Generating feasible robot motions in real-time requires achieving multiple tasks (i.e., kinematic requirements) simultaneously. These tasks can have a specific goal, a range of equally valid goals, or a range of acceptable goals with a preference toward a specific goal. To satisfy multiple and potentially competing tasks simultaneously, it is important to exploit the flexibility afforded by tasks with a range of goals. In this paper, we propose a real-time motion generation method that accommodates all three categories of tasks within a single, unified framework and leverages the flexibility of tasks with a range of goals to accommodate other tasks. Our method incorporates tasks in a weighted-sum multiple-objective optimization structure and uses barrier methods with novel loss functions to encode the valid range of a task. We demonstrate the effectiveness of our method through a simulation experiment that compares it to state-of-the-art alternative approaches, and by demonstrating it on a physical camera-in-hand robot that shows that our method enables the robot to achieve smooth and feasible camera motions.

翻译：实时生成可行的机器人运动需要同时达成多个任务（即运动学要求）。这些任务可以具有特定目标、一系列同等有效的目标，或一系列可接受但优先趋向特定目标的目标范围。为了同时满足多个且可能相互竞争的任务，充分利用具有目标范围的任务所带来的灵活性至关重要。本文提出了一种实时运动生成方法，该方法在单一统一框架内兼容所有三类任务，并利用具有目标范围的任务的灵活性来配合其他任务。我们的方法将任务纳入加权和的多目标优化结构中，并采用带有新型损失函数的障碍方法对任务的有效范围进行编码。我们通过仿真实验（与现有最优替代方法进行对比）以及在手部搭载物理相机的机器人上的实际演示，验证了该方法的有效性——实验表明该方法能使机器人实现平滑且可行的相机运动。