Título: Hybrid Offline -Online UAV Optimal Path Planning and Outbreak Dynamic Autonomous Behavior
Autores: Heictor Alves de Oliveira Cota, Fernando José Von Zuben
Resumo: 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 dynamically 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 varying 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.
Palavras-chave: UAV path planning; genetic algorithm; probabilistic cellular automata; hybrid control; dynamic obstacle avoidance.
Páginas: 8
Código DOI: 10.21528/CBIC2025-1175935
Artigo em PDF: CBIC_2025_paper1175935.pdf
Arquivo BibTeX:
CBIC_2025_1175935.bib