Achieving reactive robot behavior in complex dynamic environments is still challenging as it relies on being able to solve trajectory optimization problems quickly enough, such that we can replan the future motion at frequencies which are sufficiently high for the task at hand. We argue that current limitations in Model Predictive Control (MPC) for robot manipulators arise from inefficient, high-dimensional trajectory representations and the negligence of time-optimality in the trajectory optimization process. Therefore, we propose a motion optimization framework that optimizes jointly over space and time, generating smooth and timing-optimal robot trajectories in joint-space. While being task-agnostic, our formulation can incorporate additional task-specific requirements, such as collision avoidance, and yet maintain real-time control rates, demonstrated in simulation and real-world robot experiments on closed-loop manipulation. For additional material, please visit https://sites.google.com/oxfordrobotics.institute/vp-sto.
翻译:在复杂的动态环境中实现反应性机器人行为仍然具有挑战性,因为它依赖于能够迅速解决轨迹优化问题,因此我们可以在足够高的频率上重新规划未来运动,以完成手头的任务。我们争辩说,模型预测控制(MPC)目前对机器人操控器的限制源于低效率、高维的轨迹表现以及轨迹优化过程中时间-最佳性的疏忽。因此,我们提议了一个运动优化框架,在空间和时间上联合优化,在联合空间产生光滑和时间最佳的机器人轨迹。我们的配方可以包含额外的特定任务要求,如避免碰撞,同时保持实时控制率,在模拟和现实世界超环操作中演示。关于其他材料,请访问 https://sites.gogle.com/oxfordrobotics.inte/vp-stto。</s>