多重轨迹优化和控制机器人代理使用混合模糊嵌入式人工智能技术用于多个目标问题
Saroj Kumar1, Krishna Kant Pandey2, Dayal R Parhi3
1Centre of Excellence in Robotics, O.P. Jindal University, Raigarh, Chhattisgarh, 496109, India.
Scientific reports
|December 30, 2025
概括
这项研究引入了一种新的混合方法,将模糊逻辑和海洋捕食者算法结合起来,用于轮式机器人路径优化. 这种方法提高了导航效率,并避免了复杂环境中的碰撞.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 控制系统 控制系统
背景情况:
- 轮式机器人对于自动化复杂任务至关重要,取代了人类的努力.
- 优化机器人路径和尽量减少时间消耗是当前自动化目标的关键挑战.
- 路径优化和移动机器人的控制仍然是积极研究的领域.
研究的目的:
- 为轮式机器人开发一个多目标路径优化技术.
- 为了提高导航效率和减少移动机器人的任务完成时间.
- 在机器人路径规划和控制中解决实时目标.
主要方法:
- 开发了一种混合方法,结合了模糊逻辑和修改后的海洋捕食者优化算法.
- 模糊逻辑处理障碍距离,其输出输入到优化算法中.
- 集成了一个Petri-Net控制器来管理导航,并防止机器人之间与移动障碍物的碰撞.
主要成果:
- 混合模糊海洋捕食者算法在模拟和实时实验中成功证明了多个机器人的导航.
- 与现有方法相比,拟议的技术在导航参数中平均改善了大约10%或更多.
- 该系统有效地避免了机器人之间的碰撞在存在移动障碍物.
结论:
- 混合的模糊海洋捕食者优化算法为移动机器人路径规划提供了强大的解决方案.
- 集成Petri-Net控制器进一步完善导航,特别是在多个机器人的动态环境中.
- 拟议的方法显著改善了导航参数,验证了其与其他人工智能技术相比的有效性.
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