1. IntroductionThe general goal of

1. IntroductionThe general goal of automatic robotic system development is to enable a robot to perform the desired tasks within the context of overall system requirements [1], and modeling and simulation are gradually being adopted as an integral part of the developmental process [2,3,4]. Modeling enables developers to explore hardware and software solutions before developing the actual component. In conjunction with simulation, it also enables the automatic evaluation of a much larger potential design space compared to a manual trial-and-error approach. The alternative approach to developing a robotic system involves time-intensive ad hoc trial-and-error testing to achieve a usable configuration of the physical system. One drawback of this approach is that developers may spend valuable time determining the optimal solution to some aspect of the system, only for such effort to show little impact on the overall desired outcome.The primary challenge of the modeling and simulation approach is that knowledge of many complementary disciplines, such as electrical, mechanical, software and embedded systems engineering and signal processing, is required to determine viable solutions [5,6,7]. These disciplines have different cultures, tools and methodologies, which may prove to be an impediment to cross-disciplinary projects. In this paper, we propose a collaborative modeling approach known as co-modeling that enables the combination of models from different disciplines. Collaborative simulations, or co-simulations, allow developers to examine different aspects of a system to explore design alternatives. They utilize models to describe the different aspects of the robotic system.The aim of the present study is the analysis of cross-disciplinary robotic design alternatives using co-simulation. This type of co-simulation-based analysis is known as a design space exploration (DSE) [8]. The co-model robot design is based on a mink feeding vehicle used in agricultural farming applications, as illustrated in Figure 1 and Figure 3. The co-modeling and co-simulation were accomplished by a combination of the Crescendo technology produced by the European Design Support and Tooling for Embedded Control Software (DESTECS) FP7 project [9,10] and a MATLAB extension.Figure 1. Three-dimensional visualization of a co-simulated load-carrying robot dispensing mink fodder.In the Crescendo technology, DSE is used to select viable candidate sensor positions on an R2-G2P line-following robot with a fixed controller setup [11]. Co-simulations performed using other tools apart from Crescendo have also been documented. For example, the MODELISAR [12] project developed the Functional Mock-up Interface (FMI), which enables co-simulation and model exchange between different domain-specific simulation frameworks. The standard FMI can support MATLAB/Simulink, Modelica, Python and C/C++, among other tools. In the Integrated Tool Chain for Model-based Design of Cyber-Physical Systems (INTO-CPS) project [13], the Crescendo technology is taken further in an FMI setting [14].Feeding robots used in animal husbandry have also been developed and documented. In [15], a static feeding system was used in combination with an RFID reader to dispense food to cows with the aid of an attached RFID tag. A mobile feeding platform was also used for outdoor piglet feeding in [16]. The pig-feeding robot was used to influence the behavioral pattern of the piglets to facilitate manure collection by daily changing of the feeding position in the field.The remainder of the paper is organized as follows. Section 2 describes the co-modeling technologies utilized for coupling the Crescendo technology with MATLAB. Section 3 introduces the robotic design challenge of the mink feeding ground vehicle as a system boundary definition consisting of a problem area and modeling case. Section 4 describes the co-modeling of the developed vehicle, design exploration and evaluated simulation conditions. The domain-specific modeling methods applied to the robot and its environment are documented in Section 5. Section 6 describes the signal processing and control. Section 7 presents the results of the simulations and an overview of the candidate solutions. Section 8 discusses the simulation results and setups that are considered to be capable of ensuring the expected performance under the required conditions. Finally, concluding remarks are made in Section 9.