Confidence-Aware Object Capture for a Manipulator Subject to Floating-Base Disturbances

Capturing stationary aerial objects on unmanned surface vehicles (USVs) is challenging due to quasiperiodic and fast floating-base motions caused by wave-induced disturbances. It is hard to maintain high motion prediction accuracy due to the stochastic nature of these disturbances, and perform object capture through real-time tracking due to the limited active torque. We introduce confidence analysis in predictive capture. To address the inaccuracy predictions, we calculate a real-time confidence tube to evaluate the prediction quality. To overcome tracking difficulties, we plan a trajectory to capture the object at a future moment while maximizing the confidence of the capture position on the predicted trajectory. All calculations are completed within 0.2 s to ensure a timely response. We validate our approach through experiments, where we simulate disturbances by executing real USV motions using a servo platform. The results demonstrate that our method achieves an 80% success rate.