Source-Seeking Approach with Non-Reversing Forward Velocity Regulation via Multi-Sensor Feedback

Source-Seeking in unknown scalar fields is a fundamental problem in robotics with applications in environmental monitoring and disaster response. In this work, we present a source-seeking approach with non-reversing forward velocity regulation by fusing measurement data from multiple sensors within the Stochastic Extremum Seeking (SES) framework. Specifically, a device model with multiple sensors is first constructed, and then a velocity regulation scheme is designed by leveraging the boundedness of the hyperbolic tangent function and the non-negativity of the exponential function to guarantee strictly positive forward velocity. We then evaluate the algorithm both in simulation environments and on the real-world Two-Wheeled Differential Drive Robot platform. The experiments show that our approach not only ensures the forward velocity remains non-negative, aligning with the design expectation, but also accurately locates the source. This work provides new insights into the design of velocity regulation strategies within the SES framework.

Recommended citation: Q. Sun, G. Li, J. Shen, R. Wu, W. Zhang, and M. Geng, ‘Source-Seeking Approach with Non-Reversing Forward Velocity Regulation via Multi-Sensor Feedback’, Mathematics, vol. 14, no. 13, 2026.
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