触觉感知使细长多足机器人能够实现垂直障碍物攀越
Tactile sensing enables vertical obstacle negotiation for elongate many-legged robots
AI总结：
本研究提出触觉天线系统与控制框架，使细长多足机器人能攀爬高达自身五倍的垂直障碍物，在复杂环境中展现稳健攀爬性能。
摘要：
细长多足机器人在崎岖地形上展现出可靠的移动潜力。然而，针对这些系统的研究大多聚焦于平面运动规划，并未涉及快速垂直运动。尽管它们在轻度崎岖地形上表现良好，但近期的现场测试揭示了对三维行为（如攀爬或穿越高大障碍物）的迫切需求。三维运动规划的挑战部分在于为通常具有超过25个自由度的高复杂度系统设计感知与控制方案。为应对感知方面的首要挑战，我们提出了一种触觉天线系统，使机器人能够探测障碍物以获取其结构信息。基于这一感官输入，我们开发了一个控制框架，该框架整合了天线与足部接触传感器的数据，以动态调整机器人的垂直身体起伏，从而实现有效攀爬。通过添加简单、低带宽的触觉传感器，具有高静态稳定性和冗余度的机器人能够在复杂环境中利用简单的反馈控制器展现出可预测的攀爬性能。实验室和户外实验表明，该机器人能够攀爬高达其自身高度五倍的障碍物。此外，机器人在覆盖有移动的、与机器人尺寸相当的随机物品以及具有快速变化曲率的障碍物上展现出稳健的攀爬能力。这些发现展示了一种替代方案，用于感知环境并促进腿式机器人的有效响应，为未来高能力、低轮廓的多足机器人铺平了道路。
英文摘要：
Many-legged elongated robots show promise for reliable mobility on rugged landscapes. However, most studies on these systems focus on planar motion planning without addressing rapid vertical motion. Despite their success on mild rugged terrains, recent field tests reveal a critical need for 3D behaviors (e.g., climbing or traversing tall obstacles). The challenges of 3D motion planning partially lie in designing sensing and control for a complex high-degree-of-freedom system, typically with over 25 degrees of freedom. To address the first challenge regarding sensing, we propose a tactile antenna system that enables the robot to probe obstacles to gather information about their structure. Building on this sensory input, we develop a control framework that integrates data from the antenna and foot contact sensors to dynamically adjust the robot's vertical body undulation for effective climbing. With the addition of simple, low-bandwidth tactile sensors, a robot with high static stability and redundancy exhibits predictable climbing performance in complex environments using a simple feedback controller. Laboratory and outdoor experiments demonstrate the robot's ability to climb obstacles up to five times its height. Moreover, the robot exhibits robust climbing capabilities on obstacles covered with shifting, robot-sized random items and those characterized by rapidly changing curvatures. These findings demonstrate an alternative solution to perceive the environment and facilitate effective response for legged robots, paving ways towards future highly capable, low-profile many-legged robots.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
