Error-tolerant manipulation by caging

摘要

This paper delivers a preliminary attempt to find the optimized caging positions for a three-finger robotic hand designed for home-use logistical environments. The main idea behind optimizing caging positions falls in that optimal caging can afford largest margins to stop target objects from escaping into infinity. By employing the advantages of largest margins, optimal caging grasp can be robust enough to endure dramatic perception noises or errors and low sensing resolutions. This paper optimizes object grasping towards caging. Specifically, our algorithm utilizes Genetic Algorithm (GA) to accelerate the searching procedure and evaluate a fitness of the GA population by examining a combination of max-min, which corresponds to intersections of neighbour fingers' CC space margins, and least inter-finger distance for optimization. Simulation results show that the manipulation strategy proposed in this paper could in the worse case coordinate with sensors whose resolution are less than one pixel per centimeter.