Compliant-based robotic 3D bin packing with unavoidable uncertainties

Abstract

Robotic 3D bin packing (R-3dBPP), aiming to place deformed cases in various sizes in the container without fences, is a comprehensive application that includes perception, planning, execution, and hardware design. Traditional studies assume that the context of the real space must be accurately perceived and represented. However, disjunctions between the planner and reality are unavoidable in R-3dBPP, especially with low-cost sensors. As far as the author knows, there is no practical solution. In this paper, the above assumption is discarded and the typical types of uncertainties prevalent to guide the design of the algorithms are formulated. A new online bin-packing algorithm is proposed, keeping deformed boxes stacked in close contact with each other, so that the whole pallet is kept stable by friction between the boxes and the container. In order to meet the requirement of close contact under the non-negligible errors from sensors and planners, a compliant-based motion planning system is introduced. It replaces the precision-based feedback with a compliant end-effector and accompanying motion strategies. Last but not least, a complete online bin-packing robot system is developed and the system's performance under the influence of the uncertainties mentioned above through simulation and physical experiments is evaluated.