Source-Seeking Control of Unicycle Robots With 3-D-Printed Flexible Piezoresistive Sensors

We present the design and experimental validation of source seeking control algorithms for a unicycle mobile robot that is equipped with novel 3D-printed flexible graphenebased piezoresistive airflow sensors. Based solely on a local gradient measurement from the airflow sensors, we propose and analyze a projected gradient ascent algorithm to solve the source seeking problem. In the case of partial sensor failure, we propose a combination of Extremum-Seeking Control with our projected gradient ascent algorithm. For both control laws, we prove the asymptotic convergence of the robot to the source. Numerical simulations were performed to validate the algorithms and experimental validations are presented to demonstrate the efficacy of the proposed methods.

[1]  Petter Ögren,et al.  Cooperative control of mobile sensor networks:Adaptive gradient climbing in a distributed environment , 2004, IEEE Transactions on Automatic Control.

[2]  Sebastian Madgwick,et al.  Estimation of IMU and MARG orientation using a gradient descent algorithm , 2011, 2011 IEEE International Conference on Rehabilitation Robotics.

[3]  Achim J. Lilienthal,et al.  The 3D-Kernel DM+V/W algorithm: Using wind information in three dimensional gas distribution modelling with a mobile robot , 2010, 2010 IEEE Sensors.

[4]  D. Yoerger,et al.  Gradient search with autonomous underwater vehicles using scalar measurements , 1996, Proceedings of Symposium on Autonomous Underwater Vehicle Technology.

[5]  Miroslav Krstic,et al.  Extremum seeking for dynamic maps using Lie brackets and singular perturbations , 2017, Autom..

[6]  Ümit Özgüner,et al.  Extremum-Seeking Control in Constrained Source Tracing With Nonholonomic Vehicles , 2009, IEEE Transactions on Industrial Electronics.

[7]  Ajay Giri Prakash Kottapalli,et al.  Sensing the flow beneath the fins , 2018, Bioinspiration & biomimetics.

[8]  Miroslav Krstic,et al.  Extremum seeking with bounded update rates , 2014, Syst. Control. Lett..

[9]  Miroslav Krstic,et al.  Speed regulation in steering-based source seeking , 2010, Autom..

[10]  Cheng Wu,et al.  3-D Velocity Regulation for Nonholonomic Source Seeking Without Position Measurement , 2015, IEEE Transactions on Control Systems Technology.

[11]  Jorge Cortés,et al.  Distributed gradient ascent of random fields by robotic sensor networks , 2007, 2007 46th IEEE Conference on Decision and Control.

[12]  Oussama Khatib,et al.  Real-Time Obstacle Avoidance for Manipulators and Mobile Robots , 1985, Autonomous Robot Vehicles.

[13]  Miroslav Krstic,et al.  Stochastic source seeking for nonholonomic unicycle , 2010, Autom..

[14]  Mohsen Mojiri,et al.  Normalized Extremum Seeking and Its Application to Nonholonomic Source Localization , 2021, IEEE Transactions on Automatic Control.

[15]  Carlos Canudas de Wit,et al.  Source localization by gradient estimation based on Poisson integral , 2014, Autom..

[16]  Miroslav Krstic,et al.  Stochastic source seeking with forward and angular velocity regulation , 2016, Autom..

[17]  S. Held,et al.  Influence of the Cooling System and Road Topology on Heavy Duty Truck Platooning , 2019, 2019 IEEE Intelligent Transportation Systems Conference (ITSC).

[18]  Miroslav Krstic,et al.  Nonholonomic Source Seeking With Tuning of Angular Velocity , 2009, IEEE Transactions on Automatic Control.

[19]  Miroslav Krstić,et al.  Overshoot-Free Steering-Based Source Seeking , 2017, IEEE Transactions on Control Systems Technology.

[20]  George J. Pappas,et al.  Stochastic Source Seeking by Mobile Robots , 2012, IEEE Transactions on Automatic Control.

[21]  R. Andrew Russell,et al.  Improving the robustness of Na?ve Physics airflow mapping, using Bayesian reasoning on a multiple hypothesis tree , 2006, ROBIO.

[22]  Michael S. Triantafyllou,et al.  Cupula-Inspired Hyaluronic Acid-Based Hydrogel Encapsulation to Form Biomimetic MEMS Flow Sensors , 2017, Sensors.

[23]  Arun Kumar Sangaiah,et al.  Obstacle avoidance of mobile robots using modified artificial potential field algorithm , 2019, Other Conferences.

[24]  Yutaka Nakamura,et al.  Yuragi-based adaptive searching behavior in mobile robot: From bacterial chemotaxis to Levy walk , 2009, 2008 IEEE International Conference on Robotics and Biomimetics.

[25]  R. Andrew Russell,et al.  Improving the robustness of Naïve Physics airflow mapping, using Bayesian reasoning on a multiple hypothesis tree , 2006, 2006 IEEE International Conference on Robotics and Biomimetics.

[26]  Alexander Scheinker,et al.  Bounded extremum seeking with discontinuous dithers , 2016, Autom..

[27]  A S Matveev,et al.  Navigation of a non-holonomic vehicle for gradient climbing and source seeking without gradient estimation , 2010, Proceedings of the 2010 American Control Conference.

[28]  P. Olver.  Nonlinear Systems , 2013 .

[29]  Amar M. Kamat,et al.  PDMS Flow Sensors With Graphene Piezoresistors Using 3D-Printing and Soft Lithography , 2020, 2020 IEEE Sensors.

[30]  G. Wadhams,et al.  Making sense of it all: bacterial chemotaxis , 2004, Nature Reviews Molecular Cell Biology.

[31]  Shuo Pang,et al.  Chemical Plume Source Localization , 2006, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[32]  J. Baillieul,et al.  Autonomous vehicle control for ascending/descending along a potential field with two applications , 2008, 2008 American Control Conference.

[33]  Frank W. Grasso,et al.  Biomimetic robot lobster performs chemo-orientation in turbulence using a pair of spatially separated sensors: Progress and challenges , 2000, Robotics Auton. Syst..

[34]  Arye Nehorai,et al.  Localizing vapor-emitting sources by moving sensors , 1996, IEEE Trans. Signal Process..

[35]  Naomi Ehrich Leonard,et al.  Vehicle networks for gradient descent in a sampled environment , 2002, Proceedings of the 41st IEEE Conference on Decision and Control, 2002..

[36]  Milos S. Stankovic,et al.  Lie bracket approximation of extremum seeking systems , 2011, Autom..

[37]  Amar M. Kamat,et al.  Bioinspired PDMS-graphene cantilever flow sensors using 3D printing and replica moulding , 2020, Nanotechnology.

[38]  Miroslav Krstic,et al.  Source seeking with non-holonomic unicycle without position measurement and with tuning of forward velocity , 2007, Syst. Control. Lett..

[39]  Kay Chen Tan,et al.  Evolutionary artificial potential fields and their application in real time robot path planning , 2000, Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No.00TH8512).

[40]  Yong-Lin Kuo,et al.  Force/Position Hybrid Control for a Hexa Robot Using Gradient Descent Iterative Learning Control Algorithm , 2019, IEEE Access.

[41]  Achim J. Lilienthal,et al.  From Insects to Micro Air Vehicles - A Comparison of Reactive Plume Tracking Strategies , 2014, IAS.

[42]  A. Kottapalli,et al.  Fish-inspired self-powered microelectromechanical flow sensor with biomimetic hydrogel cupula , 2017 .

[43]  Bing Qiao,et al.  Kinematic Model of a Four Mecanum Wheeled Mobile Robot , 2015 .

[44]  Jeffrey S. Kolski,et al.  In-hardware demonstration of model-independent adaptive tuning of noisy systems with arbitrary phase drift , 2014 .

[45]  Mac Schwager,et al.  Decentralized path planning for coverage tasks using gradient descent adaptive control , 2014, Int. J. Robotics Res..

[46]  Carlos Canudas-de-Wit,et al.  Source seeking via collaborative measurements by a circular formation of agents , 2010, Proceedings of the 2010 American Control Conference.