A Single Forward-Velocity Control Signal for Stochastic Source Seeking With Multiple Nonholonomic Vehicles

Author(s):  
Paul Frihauf ◽  
Shu-Jun Liu ◽  
Miroslav Krstic

With a single stochastic extremum seeking control signal, we steer multiple autonomous vehicles, modeled as nonholonomic unicycles, toward the maximum of an unknown, spatially distributed signal field. The vehicles, whose angular velocities are constant and distinct, travel at the same forward velocity, which is controlled by the stochastic extremum seeking controller. To determine the vehicles’ velocity, the controller uses measurements of the signal field at the respective vehicle positions and excitation based on filtered white noise. The positions of the vehicles are not measured. We prove local exponential convergence, both almost surely and in probability, to a small neighborhood near the source and provide a numerical example to illustrate the effectiveness of the algorithm.

Author(s):  
Yuheng Wu ◽  
Mohammad Hazzaz Mahmud ◽  
Radha Sree Krishna Moorthy ◽  
Madhu Chinthavali ◽  
Yue Zhao

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 717
Author(s):  
Farhad Shamsfakhr ◽  
Andrea Motroni ◽  
Luigi Palopoli ◽  
Alice Buffi ◽  
Paolo Nepa ◽  
...  

Autonomous vehicles enable the development of smart warehouses and smart factories with an increased visibility, flexibility and efficiency. Thus, effective and affordable localisation methods for indoor vehicles are attracting interest to implement real-time applications. This paper presents an Extended Kalman Smoother design to both localise a mobile agent and reconstruct its entire trajectory through a sensor-fusion employing the UHF-RFID passive technology. Extensive simulations are carried out by considering the smoother optimal-window length and the effect of missing measurements from reference tags. Monte Carlo simulations are conducted for different vehicle trajectories and for different linear and angular velocities to evaluate the method accuracy. Then, an experimental analysis with a unicycle wheeled robot is performed in real indoor scenario, showing a position and orientation root mean square errors of 15 cm, and 0.2 rad, respectively.


2020 ◽  
Vol 53 (2) ◽  
pp. 1614-1620
Author(s):  
Fabiana Federica Ferro ◽  
Michele Lionello ◽  
Mirco Rampazzo ◽  
Alessandro Beghi ◽  
Martin Guay

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