Time-Optimal Anti-Sloshing Trajectory Planning for Multiple Liquid-Filled Containers Subject to SCARA Motion
Andrea Ferrari, Roberto Di Leva, Simone Soprani, Luigi Biagiotti, Gianluca Palli, Marco Carricato
AI summary
Problem
Simultaneous robotic transport of multiple liquid-filled containers often causes dangerous sloshing and spillage, yet existing methods cannot minimize execution time while constraining peak liquid heights during complex 4D SCARA motions.
Approach
The authors extend a mass-spring-damper sloshing model to 4D SCARA motions and solve constrained optimization problems that minimize trajectory duration while enforcing strict liquid height limits, covering both fixed-path and point-to-point scenarios.
Key results
- Extended mass-spring-damper sloshing model to 4D SCARA motions
- Formulated constrained optimization for time-optimal anti-sloshing trajectories
- Proved that constraining only outermost containers drastically reduces computation time
- Validated algorithms through extensive simulations and physical experiments
Why it matters
Enables safer, faster automated pick-and-place operations for liquid handling in pharmaceutical and food/beverage manufacturing.
Abstract
This paper develops algorithms for planning time- optimal pick-and-place trajectories for multiple cylindrical con- tainers filled with liquid and simultaneously transported by a robot. The considered trajectories comprise 3D translations combined with a 1D rotation about the vertical direction, i.e. SCARA mo- tions. The presented approach minimizes the execution time, while ensuring that the liquid surface within each container remains below an imposed threshold throughout the motion. Two types of optimaltrajectoriesarestudied:oneoptimizesthemotionlawalong a given path, the other optimizes both the path and the motion law. Extensive simulations identify the most efficient optimization setup, whereas experiments validate the approach. The data sets of all simulated and experimental motions are distributed through an external repository.