基于双合组合聚合和变压器优化的自主机器人适应式避障模型
Yuhu Tang1, Ying Bai1, Qiang Chen2
1School of Artificial Intelligence and Big Data, Hefei University, Hefei 230601, China.
Sensors (Basel, Switzerland)
|April 28, 2025
概括
本研究介绍了GAS-H-Trans,这是自动驾驶机器人的增强型变压器模型,可以在复杂的环境中更好地识别和避免障碍物. 该模型在图像细分和成功避开障碍方面实现了高精度,提高了机器人的安全性和效率.
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
- 计算机视觉 计算机视觉
背景情况:
- 自主机器人需要强大的障碍物识别和避免,以便在动态环境中安全操作.
- 当前的深度学习方法在适应复杂,现实世界的场景方面面临挑战.
研究的目的:
- 提出一个改进的基于变压器的架构,GAS-H-Trans,以提高自主机器人的语义理解和避开障碍.
- 在动态环境中提高避障策略的准确性和适应性.
主要方法:
- 采用基于变压器的双合组合聚合策略,以优化特征提取和全球表示.
- 集成的哈里斯霍克优化 (HHO) 用于超参数调整和粒子群优化 (PSO) 用于人工潜力场 (APF) 系数优化.
- 实施了二次精确图像细分策略,在关键障碍物附近设置了观察点.
主要成果:
- 在图像细分任务中,GAS-H-Trans实现了85.2%的欧盟平均交叉点 (mIoU),表现优于基线模型.
- 在GAS-H-Trans + PSO优化的APF框架中,在虚拟环境中,避免障碍的成功率为93.6%.
- 提出的方法显著提高了动态运动规划和细分精度的性能.
结论:
- GAS-H-Trans模型为自动驾驶机器人提供了在障碍物识别和避免方面取得的重大进步.
- 综合优化技术提高了模型的适应性和复杂的导航任务的成功率.
- 这种方法为现实世界的自主导航应用提供了一个有希望的解决方案.
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