A flexible and collaborative approach to robotic box-filling and item sorting

In this paper, we introduce an adaptive robotic manipulation framework to respond to the flexibility needs of common industrial tasks such as box-filling and item sorting. The proposed framework consists of a vision module and a robot control module. The vision module is responsible for the detection  and  tracking  of  the environment (e.g., box and the items), which is also  capable of  creating  an  occupancy  grid  in  real-time,  to  continuously update  the  robot  trajectory  planner  with  the  occupied portions of the detected box and their coordinates. The robot  control  module  includes  a  trajectory  planner and a self-tuning Cartesian impedance controller, to implement an adaptive strategy for the picking, placement, and sorting of the items in the box. The item-sorting strategy is based on our preliminary observations on human motor behavior, implementing a trade-off between the task execution accuracy and environmental perception uncertainty. The efficacy of the framework in performing a flexible box-filling task using a robot, autonomously or in collaboration with a human, is evaluated through several experiments.