Path Planning Strategies in Radioactive Environments

Path planning in radioactive settings requires the integration of spatial navigation algorithms with models of ionising radiation to ensure that robots or human operators can traverse hazardous zones while keeping cumulative exposure within safe limits. Traditional graph-based search methods such as A* and Dijkstra’s algorithm have been adapted to incorporate radiation dose as an additional cost dimension, yielding shortest-path solutions that also minimise exposure. Beyond deterministic searches, potential-field approaches generate continuous risk gradients to steer agents away from high-dose regions. In recent years, metaheuristic and bio-inspired techniques—including genetic algorithms, particle swarm optimisation and ant colony methods—have been employed to tackle the multi-objective nature of the problem, balancing path length, obstacle avoidance and dose accumulation. Advances in simulation and 3D risk mapping permit the real-time visualisation of radiation fields, enabling dynamic replanning in changing environments. Furthermore, reinforcement-learning frameworks have been introduced to allow agents to learn optimal navigation policies through trial-and-error within virtual radioactive terrains. Collectively, these strategies support a wide range of applications, from nuclear decommissioning and emergency response to maintenance of accelerator facilities, underscoring their global significance for safety and operational efficiency.

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Path Planning Strategies in Radioactive Environments publication trend

The graph below shows the total number of articles in path planning strategies in radioactive environments across all publications each year (not limited to Nature Index journals).

Technical terms

Radiation dose: measure of ionising radiation energy absorbed by a material or biological tissue.

Combinatorial optimisation: process of finding an optimal ordering or selection from a finite set of discrete possibilities.

Reinforcement learning: machine learning paradigm in which agents learn optimal actions via trial-and-error interactions guided by rewards.

Genetic algorithm: search heuristic inspired by natural selection, employing operations such as mutation and crossover to evolve candidate solutions.

Deep Q Network (DQN): reinforcement learning method that uses a deep neural network to approximate the action-value function for decision making.

Metaheuristic: high-level strategy designed to guide subordinate heuristics in efficiently exploring large search spaces.

References

1. Bio-Inspired Optimization Algorithm Associated with Reinforcement Learning for Multi-Objective Operating Planning in Radioactive Environment. Biomimetics (2024).
2. A Novel Path Planning Approach for Mobile Robot in Radioactive Environment Based on Improved Deep Q Network Algorithm. Symmetry (2023).
3. Real-time 3D radiation risk assessment supporting simulation of work in nuclear environments. Journal of Radiological Protection (2014).
4. Route Optimization Methods for Response to Radiological Emergency Situations. IOP Conference Series Materials Science and Engineering (2020).