A Real-Time Path Planning Algorithm in Radiation Environments Based on an Improved A* Combined with the Dynamic Window Approach

In emergency scenarios such as nuclear radiation accidents, mobile robots are often deployed for tasks including detection, search and rescue, and environmental assessment. To mitigate radiation-induced damage to onboard components, the cumulative radiation dose is commonly incorporated as an optimization objective in path planning. However, the highly uncertain radiation field and the frequent emergence of unknown obstacles during such incidents pose significant challenges for traditional path planning methods, which struggle to generate optimal paths and implement efficient, reliable obstacle avoidance strategies. To address these issues, this study proposes a real-time path planning method for radiation environments [Real-Time A* algorithm with Dynamic Window Approach (RTAD)] based on an improved A* algorithm combined with the Dynamic Window Approach (DWA). The method jointly optimizes the cost function of the A* algorithm by integrating path length, cumulative radiation dose, and energy consumption, enhanced through a multiscale evaluation mechanism to improve planning accuracy. Additionally, the improved DWA algorithm incorporates a radiation-aware evaluation function, adaptive weight adjustment strategy, and speed variation penalty term, enabling effective real-time obstacle avoidance in complex nuclear radiation environments. Simulation results demonstrate that compared to the conventional A*-DWA approach, the proposed RTAD algorithm reduces cumulative radiation dose by approximately 23.1%, increases average movement speed by 28.6%, and decreases planning time by 23.4%.