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It is shown that a special shovel is a feature of the structure of the drives of the main mechanisms (mechanisms of lifting and pressure) of career excavators with working equipment, the presence in the transfer device of a two-crank-lever mechanism of working equipment that connects the main mechanisms with the working body (bucket). In this case, the transformation of the mechanical energy parameters of the motors into energy-force parameters realized at the cutting edge of the bucket (teeth) takes place depending on the type of the kinematic scheme of the two-link-lever mechanism. The concept of "control function" defining the relationship between the parameters characterizing the position of the bucket in the face (the coordinates of the tip of the cutting edge of the bucket, the digging speed) and the required control level are introduced. These are the values of the lifting and head speeds ensuring the bucket movement along a given trajectory. © Published under licence by IOP Publishing Ltd.

This work describes a method to extend classical maps in terms of additional information and a prediction about objects within the environment. The prediction system is based on the behaviour of the observed objects and influences accordingly the updating of the map. First all objects are classified due to their size and further parameters characterizing the ability to move. Furthermore the velocity and orientation of objects within the visible area of the autonomous system are extracted from a vision sensor. In case of a mobile autonomous system they help to adjust its path in real time. Additionally all objects are tracked. In order to generate the statistical map a statistical indicator is introduced describing the possible future positions of objects. Thus the conventional maps can be improved by adding information about the status of the considered space. Furthermore the status of objects can be predicted even when they are not visible anymore. In the case of a mobile system, it will improve the awareness drastically enabling it to act pre-emptively and improve the human-machine interaction in e.g. a production environment. This work describes a method to extend classical maps in terms of additional information and a prediction about objects within the environment. The prediction system is based on the behaviour of the observed objects and influences accordingly the updating of the map. First all objects are classified due to their size and further parameters characterizing the ability to move. Furthermore the velocity and orientation of objects within the visible area of the autonomous system are extracted from a vision sensor. In case of a mobile autonomous system they help to adjust its path in real time. Additionally all objects are tracked. In order to generate the statistical map a statistical indicator is introduced describing the possible future positions of objects. Thus the conventional maps can be improved by adding information about the status of the considered space. Furthermore the status of objects can be predicted even when they are not visible anymore. In the case of a mobile system, it will improve the awareness drastically enabling it to act pre-emptively and improve the human-machine interaction in e.g. a production environment.