi-agent hardware experiment with quadrotors and ground sensors in an indoor environment. Lastly, we provide analytical guarantees that our controller strictly minimizes a nonconvex cost along agent trajectories, a desirable property for general multi-agent coordination tasks.

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RANGE - robust autonomous navigation in GPS-denied environments

Cited by 148 publications

References 5 publications

Natural language command of an autonomous micro-air vehicle

Natural language command of an autonomous micro-air vehicle

Infrastructure Mapping in Well-Structured Environments Using MAV

Decentralized Control for Optimizing Communication with Infeasible Regions

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