This article discusses the scientifically and industrially important problem of automating the process of unloading goods from standard shipping containers. We outline some of the challenges barring further adoption of robotic solutions to this problem: ranging from handling a vast variety of shapes, sizes, weights, appearance and packing arrangement of the goods, through hard demands on unloading speed and reliability, to ensuring fragile goods are not damaged. We propose a modular and reconfigurable software framework in an attempt at efficiently addressing some of these challenges. We outline the general framework design, as well as the basic functionality of the core modules developed and present two instantiations of the software system on two different fully integrated demonstrators. While one is coping with an industrial scenario, namely the automated unloading of coffee sacks, with an already economically interesting performance, the other scenario is used to demonstrate the capabilities of our scientific and technological developments in the context of medium-to long-term prospects of automation in logistics. We performed evaluations which allow us to summarize several important lessons learned and to identify future directions of research on autonomous robots for handling of goods in logistics applications.
Abstract-In this paper, we propose a new approach for automatically building symbolic relational descriptions of static configurations of objects to be manipulated by a robotic system. The main goal of our work is to provide advanced cognitive abilities for such robotic systems to make them more aware of the outcome of their actions. We describe how such symbolic relations are automatically extracted for configurations of box-shaped objects using notions from geometry and static equilibrium in classical mechanics. We also present extensive simulation results as well as some real-world experiments aimed at verifying the output of the proposed approach.
Abstract-In this paper, we propose an approach for robotic manipulation systems to autonomously reason about their environments under incomplete information. The target application is to automate the task of unloading the content of shipping containers. Our goal is to capture possible support relations between objects in partially known static configurations. We employ support vector machines (SVM) to estimate the probability of a support relation between pairs of detected objects using features extracted from their geometrical properties and 3D sampled points of the scene. The set of probabilistic support relations is then used for reasoning about optimally selecting an object to be unloaded first. The proposed approach has been extensively tested and verified on data sets generated in simulation and from real world configurations.
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