No abstract

Abstract. We describe an approach to the model-based engineering of embedded and cyber-physical systems, based on the semantic integration of diverse discipline-specific notations and tools. Using the example of a small unmanned aerial vehicle, we explain the need for multiple notations and collaborative modelling. Learning from experience with binary co-modelling based on a bespoke operational semantics, we describe current work delivering an extended approach that enables integration of multiple models and tools in a consistent tool chain, founded on an extensible semantic framework exploiting the Unifying Theories of Programming.

Development of distributed software systems is complex due to the distribution of resources, which complicates validation of system-wide functionality. Such systems include various facets like functionality and distribution, each of which must be validated and integrated in the final software solution. Model-based techniques advocate various abstraction approaches to cope with such challenges. To enhance model-based development, this paper proposes (1) guidelines for development of distributed systems, where the different facets are introduced gradually through systematic modeling extensions, (2) code generation capabilities supporting technology specific realizations, and (3) demonstration of the applicability of our approach using an industrial case study involving the development of a harvest planning system, where the communication infrastructure paradigm changed late in the project. When developing this system, we spent most time validating system-wide functionality. The model extensions allowed an easier change of the underlying communication paradigm and code generation supported realization of the different system representations.

Noname manuscript No.

Operational planning, automation, and optimisation of field operations are ways to sustain the production of food and feed. A coverage path planning method mitigating the optimisation and automation of harvest operations, characterised by capacity limitations and features derived from real world scenarios, is presented. Although prior research has developed similar methods, no such methodologies have been developed for (i) multiple field entrances as line segments, (ii) the feasibility of stationary and on-the-go unloading in the headland and main field, (iii) unloading timing independent of the full bin level of the harvester, and (iv) the transport unit operational time outside the field. To find the permutation that best minimises the costs in time and distance, an artificial bee colony (ABC) algorithm was used as a meta-heuristic optimisation method. The effectiveness of the method was analysed by generating simulated operational data and by comparing it to recorded data from seven fields ranging in size (5–26 ha) and shape. The implementation of controlled traffic farming (CTF) in the coverage path planning method, but not with the recorded data, resulted in a reduced risk of soil compaction of up to 25%, and a reduction in the in-field total travel distance of up to 15% when logistics was optimised simultaneously for two transport units. A 68% increase in the full load frequency of transporting vehicles and a 14% reduction in the total number of field to storage transports was observed. For fields located at outermost edges of the storage facility (>5 km), the increase in full load frequency, average load level, and decrease in in-field travel distance resulted in a reduction in fuel consumption by 7%. Embedding the developed coverage path planning software as a service will improve the sustainability of harvest operations including a fleet of one to many harvesting and transporting units, as the system in front of the vehicle operator calculates and displays all required actions from the operator.