This contribution introduces a generic framework for sensor-based robot motion control. The key contribution is the introduction of an adaptive selection matrix for sensor-based hybrid switched-system control. The overall control system consists of multiple sensors and open- and closed-loop controllers, in-between which the adaptive selection matrix can switch discretely in order to supply command variables for low-level controllers of robotic manipulators. How control signals are chosen, is specified by Manipulation Primitives, which constitute the interface to higher-level programming. This programming paradigm is briefly specified in order to be able to define and execute sensor-guided and sensor-guarded motion commands simultaneously. The resulting control system is freely adaptable depending on the sensor and control requirements of the desired system and/or application.

This paper deals with force/position control applied to robotic manipulators. It is well-known that force/position control for manipulators is a challenging task, because of strong nonlinearities exhibited by both the manipulator itself and by its environment. Despite the complexity inherent in manipulation processes, the classic single-loop feedback system with its advantages and disadvantages is still preferred in industrial use for force/position control. As interesting alternative we suggest the MFC-p (model-following control) two-loop control structure, which is based on a simple linear 2nd order model. The theoretical as well as the practical properties of this new control scheme are discussed in detail. It is shown that with our approach it is possible to achieve a much better control performance than that provided by a classic PID system. To prove the superiority of the concept, results with industrial manipulators under force control are shown

Mason's Task Frame Formalism (TFF) is supposed to deliver robot application programmers an intuitive and powerful programming interface. The open literature provides many theoretic approaches, but almost none of them is practicable. To bring these research results into practice is the major aim of this paper. Sets of manipulation primitives specify compliant motion commands, which let us execute complex robot tasks. We introduce an appropriate notation and focus on all significant TFF values, which have to be applied to the transformations, which are required on the control level. All essential calculations are derived; to highlight the meaning for practical implementations, an example, how to embed this knowledge in control architectures, is given.

The major purpose of this paper is to combine results of current robot force control research with scientific approaches in compliant motion, which are based on Mason's task frame formalism. The embedding of adaptive implicit hybrid force/pose control in a robot control architecture for compliant motion control is described. By the usage of adaptive force control, the practicability of compliant motion applications is improved. The applied control concept is constituted in a theoretical as well as in a practical manner. To highlight the meaning for practical implementations, experimental results with industrial manipulators under adaptive force control in three degrees of freedom are finally shown.

This paper presents a general approach to specify and execute complex robot tasks considering uncertain environments. Robot tasks are defined by a precise definition of so-called skill primitive nets, which are based on Mason's hybrid force/velocity and position control concept, but it is not limited to force/velocity and position control. Two examples are given to illustrate the formally defined skill primitive nets. We evaluated the controller and the trajectory planner by several experiments. Skill primitives suite very well as interface to robot control systems. The presented hybrid control approach provides a modular, flexible, and robust system; stability is guaranteed, particularly at transitions of two skill primitives. With the interface explained here, the results of compliance motion planning become possible to be examined in real work cells. We have implemented an algorithm to search for mating directions in up to three-dimensional configuration-spaces. Thereby, on one hand we have released compliant motion control concepts and on the other hand we can provide solutions for fine motion and assembly planning. This paper shows, how these two fields can be combined by the general concept of skill primitive nets introduced here, in order to establish a powerful system, which is able to automatically execute prior calculated assembly plans based on CAD-data in uncertain environments.