Radiation-Aware Path Planning Framework for Mobile Robots in Dynamic Hazardous Environments

Area of Science:

• Robotics and Automation
• Radiation Physics
• Computational Intelligence

Background:

• Mobile robots are increasingly deployed in hazardous environments with radiation risks.
• Effective path planning must account for radiation sources, shielding, and dynamic obstacles.
• Existing methods often struggle to balance safety, efficiency, and real-time adaptability.

Purpose of the Study:

• To develop a novel radiation-aware path planning framework for mobile robots.
• To integrate multiple optimization techniques for enhanced route planning in radiation fields.
• To evaluate the framework's performance against established methods in static and dynamic scenarios.

Main Methods:

• A unified dynamic planning architecture combining A*-based global planning and chaotic particle swarm optimization (CPSO) for route refinement.
• Physically parameterized dose-rate map generation incorporating inverse-square law and material attenuation.
• B-Spline trajectory smoothing and exposure-dependent speed adaptation.
• Comparative analysis against Pure A*, Informed RRT*, A*-PSO, A*-CPSO, and A*+DWA baselines.

Main Results:

• Achieved significant reductions in cumulative absorbed dose: ~33.3% (low-risk), ~47.2% (medium-risk), ~21.4% (high-risk) in static scenarios compared to Pure A*.
• ~32.5% (low-risk), ~41.4% (medium-risk), ~40.1% (high-risk) dose reduction in dynamic scenarios.
• Demonstrated a balanced trade-off between dose reduction, trajectory feasibility, mission time, and replanning performance.

Conclusions:

• The proposed radiation-aware path planning framework effectively minimizes radiation exposure for mobile robots.
• The integration of CPSO and exposure-dependent speed adaptation enhances safety and efficiency in hazardous environments.
• The framework offers a computationally feasible and robust solution for real-world robotic applications in radiation fields.