  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.
