A Combination-Driven Radiation Source Seeking Algorithm Based on Improved SPSA

Abstract

In response to the challenges of radiation source localization in shielded environments, a combination-driven algorithm is proposed based on an improved simultaneous perturbation stochastic approximation (SPSA) framework. The proposed algorithm enhances the convergence performance by optimizing one-sided orthogonal perturbation vectors and incorporating a dynamic step-size adjustment strategy. To address path infeasibility in complex environments, a goal relocation mechanism based on breadth-first search (BFS) is introduced. For complex multi-space scenarios with shielding structures, an active exploration strategy is employed to proactively investigate unobserved regions. To evaluate the performance of the proposed algorithm in terms of convergence performance, obstacle adaptability, and exploration capacity, three simulation environments are set up in Gazebo, which respectively represent the following environments: open-field environment, complex environment and radiation shielding environment. The experiments demonstrate that the proposed algorithm outperforms the standard SPSA algorithm and particle filter algorithm in terms of convergence performance, while also exhibiting robust search capability in complex environments. Furthermore, when confronted with shielded environments, the proposed algorithm demonstrates the capability of actively exploring unknown areas within a relatively short period after being obstructed.

Keywords

source seeking algorithm / simultaneous perturbation stochastic approximation / combination-driven algorithm / active exploration

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