The seamless integration of electrical, thermal, mechanical domains and the environment allow component matching to take place through optimization routines and from desktop prior to physical prototypes being created. The physical models are more than real time capable.

The model code can be compiled to be independent of licence. This allows the models to be distributed and embedded onto controllers for predictive range calculations taking into account environmental, payload and mission inputs. The model export can also be used to export models for use in other environments such as Matlab Simulink.

Scale your overall or specific subsystem detail to suit your analysis and optimize the balance between detail and simulation performance. From inverse models, where objectives can be dialed in as boundary conditions, to forward dynamic models that range from map based to fully predictive equation based, the choice lies entirely with the user.

Amongst the many aspects of the dynamics modelling capability of the library, variable payload capability can be exploited to analyse the controller robustness for a range of real world scenarios. When coupled to our MultiRun tool, 1000s of scenarios can be run in parallel on multiple cores and compared to previous design iterations.

Physical fault injection such as short and open circuits, battery cell failure, internal combustion engine faults and mechanical failures enables critical safety aspects to be investigated. The predictive multi-body dynamics capture the consequences of component failure on the overall drone allowing the fail safe design of the entire system in a virtual environment.

Using the built-in Python and Java interfaces we can run and control the physical models externally using either of these languages.