Grasp Failure Constraints for Fast and Reliable Pick-and-Place Using Multi-Suction-Cup Grippers

TL;DR

This work tackles fast and reliable pick-and-place with heavy payloads using multi-suction-cup grippers by deriving an analytical load-distribution model that minimizes spring potential energy to obtain per-cup loads in real time. Grasp failure is formalized through suction-loss and slippage constraints, and the load-distribution is integrated into time-optimal trajectory planning via Time-Optimal Path Parameterization (TOPP), including a two-stage planning pipeline and a discretized, linearized formulation. The key contributions include a general analytical grasp-failure model for arbitrary multi-suction configurations, a closed-form load-distribution solution, experimental validation on a force-sensor-equipped testbed, and real-robot demonstrations showing improved grasp reliability with competitive motion durations. The approach offers a computationally efficient, physically grounded mechanism to enforce grasp stability in industrial settings, enabling faster and more robust pick-and-place operations than prior quasi-static or heuristic methods.

Abstract

Multi-suction-cup grippers are frequently employed to perform pick-and-place robotic tasks, especially in industrial settings where grasping a wide range of light to heavy objects in limited amounts of time is a common requirement. However, most existing works focus on using one or two suction cups to grasp only irregularly shaped but light objects. There is a lack of research on robust manipulation of heavy objects using larger arrays of suction cups, which introduces challenges in modeling and predicting grasp failure. This paper presents a general approach to modeling grasp strength in multi-suction-cup grippers, introducing new constraints usable for trajectory planning and optimization to achieve fast and reliable pick-and-place maneuvers. The primary modeling challenge is the accurate prediction of the distribution of loads at each suction cup while grasping objects. To solve for this load distribution, we find minimum spring potential energy configurations through a simple quadratic program. This results in a computationally efficient analytical solution that can be integrated to formulate grasp failure constraints in time-optimal trajectory planning. Finally, we present experimental results to validate the efficiency and accuracy of the proposed model.

Figures (15)

• Figure 1: Illustration of the reaction forces exerted by gravity and the movement of a hypothetical object being carried by the gripper (not shown). The left figure shows the distributed reaction force (blue arrows) and the suction force (green arrows) on each suction cup. The right figure shows the sum of wrenches at the origin of the tool frame of the robot.
• Figure 2: Figure A illustrates point force distribution on a single suction cup. The wrench, comprising a 3d moment and 3d force applied to the suction cup, is represented as a sum of 3d point forces distributed along the rim of the suction pad. Figure B visualizes a compressed suction cup as a spring under compression. The compressed suction cup is expected to exhibit increased stiffness, especially in the direction of compression.
• Figure 3: Frame description for our problem. Given that the object frame, attached to the CoM of the object, changes each time the robot handles different boxes, we will use the tool frame, attached to the end-effector of the manipulator, to formulate the problem.
• Figure 4: A testbed gripper and the vacuum generator used in our experimental setup. F/T sensors were attached to the bottom of each suction cup and to the base to measure the total wrench applied to the tool.
• Figure 5: Snapshots of load distribution measurements showing the varying forces generated across different points on the surface in multiple directions.