{'fraction_non_alphanumeric': 0.029180695847362513, 'fraction_numerical': 0.0, 'mean_word_length': 5.510948905109489, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
An empirical study on robustness of UAV path planning algorithms considering position uncertainty

UAV(Unmanned Aerial Vehicle) needs to accomplish its task with obstacle avoidance. However, uncertainties in the actual complex flight environment affect the application of UAV. In consideration of the error of UAV's position estimation, this paper attempts to evaluate the robustness which is measured by the safety degree of the path. UAV path planning algorithms, including A-Star, BLP(bi-level programming based algorithm), PSO(Particle Swarm Optimization) and RRT(Rapid-exploring Random Trees), are selected for the empirical study. Results demonstrate that RRT and BLP behave much better than A∗ and PSO, considering variance and scenario complexity. RRT algorithm performs better in the simpler scenario and larger variance and BLP algorithm is more robust in the case of low variance.