不平地形上四足机器人的在线全方位跳跃轨迹规划
Online Omnidirectional Jumping Trajectory Planning for Quadrupedal Robots on Uneven Terrains
AI总结：
针对四足机器人在不平地形上的在线全方位跳跃问题，提出一种级联优化框架，集成轨迹生成、跟踪与着陆控制，并采用加速进化算法和粗到精重定位，实现约0.1秒的快速轨迹生成，已在多机器人上验证。
摘要：
自然地形的复杂性常常需要动物采取跳跃等敏捷动作来提高穿越效率。为使四足机器人具备类似能力，需要复杂的实时跳跃动作。当前研究未能充分解决在线全方位跳跃问题，并且在轨迹生成过程中忽略了机器人的动力学约束。本文提出了一种通用且完整的级联在线优化框架，用于四足机器人的全方位跳跃。我们的解决方案系统性地涵盖了跳跃轨迹生成、轨迹跟踪控制器和着陆控制器。它还整合了环境感知，以导航标准运动无法绕过的障碍物，例如从高平台跳下。我们引入了一种新颖的跳跃平面来参数化全方位跳跃运动，并构建了一个紧密耦合的优化问题，考虑动力学约束，同时优化质心轨迹、地面反作用力（GRFs）和关节状态。为了满足在线要求，我们提出了一种加速进化算法作为轨迹优化器，以处理动力学约束的复杂性。为了确保着陆后环境感知的稳定性和准确性，我们引入了一种从粗到精的重定位方法，该方法结合了全局分支定界（BnB）搜索与最大后验（MAP）估计，用于导航和跳跃过程中的精确定位。所提出的框架在热启动下实现了约0.1秒的跳跃轨迹生成，并已在两台四足机器人上在不平地形上成功验证。此外，我们将框架的通用性扩展到人形机器人。
英文摘要：
Natural terrain complexity often necessitates agile movements like jumping in animals to improve traversal efficiency. To enable similar capabilities in quadruped robots, complex real-time jumping maneuvers are required. Current research does not adequately address the problem of online omnidirectional jumping and neglects the robot's kinodynamic constraints during trajectory generation. This paper proposes a general and complete cascade online optimization framework for omnidirectional jumping for quadruped robots. Our solution systematically encompasses jumping trajectory generation, a trajectory tracking controller, and a landing controller. It also incorporates environmental perception to navigate obstacles that standard locomotion cannot bypass, such as jumping from high platforms. We introduce a novel jumping plane to parameterize omnidirectional jumping motion and formulate a tightly coupled optimization problem accounting for the kinodynamic constraints, simultaneously optimizing CoM trajectory, Ground Reaction Forces (GRFs), and joint states. To meet the online requirements, we propose an accelerated evolutionary algorithm as the trajectory optimizer to address the complexity of kinodynamic constraints. To ensure stability and accuracy in environmental perception post-landing, we introduce a coarse-to-fine relocalization method that combines global Branch and Bound (BnB) search with Maximum a Posteriori (MAP) estimation for precise positioning during navigation and jumping. The proposed framework achieves jump trajectory generation in approximately 0.1 seconds with a warm start and has been successfully validated on two quadruped robots on uneven terrains. Additionally, we extend the framework's versatility to humanoid 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。
