Abstract
Flexible production is a key element in modern industrial manufacturing. Autonomous mobile manipulators can be used to execute various tasks: from logistics, to pick and place, or handling. Therefore, autonomous robotic systems can even increase the flexibility of existing production environments. However, the application of robotic systems is challenging due to their complexity and safety concerns. This paper addresses the design and implementation of the autonomous mobile manipulator OMNIVIL. A holonomic kinematic design provides high maneuverability and the implemented sensor setup with the underlying localization strategies are robust against typical static and dynamic uncertainties in industrial environments. For a safe and efficient human–robot collaboration (HRC), a novel workspace monitoring system (WMS) is developed to detect human co-workers and other objects in the workspace. The multilayer sensor setup and the parallel data analyzing capability provide superior accuracy and reliability. An intuitive zone-based navigation concept is implemented, based on the workspace monitoring system. Preventive behaviors are predefined for a conflict-free interaction with human co-workers. A workspace analyzing tool is implemented for adaptive manipulation, which significantly simplifies the determination of suitable platform positions for a manipulation task.
Highlights
In the last decades, the process of automated manufacturing was designed for constant large production batches and well-defined product types
The following strategies are implemented towards safe and efficient human–robot collaboration (HRC): (1) a novel workspace monitoring concept is presented to address the safety issue when implementing an autonomous industrial mobile manipulators” (AIMM), using RGB and thermal images as well as Lidar data; (2) the multilayer sensor setup is improved by the implementation of redundant algorithms for human co-worker detection based on neural networks
This study presented the development and implementation of the autonomous mobile manipulator
Summary
The process of automated manufacturing was designed for constant large production batches and well-defined product types. The following strategies are implemented towards safe and efficient human–robot collaboration (HRC): (1) a novel workspace monitoring concept is presented to address the safety issue when implementing an AIMM, using RGB and thermal images as well as Lidar data; (2) the multilayer sensor setup is improved by the implementation of redundant algorithms for human co-worker detection based on neural networks. One approach is to measure the time and resources needed to execute a task [47] This concept can as well be applied to the implementation process of AIMMs. In the industrial context, the time required and the knowledge base needed by the human instructor are comparable quantities
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