Grasping on Time: A Time-Controlled Dynamic Grasp Planning With Limited Jerk and Online Adaptation for Moving Objects

Grasping moving objects with minimal response time while limiting jerk is essential for many industrial and service robotics applications, and is considerably more challenging than grasping static targets. Existing approaches, such as sample-based methods, are not well-suited to this task because the stochastic nature of their solutions often produces jerky or oscillatory motions. This work introduces a method for grasping objects in motion on a conveyor belt by formulating a lightweight quadratic programming problem that explicitly controls the robot's arrival time at the target pose while constraining jerk. The method further supports real-time replanning, enabling the system to handle unpredictable events and environmental uncertainties, such as conveyor starts and stops or fluctuating velocities, without prior knowledge of object speed and without requiring synchronization between the robot and the conveyor. We demonstrate the capabilities of the proposed dynamic grasp-planning framework through multiple experiments on a physical robot at very high speeds up to 16m/minute (i.e., 26.66cm/s). A comparison with a state-of-the-art baseline highlights the superior performance of the proposed approach.