Sniffy Bug: A Fully Autonomous Swarm of Gas-Seeking Nano Quadcopters in Cluttered Environments

Nano quadcopters are ideal for gas source localization (GSL) as they are safe, agile and inexpensive. However, their extremely restricted sensors and computational resources make GSL a daunting challenge. We propose a novel bug algorithm named ‘Sniffy Bug', which allows a fully autonomous swarm of gas-seeking nano quadcopters to localize a gas source in unknown, cluttered, and GPS-denied environments. The computationally efficient, mapless algorithm foresees in the avoidance of obstacles and other swarm members, while pursuing desired waypoints. The waypoints are first set for exploration, and, when a single swarm member has sensed the gas, by a particle swarm optimization-based (PSO) procedure. We evolve all the parameters of the bug (and PSO) algorithm using our novel simulation pipeline, ‘AutoGDM'. It builds on and expands open source tools in order to enable fully automated end-to-end environment generation and gas dispersion modeling, allowing for learning in simulation. Flight tests show that Sniffy Bug with evolved parameters outperforms manually selected parameters in cluttered, real-world environments. Videos: https://bit.ly/37MmtdL

[1]  Dominique Martinez,et al.  Effectiveness and Robustness of Robot Infotaxis for Searching in Dilute Conditions , 2010, Front. Neurorobot..

[2]  Jian Huang,et al.  Combining particle filter algorithm with bio-inspired anemotaxis behavior: A smoke plume tracking method and its robotic experiment validation , 2020 .

[3]  Wojciech Giernacki,et al.  Crazyflie 2.0 quadrotor as a platform for research and education in robotics and control engineering , 2017, 2017 22nd International Conference on Methods and Models in Automation and Robotics (MMAR).

[4]  Todor Stefanov,et al.  Enabling Cognitive Autonomy on Small Drones by Efficient On-Board Embedded Computing: An ORB-SLAM2 Case Study , 2019, 2019 22nd Euromicro Conference on Digital System Design (DSD).

[5]  Juan D. Tardós,et al.  ORB-SLAM2: An Open-Source SLAM System for Monocular, Stereo, and RGB-D Cameras , 2016, IEEE Transactions on Robotics.

[6]  Alcherio Martinoli,et al.  Understanding the Potential Impact of Multiple Robots in Odor Source Localization , 2008, DARS.

[7]  David Hinkley,et al.  Bootstrap Methods: Another Look at the Jackknife , 2008 .

[8]  Yuyao He,et al.  Collaborative infotaxis: Searching for a signal-emitting source based on particle filter and Gaussian fitting , 2020, Robotics Auton. Syst..

[9]  Sawyer Buckminster Fuller,et al.  A bio-hybrid odor-guided autonomous palm-sized air vehicle , 2020, Bioinspiration & biomimetics.

[10]  Kam K. Leang,et al.  Chemical-Source Localization Using a Swarm of Decentralized Unmanned Aerial Vehicles for Urban/Suburban Environments , 2019 .

[11]  Randall D. Beer,et al.  Evolving Dynamical Neural Networks for Adaptive Behavior , 1992, Adapt. Behav..

[12]  Kartic Subr,et al.  Active Localization of Gas Leaks Using Fluid Simulation , 2019, IEEE Robotics and Automation Letters.

[13]  Michael F. P. O'Boyle,et al.  SLAMBench2: Multi-Objective Head-to-Head Benchmarking for Visual SLAM , 2018, 2018 IEEE International Conference on Robotics and Automation (ICRA).

[14]  Rodney M. Goodman,et al.  Swarm robotic odor localization: Off-line optimization and validation with real robots , 2003, Robotica.

[15]  Javier Gonzalez Monroy,et al.  GADEN: A 3D Gas Dispersion Simulator for Mobile Robot Olfaction in Realistic Environments , 2017, Sensors.

[16]  Guido C. H. E. de Croon,et al.  A Survey on Swarming With Micro Air Vehicles: Fundamental Challenges and Constraints , 2020, Frontiers in Robotics and AI.

[17]  Achim J. Lilienthal,et al.  Smelling Nano Aerial Vehicle for Gas Source Localization and Mapping , 2019, Sensors.

[18]  Philippe Lucas,et al.  Reactive Searching and Infotaxis in Odor Source Localization , 2014, PLoS Comput. Biol..

[19]  Eduardo Izquierdo-Torres,et al.  Analysis of a Dynamical Recurrent Neural Network Evolved for Two Qualitatively Different Tasks: Walking and Chemotaxis , 2008, ALIFE.

[20]  Ming Zeng,et al.  Multi-Robot gas-source localization based on reinforcement learning , 2012, 2012 IEEE International Conference on Robotics and Biomimetics (ROBIO).

[21]  Ryohei Kanzaki,et al.  Synthesis of the pheromone-oriented behavior of silkworm moths by a mobile robot with moth antennae as pheromone sensors , 1999 .

[22]  Guido C. H. E. de Croon,et al.  Tiny Robot Learning (tinyRL) for Source Seeking on a Nano Quadcopter , 2021, 2021 IEEE International Conference on Robotics and Automation (ICRA).

[23]  S. Shen,et al.  Decentralized Visual-Inertial-UWB Fusion for Relative State Estimation of Aerial Swarm , 2020, 2020 IEEE International Conference on Robotics and Automation (ICRA).

[24]  S. Lockery,et al.  Evolution and Analysis of Minimal Neural Circuits for Klinotaxis in Caenorhabditis elegans , 2010, The Journal of Neuroscience.

[25]  W. Jatmiko,et al.  A pso-based mobile robot for odor source localization in dynamic advection-diffusion with obstacles environment: theory, simulation and measurement , 2007, IEEE Computational Intelligence Magazine.

[26]  J. Adler,et al.  The sensing of chemicals by bacteria. , 1976, Scientific American.

[27]  Guido C. H. E. de Croon,et al.  A Comparative Study of Bug Algorithms for Robot Navigation , 2018, Robotics Auton. Syst..

[28]  Lino Marques,et al.  Player/Stage simulation of olfactory experiments , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[29]  Eric O. Postma,et al.  DECA: THE DOPING-DRIVEN EVOLUTIONARY CONTROL ALGORITHM , 2008, Appl. Artif. Intell..

[30]  Hrvoje Jasak,et al.  OpenFOAM: Open source CFD in research and industry , 2009 .

[31]  Dario Izzo,et al.  The Generalized Island Model , 2012, Parallel Architectures and Bioinspired Algorithms.

[32]  Dario Izzo,et al.  Evolutionary robotics approach to odor source localization , 2013, Neurocomputing.

[33]  Massimo Vergassola,et al.  ‘Infotaxis’ as a strategy for searching without gradients , 2007, Nature.

[34]  Isao Shimoyama,et al.  Synthesis of pheromone-oriented emergent behavior of a silkworm moth , 1996, Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. IROS '96.

[35]  G. C. H. E. de Croon,et al.  Minimal navigation solution for a swarm of tiny flying robots to explore an unknown environment , 2019, Science Robotics.