Abstract
Rapid robotic system development has created a demand for multi-disciplinary methods and tools to explore and compare design alternatives. In this paper, we present a collaborative modeling technique that combines discrete-event models of controller software with continuous-time models of physical robot components. The proposed co-modeling method utilizes the Vienna development method (VDM) and MATLAB for discrete-event modeling and 20-sim for continuous-time modeling. The model-based development of a mobile robot mink feeding system is used to illustrate the collaborative modeling method. Simulations are used to evaluate the robot model output response in relation to operational demands. An example of a load-carrying challenge in relation to the feeding robot is presented, and a design space is defined with candidate solutions in both the mechanical and software domains. Simulation results are analyzed using design space exploration (DSE), which evaluates candidate solutions in relation to preselected optimization criteria. The result of the analysis provides developers with an overview of the impacts of each candidate solution in the chosen design space. Based on this overview of solution impacts, the developers can select viable candidates for deployment and testing with the actual robot.
Highlights
The 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]
Further information about the individual co-simulations can be gained by visualizing the response in terms of mink fodder placement
The white rectangles on the side wall represent the placement of the radio frequency identification (RFID) tags, illustrating their impact on the mink fodder placement
Summary
The 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]. 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
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