Human-Robot Collaboration in Automotive Industry

Human–Robot Collaboration is a new trend in the field of industrial and service. Application of human-robot-collaboration techniques in automotive industries has many advantages on productivity, production quality and workers’ ergonomic; however, workers’ safety aspects play the vital role during this collaboration. Previously, the machine is allowed to be at automatic work only if operators are out of its workspace but today collaborative robots provide the opportunity to establish the human robot cooperation. In this thesis, efforts have been made to present innovative solutions for using human-robot collaboration to develop a manufacturing cell. These solutions are not only used to facilitate the operator working with collaborative robots but also consider the worker safety and ergonomic. After proposing different solutions for improving the safety of operations during the collaboration with industrial robots, the efficiency of the solutions is tested in both laboratory and virtual environments. In this research, firstly, Analytic Hierarchy Process (AHP) has been used as a potential decision maker to prove the efficiency of human-robot collaboration system over the manual one. In the second step, detailed task decomposition has been done using Hierarchical Task Analysis (HTA) to allocate operational tasks to human and robot reducing the chance of duty interference. In the International Organization of Standardization's technical specification 15066 on collaborative robot safety four methodologies have been proposed to reduce the risk of injury in the work area. The four methods implied in ISO/TS 15066 are safety-rated monitored stop (SMS), hand-guided (HG), speed and separation monitoring (SSM) and power force limiting (PFL). SMS method reduces the risk of operator’s injury by stopping the robot motion whenever the operator is in the collaborative workspace. HG method reduces the chance of operator’s injury by providing the possibility of having control over the robot motion at all times in the workstation using emergency system or enabling device. The SSM method determines the minimum protective distance between a robot and an operator in the collaborative workspace, below which the robot will stop any kind of motion and PFL method reduces the momentum of a robot in a way that contact between an operator and the robot will not cause any injury. After determining the requirements and specifications of hybrid assembly cell, few of the above-mentioned methods for evaluating the safety of human-robot-collaboration procedure have been tasted in the laboratory environment. Due to the lack of safety camera (sensors) in the laboratory workstation, the ISO methods such as SSM, that needs sensors in the workstation, have been modeled in virtual environment to evaluate different scenario of human-robot-interaction and feasibility of the assembly process. Implementing different scenarios of ISO methods in hybrid assembly workstation not only improves the operator safety who is in interaction with the collaborative robot but also improves the worker ergonomic during the performing of repetitive heavy tasks

Development of new intelligent autonomous robotic assistant for hospitals

Continuous technological development in modern societies has increased the quality of life and average life-span of people. This imposes an extra burden on the current healthcare infrastructure, which also creates the opportunity for developing new, autonomous, assistive robots to help alleviate this extra workload. The research question explored the extent to which a prototypical robotic platform can be created and how it may be implemented in a hospital environment with the aim to assist the hospital staff with daily tasks, such as guiding patients and visitors, following patients to ensure safety, and making deliveries to and from rooms and workstations. In terms of major contributions, this thesis outlines five domains of the development of an actual robotic assistant prototype. Firstly, a comprehensive schematic design is presented in which mechanical, electrical, motor control and kinematics solutions have been examined in detail. Next, a new method has been proposed for assessing the intrinsic properties of different flooring-types using machine learning to classify mechanical vibrations. Thirdly, the technical challenge of enabling the robot to simultaneously map and localise itself in a dynamic environment has been addressed, whereby leg detection is introduced to ensure that, whilst mapping, the robot is able to distinguish between people and the background. The fourth contribution is geometric collision prediction into stabilised dynamic navigation methods, thus optimising the navigation ability to update real-time path planning in a dynamic environment. Lastly, the problem of detecting gaze at long distances has been addressed by means of a new eye-tracking hardware solution which combines infra-red eye tracking and depth sensing. The research serves both to provide a template for the development of comprehensive mobile assistive-robot solutions, and to address some of the inherent challenges currently present in introducing autonomous assistive robots in hospital environments.Open Acces