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Autonomous area exploration is a critical application for various robotic systems, with micro-drones being particularly suited due to their high manoeuvrability and reliability. However, their limited operational time and computational power pose challenges for effective exploration. This paper introduces a risk-aware path-planning algorithm for micro-drones, pri-oritising energy efficiency by minimising travel distance. The algorithm, utilizing Monte-Carlo simulations and a risk-aware Probabilistic Roadmap (PRM) method, discretises the workspace and constructs a topological PRM graph to mitigate collisions. Simulation results demonstrate the algorithm's performance in unknown environments, approaching ideal exploration in known areas. Field experiments with Crazyflie micro-drones validate its practical implementation, enabling micro-drones to navigate unknown environments with an acceptable collision risk for efficient exploration in resource-constrained spaces.
Bana et al. (Thu,) studied this question.