{'fraction_non_alphanumeric': 0.018793273986152326, 'fraction_numerical': 0.005934718100890208, 'mean_word_length': 4.7175141242937855, '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}}
IFC-based Improved ACO for Multi-agent Path Planning in Web3D Mountain Environment

The path planning in Web3D for large-scale scenes is a fundamental problem. Especially, there are many agents searching for their optimal paths in a complex and large-scale mountain scene. This paper addresses a kind of method using an improved ant colony algorithm to find path for multi-agent in Web3D environment using HTML5. An improved leader-follower method is used to organize the Blue army soldiers to attach the Red army soldiers. Firstly, the paper proposes a kind of method using BIM information together with improved ACO algorithm to form a kind of Web3D path planning method. Secondly, the method is used in the reality experiment by using Egret Engine to find a multi-agent path when soldiers are marching or fighting in a mountain terrain. The result of the experiment displays that the convergence of the improved ACO algorithm is fast, and the Web3D path planning results are efficient, real time and precise.