Validation of workspace monitoring and human detection for soft safety with collaborative mobile manipulator using machine learning techniques in the ColRobot project

The EU-funded project ColRobot focuses on the use of mobile manipulators for collaborative kitting and assembly tasks in automobile and satellite production. This paper focuses on the application and experimental validation of the workspace monitoring system built by the IFF in the ColRobot project. This includes the validation of machine learning techniques for the detection of humans in a shared workspace with the aim of offering soft-safety functionality (e.g. functionality which supports the process cycle time and at the same time increases human acceptance and satisfaction), as well as the validation of a visual workspace monitoring system for hard-safety functionality under adverse lighting conditions. Soft-safety functionality is complementary to hard-safety aspects, which can be defined as the requirements expressed in the relevant standards and which ensure that hazardous situations are mitigated in most cases by stopping the robot's motion. The robotic hardware - with a focus on the vision system, the application of the machine learning techniques to the task of human detection, and initial experimental results to validate the system will be described in this article.