Landmark-based Exploration with Swarm of Resource Constrained Robots

In this paper we consider the problem of autonomous exploration of an unknown, GPS-denied environment using a swarm of robots with very limited resources and limited sensing capabilities. To that end we use a landmark complex, a simplicial complex utilizing an observation of landmarks, as a topological representation of the environment. Each robot is equipped with an omni-directional, limited-range sensor that can identify landmarks in the robot's neighborhood. The robots use the bearing angles to the landmarks for local navigation. Given a collection of identifiable landmarks, a landmark complex can then be cumulatively constructed to encapsulate the topological information of the environment. Under the assumption of sufficiently dense landmarks, we propose an exploration and exploitation strategy that guides the swarm of robots to explore an environment using only bearing measurements without any metric information. Lastly, we demonstrate the coordinate-free and localization-free navigation in the environment using the constructed landmark complex.

[1]  Hugh F. Durrant-Whyte,et al.  Simultaneous localization and mapping: part I , 2006, IEEE Robotics & Automation Magazine.

[2]  Jean-Arcady Meyer,et al.  Fast and Incremental Method for Loop-Closure Detection Using Bags of Visual Words , 2008, IEEE Transactions on Robotics.

[3]  Olivier Stasse,et al.  MonoSLAM: Real-Time Single Camera SLAM , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  J. M. M. Montiel,et al.  The SPmap: a probabilistic framework for simultaneous localization and map building , 1999, IEEE Trans. Robotics Autom..

[5]  Wolfram Burgard,et al.  Coordinated multi-robot exploration , 2005, IEEE Transactions on Robotics.

[6]  Keiji Nagatani,et al.  Topological simultaneous localization and mapping (SLAM): toward exact localization without explicit localization , 2001, IEEE Trans. Robotics Autom..

[7]  Andrew Howard,et al.  Multi-robot Simultaneous Localization and Mapping using Particle Filters , 2005, Int. J. Robotics Res..

[8]  Nikolaus Correll,et al.  Probabilistic Modeling of Swarming Systems , 2015, Handbook of Computational Intelligence.

[9]  J. Derenick,et al.  Topological Landmark-based Navigation and Mapping , 2012 .

[10]  Sebastian Thrun,et al.  FastSLAM: a factored solution to the simultaneous localization and mapping problem , 2002, AAAI/IAAI.

[11]  C. H. Dowker HOMOLOGY GROUPS OF RELATIONS , 1952 .

[12]  Vijay Kumar,et al.  Biologically inspired bearing-only navigation and tracking , 2007, 2007 46th IEEE Conference on Decision and Control.

[13]  Spring Berman,et al.  Coverage and field estimation on bounded domains by diffusive swarms , 2016, 2016 IEEE 55th Conference on Decision and Control (CDC).

[14]  Antonis A. Argyros,et al.  Robot Homing by Exploiting Panoramic Vision , 2005, Auton. Robots.

[15]  W.H. Tang,et al.  Optimal Harmonic Estimation Using A Particle Swarm Optimizer , 2008, IEEE Transactions on Power Delivery.

[16]  Sebastian Thrun,et al.  Multi-robot SLAM with Sparse Extended Information Filers , 2003, ISRR.

[17]  Spring Berman,et al.  Optimal control of stochastic coverage strategies for robotic swarms , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).