Discrete fuzzy grasp affordance for robotic manipulators

Grasp affordance determines the object-hand relative configurations which lead to successful grasps. Generation and representation of grasp affordances can increase achieved grasp quality and be integrated in path planning algorithms facilitating increased efficiency. Grasp quality is determined by various measures and may have a major impact on task success. Fuzzy grasp affordance can be defined based on a fuzzy grasp quality grade and enhance the previously Boolean notion of grasp affordance. Fuzzy grasp affordances can be represented using a discrete manifold. This facilitates integration of data from various sources and representation optimization using evolutionary algorithms. A method for construction of a discrete fuzzy grasp affordance manifold is presented and demonstrated for apple selective harvesting. The affordance constructed is based on learning from human demonstration. It includes quality grade determination, manifold structure determination, cell quantization, and smoothing. An algorithm for adaptation of the computed manifold to different manipulators and grippers is developed and implemented for two different end effectors. Additionally a method for online integration of the developed affordance is presented.