Reactive Control and Metric-Topological Planning for Exploration

Autonomous navigation in unknown environments with the intent of exploring all traversable areas is a significant challenge for robotic platforms. In this paper, a simple yet reliable method for exploring unknown environments is presented based on bio-inspired reactive control and metric-topological planning. The reactive control algorithm is modeled after the spatial decomposition of wide and small-field patterns of optic flow in the insect visuomotor system. Centering behaviour and small obstacle detection and avoidance are achieved through wide-field integration and Fourier residual analysis of instantaneous measured nearness respectively. A topological graph is estimated using image processing techniques on a continuous occupancy grid. Node paths are rapidly generated to navigate to the nearest unexplored edge in the graph. It is shown through rigorous field-testing that the proposed control and planning method is robust, reliable, and computationally efficient.

[1]  Vijay Kumar,et al.  Autonomous robotic exploration using a utility function based on Rényi’s general theory of entropy , 2017, Autonomous Robots.

[2]  Wolfgang Hess,et al.  Real-time loop closure in 2D LIDAR SLAM , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[3]  Cyrill Stachniss,et al.  Information-Driven Autonomous Exploration for a Vision-Based Mav , 2017 .

[4]  Michael Jenkin,et al.  Robotic exploration as graph construction , 1991, IEEE Trans. Robotics Autom..

[5]  Michael H Dickinson,et al.  Fly Flight A Model for the Neural Control of Complex Behavior , 2001, Neuron.

[6]  A. Borst,et al.  Neural networks in the cockpit of the fly , 2002, Journal of Comparative Physiology A.

[7]  Sebastian Thrun,et al.  Learning Metric-Topological Maps for Indoor Mobile Robot Navigation , 1998, Artif. Intell..

[8]  F. Ruffier,et al.  Optic flow-based collision-free strategies: From insects to robots. , 2017, Arthropod structure & development.

[9]  Wolfram Burgard,et al.  Coordinated multi-robot exploration using a segmentation of the environment , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[10]  Wolfram Burgard,et al.  Autonomous exploration and mapping of abandoned mines , 2004, IEEE Robotics & Automation Magazine.

[11]  A. Matveev,et al.  Algorithms for collision-free navigation of mobile robots in complex cluttered environments: a survey , 2014, Robotica.

[12]  Roland Siegwart,et al.  Sparse 3D Topological Graphs for Micro-Aerial Vehicle Planning , 2018, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[13]  M. Srinivasan,et al.  Visual motor computations in insects. , 2004, Annual review of neuroscience.

[14]  Wenzhe Li,et al.  Room segmentation: Survey, implementation, and analysis , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[15]  William Whittaker,et al.  Autonomous exploration using multiple sources of information , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[16]  Juha Hyyppä,et al.  ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences , 2016 .

[17]  Wolfram Burgard,et al.  Coordination for Multi-Robot Exploration and Mapping , 2000, AAAI/IAAI.

[18]  Roland Siegwart,et al.  Receding horizon path planning for 3D exploration and surface inspection , 2018, Auton. Robots.

[19]  James Sean Humbert,et al.  Bioinspired Visuomotor Convergence , 2010, IEEE Transactions on Robotics.

[20]  Giulio Sandini,et al.  Embedded visual behaviors for navigation , 1997, Robotics Auton. Syst..

[21]  Randy Beard,et al.  Maximizing Miniature Aerial Vehicles Obstacle and Terrain Avoidance for MAVs , 2006 .

[22]  Brian Yamauchi,et al.  A frontier-based approach for autonomous exploration , 1997, Proceedings 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation CIRA'97. 'Towards New Computational Principles for Robotics and Automation'.

[23]  Gaurav S. Sukhatme,et al.  A probabilistic framework for next best view estimation in a cluttered environment , 2014, J. Vis. Commun. Image Represent..

[24]  Vijay Kumar,et al.  Information-theoretic mapping using Cauchy-Schwarz Quadratic Mutual Information , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[25]  Hector D. Escobar-Alvarez,et al.  Autonomous Bio-Inspired Small-Object Detection and Avoidance , 2018, 2018 IEEE International Conference on Robotics and Automation (ICRA).

[26]  Geoffrey A. Hollinger,et al.  Distributed inference-based multi-robot exploration , 2018, Auton. Robots.

[27]  Yi Lin,et al.  Online Safe Trajectory Generation for Quadrotors Using Fast Marching Method and Bernstein Basis Polynomial , 2018, 2018 IEEE International Conference on Robotics and Automation (ICRA).

[28]  Svetha Venkatesh,et al.  Robot navigation inspired by principles of insect vision , 1999, Robotics Auton. Syst..

[29]  Ching Y. Suen,et al.  A fast parallel algorithm for thinning digital patterns , 1984, CACM.