An Integrated Strategy for Autonomous Exploration of Spatial Processes in Unknown Environments

Exploration of spatial processes, such as radioactivity or temperature is a fundamental task in many robotic applications. In the literature, robotic exploration is mainly carried out for applications where the environment is a priori known. However, for most real life applications this assumption often does not hold, specifically for disaster scenarios. In this paper, we propose a novel integrated strategy that allows a robot to explore a spatial process of interest in an unknown environment. To this end, we build upon two major blocks. First, we propose the use of GP to model the spatial process of interest, and process entropy to drive the exploration. Second, we employ registration algorithms for robot mapping and localization, and frontier-based exploration to explore the environment. However, map and process exploration can be conflicting goals. Our integrated strategy fuses the two aforementioned blocks through a trade-off between process and map exploration. We carry out extensive evaluations of our algorithm in simulated environments with respect to different baselines and environment setups using simulated GP data as a process at hand. Additionally, we perform experimental verification with a mobile holonomic robot exploring a simulated process in an unknown labyrinth environment. Demonstrated results show that our integrated strategy outperforms both frontier-based and GP entropy-driven exploration strategies.

[1]  Nils J. Nilsson,et al.  A Formal Basis for the Heuristic Determination of Minimum Cost Paths , 1968, IEEE Trans. Syst. Sci. Cybern..

[2]  Ian D. Reid,et al.  On the comparison of uncertainty criteria for active SLAM , 2012, 2012 IEEE International Conference on Robotics and Automation.

[3]  Kam K. Leang,et al.  Autonomous Chemical-Sensing Aerial Robot for Urban/Suburban Environmental Monitoring , 2019, IEEE Systems Journal.

[4]  Sebastian Thrun,et al.  Probabilistic robotics , 2002, CACM.

[5]  Brendan Englot,et al.  LeGO-LOAM: Lightweight and Ground-Optimized Lidar Odometry and Mapping on Variable Terrain , 2018, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[6]  Francesco Amigoni,et al.  A Multi-Objective Exploration Strategy for Mobile Robots , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[7]  Roland Siegwart,et al.  Comparing ICP variants on real-world data sets , 2013, Auton. Robots.

[8]  Renaud Dubé,et al.  SegMap: 3D Segment Mapping using Data-Driven Descriptors , 2018, Robotics: Science and Systems.

[9]  Andreas Krause,et al.  Near-Optimal Sensor Placements in Gaussian Processes: Theory, Efficient Algorithms and Empirical Studies , 2008, J. Mach. Learn. Res..

[10]  Andreas Krause,et al.  A tutorial on Gaussian process regression: Modelling, exploring, and exploiting functions , 2016, bioRxiv.

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

[12]  Nicola Basilico,et al.  Exploration strategies based on multi-criteria decision making for searching environments in rescue operations , 2011, Auton. Robots.

[13]  Daniele Nardi,et al.  Multi‐objective exploration and search for autonomous rescue robots , 2007, J. Field Robotics.

[14]  Ji Zhang,et al.  LOAM: Lidar Odometry and Mapping in Real-time , 2014, Robotics: Science and Systems.

[15]  Hyun Myung,et al.  Magnetic field constraints and sequence-based matching for indoor pose graph SLAM , 2015, Robotics Auton. Syst..

[16]  Wolfram Burgard,et al.  Speeding-Up Robot Exploration by Exploiting Background Information , 2016, IEEE Robotics and Automation Letters.

[17]  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'.

[18]  Christopher K. I. Williams,et al.  Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning) , 2005 .

[19]  Torsten Bertram,et al.  Integrated online trajectory planning and optimization in distinctive topologies , 2017, Robotics Auton. Syst..

[20]  Haitao Liu,et al.  Generalized Robust Bayesian Committee Machine for Large-scale Gaussian Process Regression , 2018, ICML.

[21]  Alexei Makarenko,et al.  Information based adaptive robotic exploration , 2002, IEEE/RSJ International Conference on Intelligent Robots and Systems.

[22]  SiegwartRoland,et al.  Comparing ICP variants on real-world data sets , 2013 .

[23]  Jan Faigl,et al.  Online Incremental Learning of the Terrain Traversal Cost in Autonomous Exploration , 2019, Robotics: Science and Systems.

[24]  Cipriano Galindo,et al.  Integrating SLAM into gas distribution mapping , 2007 .

[25]  Xiaolei Ma,et al.  Vehicle Routing Problem , 2013 .

[26]  Gal A. Kaminka,et al.  Efficient frontier detection for robot exploration , 2014, Int. J. Robotics Res..

[27]  Christoph Manss,et al.  Decentralized multi-agent exploration with online-learning of Gaussian processes , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).

[28]  J. MacQueen Some methods for classification and analysis of multivariate observations , 1967 .

[29]  Hugh F. Durrant-Whyte,et al.  Modeling and decision making in spatio-temporal processes for environmental surveillance , 2010, 2010 IEEE International Conference on Robotics and Automation.

[30]  Fabio Tozeto Ramos,et al.  Bayesian Optimisation for informative continuous path planning , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

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

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

[33]  Vijay Kumar,et al.  Online methods for radio signal mapping with mobile robots , 2010, 2010 IEEE International Conference on Robotics and Automation.

[34]  Wolfram Burgard,et al.  Exploring Unknown Environments with Mobile Robots using Coverage Maps , 2003, IJCAI.

[35]  Alessandro Farinelli,et al.  Multi-objective exploration and search for autonomous rescue robots: Research Articles , 2007 .

[36]  Jaime Valls Miró,et al.  Exploration on continuous Gaussian process frontier maps , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[37]  Benjamin Bird,et al.  A Robot to Monitor Nuclear Facilities: Using Autonomous Radiation-Monitoring Assistance to Reduce Risk and Cost , 2019, IEEE Robotics & Automation Magazine.

[38]  Mac Schwager,et al.  Distributed robotic sensor networks: An information-theoretic approach , 2012, Int. J. Robotics Res..

[39]  John J. Leonard,et al.  Past, Present, and Future of Simultaneous Localization and Mapping: Toward the Robust-Perception Age , 2016, IEEE Transactions on Robotics.

[40]  Dmitriy Shutin,et al.  Robotic Active Information Gathering for Spatial Field Reconstruction with Rapidly-Exploring Random Trees and Online Learning of Gaussian Processes † , 2019, Sensors.

[41]  G. Choquet Theory of capacities , 1954 .

[42]  Libor Preucil,et al.  On distance utility in the exploration task , 2011, 2011 IEEE International Conference on Robotics and Automation.

[43]  Libor Preucil,et al.  An Integrated Approach to Goal Selection in Mobile Robot Exploration , 2019, Sensors.

[44]  Rafael Valencia Carreño Mapping, planning and exploration with Pose SLAM , 2013 .