基于A*算法在具有障碍的环境中的多USV合作狩猎方法.
Zhihao Chen1, Zhiyao Zhao1,2,3, Jiping Xu1,2,3
1School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China.
本研究介绍了一种高效的合作狩猎方法,用于使用增强的A*算法对多个无人驾驶表面车辆 (USV) 进行搜索. 该方法改善了路线规划和目标搜索能力,在障碍物充满的环境中.
科学领域:
- 机器人技术和自主系统
- 海洋工程 海洋工程
- 人工智能的人工智能
背景情况:
- 单一的无人地面战斗机 (USV) 具有有限的任务执行能力.
- 涉及多个USV的合作策略对于复杂的任务至关重要,特别是合作狩猎.
- 现有的路径规划算法在动态,多代理情景中可能缺乏效率和适应性.
研究的目的:
- 开发一种高效的合作狩猎方法,用于在有障碍的环境中对多个USV进行狩猎.
- 增强A*算法,以改善路径规划和目标搜索效率.
- 引入生物模拟群策略,用于协调USV狩猎行为.
主要方法:
- 一个增强的A*算法,采用基于USV最小转半径的路径平滑.
- 使用后顺序穿越递归算法来确定最佳路径,提高A*效率.
- 一个仿生多USV群猎策略模拟狮子狩猎战术的预形成和目标包围.
主要成果:
- 拟议的路径平滑方法提高了A*算法的效率.
- 生物模拟群策略可以实现自主形成和有效的目标制.
- 模拟实验验证了算法在路径规划和目标搜索中的有效性.
结论:
- 开发的合作狩猎方法显著提高了多USV系统的性能.
- 增强的A*算法和生物模拟群策略为自主海洋作业提供了强大的解决方案.
- 这项研究有助于推进协调自动驾驶汽车监控和拦截系统的发展.
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