论文标题
使用集成的概率路线图（PRM）和参考调查员（RG）的多模式腿部运动运动的有效路径规划和跟踪
Efficient Path Planning and Tracking for Multi-Modal Legged-Aerial Locomotion Using Integrated Probabilistic Road Maps (PRM) and Reference Governors (RG)
论文作者
论文摘要
在有重大计划外的骚乱的情况下，也可以成功地实施生物启发的腿部机器人，即使在存在重大计划的障碍的情况下，也可以努力行走，行走和跳跃。尽管所有这些成就，但在多模式腿部系统中的实践控制和高级决策算法还是忽略的。在自然界中，诸如鸟类之类的动物令人印象深刻地展示了多种移动模式，包括腿部和空中运动。他们能够在大墙壁，狭窄的空间上执行强大的运动，并可以从不可预测的情况（例如突然的阵风或湿滑的表面）中恢复。受这些动物的多功能性和结合腿部和空中移动性以协商环境的能力的启发，我们的主要目标是设计和控制腿部机器人，这些机器人在一个平台中整合了两种完全不同形式的运动，地面和空中移动性。我们的机器人The Husky Carbon正在开发以整合空中和腿部的运动，并在腿部和空中移动之间转变。这项工作利用了基于对沙哑的动力学模型的低级控制的参考调速器（RG）来维持腿部运动的效率，使用概率的路线图（PRM）和3D A*算法，以基于运输和气动移动性的运输能力成本来产生最佳路径
There have been several successful implementations of bio-inspired legged robots that can trot, walk, and hop robustly even in the presence of significant unplanned disturbances. Despite all of these accomplishments, practical control and high-level decision-making algorithms in multi-modal legged systems are overlooked. In nature, animals such as birds impressively showcase multiple modes of mobility including legged and aerial locomotion. They are capable of performing robust locomotion over large walls, tight spaces, and can recover from unpredictable situations such as sudden gusts or slippery surfaces. Inspired by these animals' versatility and ability to combine legged and aerial mobility to negotiate their environment, our main goal is to design and control legged robots that integrate two completely different forms of locomotion, ground and aerial mobility, in a single platform. Our robot, the Husky Carbon, is being developed to integrate aerial and legged locomotion and to transform between legged and aerial mobility. This work utilizes a Reference Governor (RG) based on low-level control of Husky's dynamical model to maintain the efficiency of legged locomotion, uses Probabilistic Road Maps (PRM) and 3D A* algorithms to generate an optimal path based on the energetic cost of transport for legged and aerial mobility
