There is a growing demand for autonomous mobile robots in industry. The robots need to solve, among other things, the problem of navigation. Since the robots operate in semi-structured and (partially) unknown environments, the local path planning sub-problem has received a lot of attention from researchers. The result is a large variety of algorithms to solve this sub-problem, which are often developed and optimized for a specific scenario of environment. This gives the robots a large set of different possible system configurations. Traditionally, the engineer makes the choice at design-time about which system configuration to use. However, there is a demand for more autonomy in robotic systems, and it is therefore desired to have the robot make this choice at run-time. Self-adaptive systems are typically used to switch between system configuration at run-time.
The aim of this research is to improve the knowledge of the system about the navigation quality in different environments. This knowledge will help the self-adaptive system to make a better informed decision about adaptation. The contribution of this research is an approach to construct this knowledge for the system. First, metrics for navigation quality in terms of safety and performance and relevant environment characteristics in relation to the navigation quality are developed. Machine learning methods are used to train quality models that can be used to predict the safety level of a system configuration based on the measurements of the environment characteristics. These quality models are implemented in the MROS self-adaptive framework, and experiments showed that the use of quality models in this framework is useful to make better informed adaptation choices and and improves the navigation quality.