Flexible and Robust Walking in Uneven Terrain

机器人技术已经取得了显著的进步，使最先进的机器能够以相对较高的速度行走和奔跑。尽管如此，目前的两足动物不能够处理在实际使用这种机器期间会发生的大干扰，并且在复杂环境中产生行走模式方面仍然具有严重的限制。 目前，这些限制似乎比机器人在理想的实验室条件下不能像人类那样快速行走或奔跑的事实更成问题，因为腿式运动的核心论点是在崎岖地形中比轮式或履带式车辆优越。因此，本计画的目标是研究提高机器人的强健性与灵活性的方法。我们认为这项研究是在严格控制的实验室条件外使用机器人的第一步，并证明了在崎岖地形中腿运动的固有优势。 该项目将包括理论研究、数值模拟和使用DFG项目“Natur und Technik intelligenten Laufens”期间开发的机器人Lola的行走实验。项目标题中的“灵活性”一词表示控制系统实际使用机器人大部分物理能力的能力。 这一点很重要，例如，当跨过或踩到复杂的、以前未知的障碍物时，或者当以非常大的步伐行走时。我们所说的“鲁棒性”是指机器人从由外力或环境模型中的误差引起的非常大的干扰中恢复的能力。这只能通过对系统的原始计划运动进行大的修改来实现。这一建议基于两个假设：1。采用时变任务空间定义和预测逆运动学算法，简化和加快了具有奇异性和避碰的稳定运动实时规划.通过对行走模式和自适应机器人和环境模型的反应性重新规划，增加了对干扰的鲁棒性。在这个项目中开发的方法很容易转移到类似的机器人，并在很大程度上，更传统的机器人机械手。具体来说，我们计划在稍后的时间使用该项目中开发的方法，用于农业机器人在混乱，未知环境中的运动生成。

There have been remarkable advances in the technology of biped robots, enabling the most advanced machines to walk and run at relatively high speeds. Still, current bipeds are not capable of handling the large disturbances that would occur during the practical use of such machines and still have severe limitations in the generation of walking patterns in complex environments. Currently, these limitations appear to be more problematic than the fact that robots can not walk or run as fast in ideal laboratory conditions as humans can, since a central argument for legged locomotion is the supposed superiority to wheeled or tracked vehicles in rough terrain. Therefore, the goal of this project is to research methods for improving the robustness and flexibility of biped robots. We view this research as a first step towards using biped robots outside tightly controlled laboratory conditions and proving the inherent superiority of legged locomotion in rough terrain. The project will include theoretical studies, numerical simulations and walking experiments using the biped robot Lola developed during the DFG project „Natur und Technik intelligenten Laufens“.The term „flexiblility“ in the title of the project signifies the capacity of the control system to actually use a large portion of the robot‘s physical capabilities. This is important, e.g., when stepping over or onto complex, previously unknown obstacles or when walking with very large steps. By term ‘robustness‘ we mean the ability of the robot to recover from very large disturbances, due to external forces or errors in the environment model. This can only be achieved by large modifications of the originally planned motion of the system. This proposal is based on two hypotheses: 1. Real-time planning of stable locomotion with singularity and collision avoidance is simplified and sped up by using a time-varying definition of task-space and a predictive inverse kinematics algorithm.2. Robustness against disturbances is increased by reactive re-planning of walking patterns and adaptive robot and environment models. The methods developed during this project are easily transferable to similar biped robots and, to a large extent, more conventional robot manipulators. Specifically, we are planning to use the methods developed in this project for motion generation of agricultural robots in cluttered, unknown environments at a later time.