Forest fire is a significant global natural disaster, and unmanned aerial vehicles (UAVs) have gained attention in wildfire prevention for their efficient and flexible monitoring capabilities. Proper UAV patrol path planning can enhance fire-monitoring accuracy and response speed. This paper proposes a probabilistic path planning (PPP) module that plans UAV patrol paths by combining real-time fire occurrence probabilities at different points. Initially, a forest fire risk logistic regression model is established to compute the fire probabilities at different patrol points. Subsequently, a patrol point filter is applied to remove points with low fire probabilities. Finally, combining fire probabilities with distances between patrol points, a dynamic programming (DP) algorithm is employed to generate an optimal UAV patrol route. Compared with conventional approaches, the experimental results demonstrate that the PPP module effectively improves the timeliness of fire monitoring and containment, and the introduction of DP, considering that the fire probabilities and the patrol point filter both contribute positively to the experimental outcomes. Different combinations of patrol point coordinates and their fire probabilities are further studied to summarize the applicability of this method, contributing to UAV applications in forest fire monitoring and prevention.

The precision and stability of the Robot-Assisted Percutaneous Puncture (RAPP) system have become increasingly crucial with the widespread integration of robotic technology in the field of medicine. The accurate calibration of the RAPP system prior to surgery significantly influences target positioning performance. This study proposes a novel system calibration method that simultaneously addresses system hand–eye calibration and robot kinematic parameters calibration, thereby enhancing the surgery success rate and ensuring patient safety. Initially, a Closed-loop Hand–eye Calibration (CHC) method is employed to rapidly establish transformation relationships among system components. These CHC results are then integrated with nominal robot kinematic parameters to preliminarily determine the system calibration parameters. Subsequently, a hybrid algorithm, combining the regularized Levenberg–Marquardt (LM) algorithm and a particle filtering algorithm, is utilized to accurately estimate the system calibration parameters in stages. Numerical simulations and puncture experiments were conducted using the proposed system calibration method and other comparative methods. The experimental results revealed that, among several comparative methods, the approach presented in this paper yields the greatest improvement in the puncture accuracy of the RAPP system, demonstrating the accuracy and effectiveness of this method. In conclusion, this calibration method significantly contributes to enhancing the precision, operational capability, and safety of the RAPP system in practical applications.