Online Trajectory Scaling for Manipulators Subject to High-Order Kinematic and Dynamic Constraints

Robotic manipulators are usually driven by means of minimum-time trajectories. Unfortunately, such trajectories strongly solicit the actuators whose dynamic limits could be easily exceeded. Therefore, kinematic and/or dynamic constraints are commonly considered for offline planning. Nevertheless, during actual operations, dynamic limits could be violated because of model uncertainties and measurement noise, thus causing performance losses. In order to fulfill the given bounds with certainty, planned trajectories are typically online scaled, by accounting for generalized force (GF) constraints. The resulting command signal is typically discontinuous; therefore, the system mechanics are unnecessarily solicited, and nonmodeled dynamics are excited. Moreover, in the case of systems that admit limited derivatives for GFs, tracking accuracy worsens. To prevent possible problems that derive from GF discontinuities, this paper proposes an online trajectory scaling approach that accounts for the simultaneous existence of joint constraints on GFs and their derivatives. At the same time, it is able to manage bounds on joint velocities, accelerations, and jerks.