Cohesive Autonomous Navigation System

The ability for a robotic system to fully and autonomously interact with its environment is key to the future of applications such as commercial package delivery services, elderly robotic assistants, agricultural monitoring systems, natural disaster search and rescue robots, civil construction monitoring systems, robotic satellite servicing, and many more. An architecture that is conducive to Simultaneous Localization And Mapping (SLAM), path planning, and mission planning is a critical element of a system to be robust enough to handle such applications with true autonomy. In this paper we present an architecture that lends itself to such cohesive operation of all the aforementioned goals through the implementation of a common core database to represent the environment. We present the overall architecture followed by a description of the components of the architecture and how they interact, including: a demonstration of image processing techniques using geographic information science (GIS) analytical methods and ellipsoid feature models, an explanation of database management tools using k-vector, an outline of the SLAM approach, and a description of the path planning algorithm employed.

[1]  G. Evensen.  The ensemble Kalman filter for combined state and parameter estimation , 2009, IEEE Control Systems.

[2]  Jonathan P. How,et al.  Aircraft trajectory planning with collision avoidance using mixed integer linear programming , 2002, Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301).

[3]  Lawrence J. DeLucas,et al.  International space station , 1996 .

[4]  Luc Jaulin.  A Nonlinear Set Membership Approach for the Localization and Map Building of Underwater Robots , 2009, IEEE Transactions on Robotics.

[5]  A. Robertson.  The CIE 1976 Color-Difference Formulae , 1977 .

[6]  Jeffrey S. Kargel,et al.  Multispectral imaging contributions to global land ice measurements from space , 2005 .

[7]  E. Kraft,et al.  A quaternion-based unscented Kalman filter for orientation tracking , 2003, Sixth International Conference of Information Fusion, 2003. Proceedings of the.

[8]  Burkhard Wünsche,et al.  Using the Kinect as a navigation sensor for mobile robotics , 2012, IVCNZ '12.

[9]  Steve Ulrich,et al.  Admissible Subspace TRajectory Optimizer (ASTRO) for Autonomous Robot Operations on the Space Station , 2014 .

[10]  Gordon Wyeth,et al.  Persistent Navigation and Mapping using a Biologically Inspired SLAM System , 2010, Int. J. Robotics Res..

[11]  Lydia E. Kavraki,et al.  The Open Motion Planning Library , 2012, IEEE Robotics & Automation Magazine.

[12]  Alvar Saenz-Otero,et al.  The SPHERES ISS Laboratory for Rendezvous and Formation Flight , 2003 .

[13]  K.A. Morgansen,et al.  Decentralized reactive collision avoidance for multiple unicycle-type vehicles , 2008, 2008 American Control Conference.

[14]  Wolfram Burgard,et al.  An evaluation of the RGB-D SLAM system , 2012, 2012 IEEE International Conference on Robotics and Automation.

[15]  Manuela M. Veloso,et al.  Real-Time Randomized Path Planning for Robot Navigation , 2002, RoboCup.

[16]  Michael P. Bishop,et al.  Characterizing instability of aeolian environments using analytical reasoning , 2015 .

[17]  R. Olfati-Saber,et al.  Collision avoidance for multiple agent systems , 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).

[18]  Ronald Parr,et al.  DP-SLAM: fast, robust simultaneous localization and mapping without predetermined landmarks , 2003, IJCAI 2003.

[19]  Manop Wongsaisuwan,et al.  A study on Unscented SLAM with path planning algorithm integration , 2014, 2014 11th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON).

[20]  Daniel P. Huttenlocher,et al.  Efficient Graph-Based Image Segmentation , 2004, International Journal of Computer Vision.