An Adaptive Memory Model for Long-Term Navigation of Autonomous Mobile Robots

This paper introduces an environmental representation for autonomous mobile robots that continuously adapts over time. The presented approach is inspired by human memory information processing and stores the current as well as past knowledge of the environment. In this paper, the memory model is applied to time-variant information about obstacles and driveable routes in the workspace of the autonomous robot and used for solving the navigation cycle of the robot. This includes localization and path planning as well as vehicle control. The presented approach is evaluated in a real-world experiment within changing indoor environment. The results show that the environmental representation is stable, improves its quality over time, and adapts to changes.

[1]  Wolfram Burgard,et al.  Probabilistic Algorithms and the Interactive Museum Tour-Guide Robot Minerva , 2000, Int. J. Robotics Res..

[2]  Bernardo Wagner,et al.  Robust Self-Localization in Industrial Environments based on 3D Ceiling Structures , 2006, IROS.

[3]  Wolfram Burgard,et al.  Mobile Robot Mapping and Localization in Non-Static Environments , 2005, AAAI.

[4]  W. Burgard,et al.  Markov Localization for Mobile Robots in Dynamic Environments , 1999, J. Artif. Intell. Res..

[5]  Bernardo Wagner,et al.  Adaptive Path Planning for Long-Term Navigation of Autonomous Mobile Robots , 2009, ECMR.

[6]  Tom Duckett,et al.  A multilevel relaxation algorithm for simultaneous localization and mapping , 2005, IEEE Transactions on Robotics.

[7]  Wolfram Burgard,et al.  Temporary maps for robust localization in semi-static environments , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[8]  Bernardo Wagner,et al.  Colored 2D maps for robot navigation with 3D sensor data , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[9]  Bernardo Wagner,et al.  A hybrid feedback controller for car-like robots - combining reactive obstacle avoidance and global replanning , 2007, Integr. Comput. Aided Eng..

[10]  Joachim Hertzberg,et al.  An Explicit Loop Closing Technique for 6D SLAM , 2009, ECMR.

[11]  William Whittaker,et al.  Autonomous driving in urban environments: Boss and the Urban Challenge , 2008 .

[12]  Tom Duckett,et al.  An adaptive appearance-based map for long-term topological localization of mobile robots , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[13]  Wolfram Burgard,et al.  Map building with mobile robots in populated environments , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[14]  Richard C. Atkinson,et al.  Human Memory: A Proposed System and its Control Processes , 1968, Psychology of Learning and Motivation.

[15]  Sebastian Thrun,et al.  Online simultaneous localization and mapping with detection and tracking of moving objects: theory and results from a ground vehicle in crowded urban areas , 2003, 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422).

[16]  Alberto Elfes,et al.  Using occupancy grids for mobile robot perception and navigation , 1989, Computer.