A Hybrid Metric for Navigation of Autonomous Intralogistics Vehicles in Mixed Indoor and Outdoor Operation

While autonomous guided vehicle systems are increasingly used in homogeneous and structured environments, their use in complex and variable scenarios is usually limited. Established algorithms for the navigation of systems use static maps with deterministic metrics, which can only achieve optimal results in clearly defined environments. In dynamic and extensive deployment scenarios, which are also dependent on a large number of influencing parameters, autonomous intralogistics systems cannot yet be deployed dynamically. One example here is mixed transport between buildings under changing weather conditions. As a solution for dynamic navigation, we propose a hybrid metric in combination with topological maps and cyclic environmental sensing. Based on a quantification of influencing factors on each intralogistics entity, an optimal and dynamic navigation of every system can be performed at any time. The individual components are implemented in the context of an autonomous tow truck system and evaluated in different application scenarios. The results show significant added value in use cases with sudden weather changes and complex route networks

Augmented Virtuality Data Annotation and Human-in-the-Loop Refinement for RGBD Data in Industrial Bin-Picking Scenarios

Beyond conventional automated tasks, autonomous robot capabilities aside to human cognitive skills are gaining importance. This comprises goods commissioning and material supply in intralogistics as well as material feeding and assembly operations in production. Deep learning-based computer vision is considered as enabler for autonomy. Currently, the effort to generate specific datasets is challenging. Adaptation of new components often also results in downtimes. The objective of this paper is to propose an augmented virtuality (AV) based RGBD data annotation and refinement method. The approach reduces required effort in initial dataset generation to enable prior system commissioning and enables dataset quality improvement up to operational readiness during ramp-up. In addition, remote fault intervention through a teleoperation interface is provided to increase operational system availability. Several components within a real-world experimental bin-picking setup serve for evaluation. The results are quantified by comparison to established annotation methods and through known evaluation metrics for pose estimation in bin-picking scenarios. The results enable to derive accurate and more time-efficient data annotation for different algorithms. The AV approach shows a noticeable reduction in required effort and timespan for annotation as well as dataset refinement

Hierarchical And Flexible Navigation For AGVs In Autonomous Mixed Indoor And Outdoor Operation

Globalized markets require a high degree of flexibility in existing production and logistics environments. Flexible intralogistics systems are one key component for enabling versatile production by ensuring material supply and providing a dynamic link between different production stages. Challenged by historically grown production layouts and the increasing need of adaptable supply routes due to new forms of workshop organization like matrix production, state-of-the art approaches for intralogistics systems based on automated guided vehicles (AGVs) are not sufficient for mixed indoor and outdoor operation. In order to enable a truly flexible operation of systems in these mixed indoor and outdoor scenarios, new solutions for navigation and planning as well as increased autonomous capabilities of AGVs need to be focused (e.g. adaptive detection of driveable regions). In this paper, we propose an approach of hierarchical navigation utilizing a node-based representation of the production and logistics environment to allow flexible pathing and routing of AGVs. Based on inherent information of each node, autonomous capabilities of AGVs are activated according to the requirements of the area of operation. To prove reliability of the proposed hierarchical and flexible navigation, an evaluation of the approach is performed utilizing an industrial mobile system enhanced by autonomous capabilities in varying real-world cross-building scenarios