论文标题
部分可观测时空混沌系统的无模型预测
Real-time Trajectory Optimization and Control for Ball Bumping with Quadruped Robots
论文作者
论文摘要
本文研究了用四倍的机器人进行实时运动计划和控制球撞击运动的控制。为了使四足动物以不同的初始化撞击飞行球,我们开发了一种非线性轨迹优化的计划计划,该方案共同识别起飞时间和状态,以实现在飞行阶段中获得准确的球命中。这样的计划方案采用了二维单一刚性车身模型,该模型在高度时间敏感的任务的准确性和效率之间达到了令人满意的平衡。为了精确执行计划的运动,跟踪控制器需要将施加的起飞和击球事件施加的严格时间态限制纳入。为此,我们开发了一个改进的模型预测控制器,该控制器尊重关键时期约束。拟议的规划和控制框架已使用真正的Aliengo机器人进行了验证。实验表明,问题计划方法平均可以在大约60毫米中计算,从而成功实现了碰撞运动,并实时实时进行各种初始化。
This paper studies real-time motion planning and control for ball bumping motion with quadruped robots. To enable the quadruped to bump the flying ball with different initializations, we develop a nonlinear trajectory optimization-based planning scheme that jointly identifies the take-off time and state to achieve accurate ball hitting during the flight phase. Such a planning scheme employs a two-dimensional single rigid body model that achieves a satisfactory balance between accuracy and efficiency for the highly time-sensitive task. To precisely execute the planned motion, the tracking controller needs to incorporate the strict time-state constraint imposed on the take-off and ball-hitting events. To this end, we develop an improved model predictive controller that respects the critical time-state constraints. The proposed planning and control framework is validated with a real Aliengo robot. Experiments show that the problem planning approach can be computed in approximately 60ms on average, enabling successful accomplishment of the ball bumping motion with various initializations in real time.