Hybrid Offline-Online UAV Optimal Path Planning and Outbreak Dynamic Autonomous Behavior

This paper presents a novel hybrid path planning framework designed for autonomous Unmanned Aerial Vehicles (UAVs) operating in dynamic and uncertain environments. The proposed  approach  integrates  an  Offline  Phase  that  leverages a Genetic Algorithm (GA) to optimize PID control parameters and velocity profiles, alongside an A* search algorithm for initial path generation on static obstacle maps. This phase establishes an energy-efficient and optimized baseline trajectory. The Online Phase is activated only upon the detection of unexpected events or  dynamic  obstacles.  Here,  a  Parallel  Probabilistic  Cellular Automata  with  Monte  Carlo  Sampling  (P-PCA-MCS)  system is employed for real-time collision avoidance. This system dy- namically updates and fuses PCA-based occupancy probabilities with Monte Carlo-sampled collision probabilities for adversarial drone  trajectory  prediction,  resulting  in  a  comprehensive  risk map.  At  predefined  replanning  intervals,  the  drone  evaluates motion  primitives  based  on  a  quality  function  derived  from these fused probabilities, enabling rapid and adaptive trajectory adjustments to avoid dynamic threats while striving to return to  the  pre-optimized  path.  Extensive  simulations  across  vary- ing  complexities  demonstrate  that  the  P-PCA-MCS  algorithm consistently achieves superior performance. Compared to other state-of-the-art methods, it significantly reduces collision rates, maintains near-optimal path efficiency, and exhibits remarkably low computation burden, proving its efficacy for robust, real-time autonomous drone navigation in high-density airspaces.