Aim-Aware Collision Monitoring: Discriminating between Expected and Unexpected Post-Impact Behaviors

To speed up and reduce power consumption per cycle in robotic manipulation, one option is to exploit intentional collisions with the surrounding environment and objects, an approach referred to as impact-aware manipulation. Within this context, this paper focuses on developing an online collision monitoring framework for distinguishing between expected and unexpected post-impact behaviors. The classification is based on a desired post-impact motion created via an idealized rigid robot- object-environment model. To generate a classification error bound, it employs a causal envelop filter that is needed due to the unavoidable joint and environment flexibility. In this way, it becomes possible to compare a desired idealized rigid response, which is straightforward to obtain with existing tools, with a measured impact response, which is affected by difficult- to-model post-impact oscillations. The classifier can be used for single-contact as well as multi-contact impact scenarios, such as those occurring in surface-to-surface impacts, and allows for tuning of the sensitivity between expected and unexpected post- impact behaviors. The monitoring framework fuses a (bandpass) momentum observer with impact-aware control to extend the classical collision event pipeline. As a proof of concept, we show the effectiveness of the approach through numerical simulations as well as with preliminary experimental results.