The research of robotic autonomous radioactivity detection or radioactive source search plays an important role in the monitoring and disposal of nuclear safety and biological safety. In this paper, a method for autonomously searching for radioactive sources through mobile robots was proposed. In the method, by using a partially observable Markov decision process (POMDP), the search of autonomous unknown radioactive sources was realized according to a series of radiation information measured by mobile robot. First, the factors affecting the accuracy of radiation measurement during the robot’s movement were analyzed. Based on these factors, the behavior set of POMDP was designed. Secondly, the parameters of the radioactive source were estimated in the Bayesian framework. In addition, through the reward strategy, autonomous navigation of the robot to the position of the radiation source was achieved. The search algorithm was simulated and tested, and the TurtleBot robot platform was used to conduct a real search experiment on the radio source Cs-137 with an activity of 37 MBq indoors. The experimental results showed the effectiveness of the method. Additionally, from the experiments, it could been seen that the robot was affected by the linear velocity, angular velocity, positioning accuracy and the number of measurements in the process of autonomous search for the radioactive source. The proposed mobile robot autonomous search method can be applied to the search for lost radioactive sources, as well as for the leakage of substances (nuclear or chemical) in nuclear power plants and chemical plants.

This letter presents a reconfigurable leader-follower formation approach using relative pose estimation to steer a robot team to maintain a geometry pattern and pass through narrow spaces in unknown environments. The relative pose is estimated using a nonlinear optimization process and the sliding window approach by minimizing the residual error of short-term peer-to-peer UWB (Ultra-Wideband) ranging and odometry. We enhance the estimation accuracy using pose graph optimization by additionally considering odometry as constraints. To pass through narrow areas in unknown environments, we present a pattern reconfiguration mechanism to determine the formation styles (geometry pattern or linear passing pattern) based on environment traversability. A tracking controller is designed to steer the robots to achieve the desired formation style. The effectiveness and performance of the proposed approach are verified by experiments in an indoor environment with ground nonholonomic robots.

In response to the demand for customs detection and tracking of individuals carrying radioactive sources, this paper proposes a method that integrates radiographic imaging information for identifying and tracking carriers. Initially, a portable source personnel visual tracking system equipped with a gamma camera is constructed to capture the position of radioactive sources and images of moving personnel within the security inspection area. Subsequently, a method is developed to link personnel targets identified by both the gamma camera and surveillance cameras, using Euclidean distance and intersecting areas to pinpoint carriers. Furthermore, leveraging the improved RepVGG reparameterization structure and residual networks, we accelerate Re-Identification (RE-ID) inference and propose a Deep Feature Tracking (DFT) method. This method tracks personnel by comparing feature similarities in adjacent frames. Experimental results validate the effectiveness of this method in identifying suspicious individuals carrying radioactive sources and in mapping the walking trajectories of these carriers using pre-deployed surveillance cameras.

In this paper, an intelligent mobile measurement system is proposed to locate multiple unknown radioactive sources in complex environments through dynamic Voronoi partitioning and particle filtering, integrated with a dual-layer data fusion mechanism of Probability of Detection (POD) map guidance and radiation field mapping. Firstly, with each mobile sensor node as a Voronoi centroid, the entire measurement area is dynamically partitioned, enabling concurrent and conflict-free data acquisition within their allocated sub-regions. Then, to resolve issues related to multi-source interference, local optima, and misleading initial intelligence, the dual-layer map-driven mechanism is designed to guide the mobile nodes toward highly uncertain areas. Crucially, the system is explicitly initialized with rough priors containing position deviations and false alarms. By fusing real-time counts per second (CPS) measurements, the nodes autonomously correct these initial errors and construct a global radiation field intensity map in real time. Finally, experiments are conducted to demonstrate the efficiency and accuracy of the proposed multi-sensor measurement method. The simulation shows that localization measurement errors are reduced by approximately 62.5%. Furthermore, in a real-world physical experiment involving three robots and two sources (3V2), the cooperative measurement approach reduces search time from 45 s to 19 s compared to a single-node baseline, achieving robust sub-meter positioning accuracy.

In the early stages of severe nuclear accidents, conventional multi-robot source-seeking approaches based on continuous radiation mapping can become unreliable due to extreme sensing and communication constraints. High-intensity radiation fields may induce detector dead-time saturation and nonlinear response distortions, while continuous data exchange can impose excessive communication burdens in degraded post-disaster environments. To overcome these challenges, this paper proposes a model-free event-triggered extremum-seeking control framework for heterogeneous multi-robot systems operating with binary radiation feedback. By transforming continuous radiation measurements from onboard NaI(Tl) scintillation detectors into sparse threshold-based signals, the proposed approach enables autonomous source seeking without requiring accurate sensor linearity or high-resolution radiation maps. An asynchronous event-triggered communication mechanism is developed to coordinate heterogeneous robotic platforms, including non-holonomic Ackermann-steering vehicles and holonomic quadruped robots, using only discrete state transitions. Based on singular perturbation and averaging theories, a Lyapunov-based stability analysis establishes the convergence properties of the hybrid closed-loop system and guarantees Zeno-free operation. Extensive simulations and cyber-physical experiments using a Cs-137 radioactive source demonstrate that the proposed framework effectively identifies and approaches high-activity regions under severe detector degradation. Furthermore, a hardware-level proxy experiment verifies its robustness against detector dead-time-induced performance degradation. The proposed asynchronous zero-order-hold mechanism reduces communication overhead by 96.9% compared with conventional time-triggered strategies. This work provides a resilient and hardware-efficient solution for autonomous radioactive source seeking in extreme nuclear emergency environments, particularly during the critical early response window when conventional radiation mapping strategies may become physically infeasible.