基于虚拟模型控制与最小加加速度轨迹生成的快速在线全向四足跳跃框架
A Fast Online Omnidirectional Quadrupedal Jumping Framework Via Virtual-Model Control and Minimum Jerk Trajectory Generation
AI总结：
本文提出一种基于最小加加速度轨迹生成和虚拟模型控制的四足机器人快速全向跳跃框架，在增强版Mini Cheetah上实现50微秒级实时求解与连续全向跳跃。
摘要：
探索四足机器人敏捷性的极限，尤其是在快速实时规划与执行全向跳跃轨迹的背景下，由于涉及复杂动力学，特别是考虑到显著的冲量接触时，面临着巨大挑战。本文提出了一种新框架，使四足机器人具备快速的全向跳跃能力。该框架利用最小加加速度技术，高效生成跳跃轨迹，充分利用其解析解，确保数值稳定性和动态兼容性，同时仅需极少的计算资源。采用虚拟模型控制构建二次规划（QP）优化问题，以在跳跃阶段精确跟踪质心（CoM）轨迹。全身控制策略实现了精确且柔顺的着陆运动。此外，不同的跳跃阶段由时间调度触发。该框架的有效性通过在开源Mini Cheetah机器人的增强版本上的实现得到了验证。成功执行了包括向前、向后及其他方向在内的全向跳跃，展示了机器人执行快速连续跳跃的能力，平均轨迹生成与跟踪求解时间仅为50微秒。
英文摘要：
Exploring the limits of quadruped robot agility, particularly in the context of rapid and real-time planning and execution of omnidirectional jump trajectories, presents significant challenges due to the complex dynamics involved, especially when considering significant impulse contacts. This paper introduces a new framework to enable fast, omnidirectional jumping capabilities for quadruped robots. Utilizing minimum jerk technology, the proposed framework efficiently generates jump trajectories that exploit its analytical solutions, ensuring numerical stability and dynamic compatibility with minimal computational resources. The virtual model control is employed to formulate a Quadratic Programming (QP) optimization problem to accurately track the Center of Mass (CoM) trajectories during the jump phase. The whole-body control strategies facilitate precise and compliant landing motion. Moreover, the different jumping phase is triggered by time-schedule. The framework's efficacy is demonstrated through its implementation on an enhanced version of the open-source Mini Cheetah robot. Omnidirectional jumps-including forward, backward, and other directional-were successfully executed, showcasing the robot's capability to perform rapid and consecutive jumps with an average trajectory generation and tracking solution time of merely 50 microseconds.
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。
