An exploration-based approach to terrain traversability assessment for a walking robot

In this paper we present a traversability assessment method for motion planning in autonomous walking robots. The aim is to plan the motion of the robot in a real scenario on a rough terrain, where the level of details in the obtained terrain maps is not sufficient for motion planning. A guided RRT (Rapidly-exploring Random Trees) algorithm is used to plan the motion of the robot on rough terrain. We are looking for a method that can learn the terrain traversability cost function to the benefit of the guiding function of the planning algorithm. A probabilistic regression technique is used to solve the traversability assessment problem. Computing the predictions of the traversability values we use the RRT planner to explore the space of possible solutions. We demonstrate efficiency of the prediction method and we show results of experiments on the real walking robot.

[1]  Piotr Skrzypczynski,et al.  Posture optimization strategy for a statically stable robot traversing rough terrain , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[2]  Dominik Belter.  Perception-Based Motion Planning for a Walking Robot in Rugged Terrain , 2012 .

[3]  Jaime Valls Miró,et al.  Planning high-visibility stable paths for reconfigurable robots on uneven terrain , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[4]  Homayoun Seraji,et al.  Vision-based terrain characterization and traversability assessment , 2001, J. Field Robotics.

[5]  YE Cang,et al.  Enhancing Mobile Robot Navigation in Urban Environments by a Polar Traversability Index , 2007 .

[6]  Larry D. Hostetler,et al.  The estimation of the gradient of a density function, with applications in pattern recognition , 1975, IEEE Trans. Inf. Theory.

[7]  Kurt Konolige,et al.  Small Vision Systems: Hardware and Implementation , 1998 .

[8]  Heiko Hirschmüller,et al.  Stereo-vision-based navigation of a six-legged walking robot in unknown rough terrain , 2012, Int. J. Robotics Res..

[9]  Thierry Siméon,et al.  Motion generation for a rover on rough terrains , 2001, Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems. Expanding the Societal Role of Robotics in the the Next Millennium (Cat. No.01CH37180).

[10]  Piotr Skrzypczynski,et al.  Rough terrain mapping and classification for foothold selection in a walking robot , 2010, 2010 IEEE Safety Security and Rescue Robotics.

[11]  Panagiotis Papadakis,et al.  Terrain traversability analysis methods for unmanned ground vehicles: A survey , 2013, Eng. Appl. Artif. Intell..

[12]  Fiora Pirri,et al.  3D Mobility Learning and Regression of Articulated, Tracked Robotic Vehicles by Physics-based Optimization , 2012, VRIPHYS.

[13]  J. Koláček,et al.  Nonparametric Conditional Density Estimation , 2013 .

[14]  James S. Albus,et al.  Learning traversability models for autonomous mobile vehicles , 2008, Auton. Robots.

[15]  Piotr Skrzypczynski,et al.  Precise self-localization of a walking robot on rough terrain using parallel tracking and mapping , 2013, Ind. Robot.

[16]  Takafumi Kanamori,et al.  Nonparametric Conditional Density Estimation Using Piecewise-Linear Solution Path of Kernel Quantile Regression , 2009, Neural Computation.

[17]  Daniel E. Koditschek,et al.  RHex: A Simple and Highly Mobile Hexapod Robot , 2001, Int. J. Robotics Res..

[18]  Edward Tunstel,et al.  Soft computing for visual terrain perception and traversability assessment by planetary robotic systems , 2005, 2005 IEEE International Conference on Systems, Man and Cybernetics.

[19]  Dolores Blanco,et al.  Outdoor Motion Planning Using Fast Marching , 2009 .

[20]  Libor Preucil,et al.  RRT-path – A Guided Rapidly Exploring Random Tree , 2009 .

[21]  Piotr Skrzypczynski,et al.  Estimating terrain elevation maps from sparse and uncertain multi-sensor data , 2012, 2012 IEEE International Conference on Robotics and Biomimetics (ROBIO).