路径规划和机器人狗的运动控制通过粗的地形,基于视觉导航
Tianxiang Chen1, Yipeng Huangfu1, Sutthiphong Srigrarom1
1Mechanical Engineering, National University of Singapore, Singapore 117576, Singapore.
Sensors (Basel, Switzerland)
|November 27, 2024
概括
这项研究通过先进的控制和视觉系统来增强机器人狗的导航能力,提高稳定性和路径规划准确性,在具有挑战性的地形上进行现实世界的应用.
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:
- 在复杂的地形上进行自主导航仍然是四足机器人面临的重大挑战.
- 现有的系统经常与动态环境和精确的路径规划作斗争.
- 对于灾难应对等应用来说,需要强大且可适应的导航解决方案至关重要.
研究的目的:
- 为了增强四足机器人狗的自主导航和避难障碍.
- 开发一个强大的路径规划算法,用于在崎的地形上操作的机器人狗.
- 为了提高机器人的稳定性,机动性和轨迹准确性,在不同的环境中.
主要方法:
- 将模型预测控制 (MPC) 和全身控制 (WBC) 与深度视觉 (Intel RealSense D435i) 的整合.
- 针对动态地形轨迹优化和实施多体动态模型的EGO-Planner的定制.
- 开发双筒视觉跟踪方法用于路径规划和运动控制,将传感器数据与动力学模型合并.
主要成果:
- 与SLAM系统相比,RGB-D视觉系统显示出优越的速度稳定性 (20%的错误减少) 和轨迹精度 (10%的改进).
- 实现了更顺的导航,路径规划代减少了15%,在具有挑战性的环境中,成功率恢复速度提高了30%.
- 在模拟的城市灾害场景中成功导航,验证了系统应急响应的潜力.
结论:
- 增强的自主导航系统显著提高了四足机器人在多样化和具有挑战性的地形上的性能.
- 开发的基于双眼视觉的路径规划为未来的机器人狗导航提供了强大的框架.
- 这些进步为在复杂,动态和危险的环境中使用更有能力的机器人铺平了道路.
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