This paper proposes a framework for generating fast, smooth and predictable braking manoeuvers for a controlled robot. The proposed framework integrates two approaches to obtain feasible modal limits for designing braking trajectories. The first approach is real-time capable but conservative considering the usage of the available feasible actuator control region, resulting in longer braking times. In contrast, the second approach maximizes the used braking control inputs at the cost of requiring more time to evaluate larger, feasible modal limits via optimization. Both approaches allow for predicting the robot's stopping trajectory online. In addition, we also formulated and solved a constrained, nonlinear final-time minimization problem to find optimal torque inputs. The optimal solutions were used as a benchmark to evaluate the performance of the proposed predictable braking framework. A comparative study was compiled in simulation versus a classical optimal controller on a 7-DoF robot arm with only three moving joints. The results verified the effectiveness of our proposed framework and its integrated approaches in achieving fast robot braking manoeuvers with accurate online predictions of the stopping trajectories and distances under various braking settings.

翻译：本文提出了一种框架，用于为受控机器人生成快速、平滑且可预测的制动操纵。所提出的框架整合了两种方法，以获取设计制动轨迹的可行模态极限。第一种方法具备实时能力，但在使用可用执行器控制区域时较为保守，导致制动时间较长。相比之下，第二种方法通过优化来最大化使用的制动控制输入，但代价是需要更多时间来评估更大的可行模态极限。两种方法均允许在线预测机器人的停止轨迹。此外，我们还构建并求解了一个受约束的非线性最终时间最小化问题，以寻找最优力矩输入，并将最优解作为基准来评估所提出的可预测制动框架的性能。通过在仅具有三个活动关节的七自由度机器人臂上进行仿真，并与经典最优控制器进行比较，结果验证了我们提出的框架及其整合方法在实现快速机器人制动操纵方面的有效性，同时能够在不同制动设置下准确在线预测停止轨迹和距离。