Combining Mobile Robotics and Packing for Optimal deliveries

Abstract

The problem addressed is taken from warehousing and distribution. It concerns the order preparation in e-commerce. An order is a set of lines. Each line describes the ordered product (box) with its corresponding quantity. The problem consists of packing boxes subject to orientation, fragility, stackability, weight and volume constraints of various sizes into available containers (trays, cartons) in a way which optimizes the total number of containers to integrate in a number of delivery trips in e-commerce. The article addresses the problem where the packing is performed by a mobile robot. The aim of the work carried out in the context of the project INCASE[0] which is a plat-form of the INTERREG V [1] is to conduct, among other experiments[2] a set of experiments where the packing activity is performed by a mobile robot in order to pick up items from shelves and then pack items in boxes. To keep the problem simple, we assume that positions of items to pick up and to place them in the box are known; they are computed by efficient bin packing optimization algorithms (2D, 3D) of Optim Suite of KLS OPTIM in production since many years. There are many ways of using bin packing algorithms. The first possible use is the online packing of items based on a fixed order defined by the host system. This approach has several drawbacks, among them the inefficient handling of stability constraint and sub-use of available box volumes. The second approach is to allow packing algorithms to compute optimal or sub-optimal solutions whilst respecting all constraints; this approach is used in production. One drawback of this approach is such solutions are not always feasible by a mobile robot. The aim of our work is that the mobile robot must be able to take into account optimization constraints in order to place objects on the pallets. The objective is to find a trade off between optimality and feasibility. The work is to conduct a set of real-life experiments and simulations by mobile robots to identify failure cases like falling, unsteady, damaged items due to robot arm forces, instability or space. The aim is to learn from these simulations, introduction of machine learning, and to derive a set of rules to integrate in the bin packing optimization algorithms as strategies to build robust packing solution for autonomous mobile robots.