Modern mobile machines are being automated in increasing rate and this automation is mostly achieved through software. Thus, a large fraction of added value is gained from creating the software, so it is important to increase efficiency of its production.

 

Since software engineering is a young discipline and very different compared to more traditional engineering, there are not as many standards and generally accepted processes for production. This situation is changing, and standards and general processes are emerging from within the industry.

However, it is not always clear if a new process or a software tool increases productivity. Empirical software engineering as a discipline has emerged in research community to rigorously evaluate the efficacy of such aspects.

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In the ESEAM, the goal is to collect evidence on different methods and tools that increase productivity in creating software for autonomous mobile machines. The main source of data will be small scale software projects done by volunteer students that increase autonomy of small robot systems. These small robots simulate real mobile machines.

For example, the project topics could be implementing a real-time shared map/data structure from observations from multiple machines, user interface for simulated warehouse or to add a new actuator both physically and in software to a pre-existing robot.

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More accurate research questions will be formed by initial survey study and the feedback from the steering council. This project is in tight collaboration with PEAMS, so the objects of study in PEAMS will be used in here, such as ROS2.