Real-life work operations of industrial robotic manipulators are performed within a constrained state space. Such operations most often require accurate planning and tracking a desired trajectory, where all the characteristics of the dynamic model are taken into consideration. This paper presents a general method and an efficient computational procedure for path planning with respect to state space constraints. Given a dynamic model of a robotic manipulator, the proposed solution takes into consideration the influence of all imprecisely measured model parameters, making use of iterative learning control (ILC). A major advantage of this solution is that it resolves the well-known problem of interrupting the learning procedure due to a high transient tracking error or when the desired trajectory is planned closely to the state space boundaries. The numerical procedure elaborated here computes the robot arm motion to accurately track a desired trajectory in a constrained state space taking into consideration all the dynamic characteristics that influence the motion. Simulation results with a typical industrial robot arm demonstrate the robustness of the numerical procedure. In particular, the results extend the applicability of ILC in robot motion control and provide a means for improving the overall trajectory tracking performance of most robotic systems.

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ROBCO 12 is an Intelligent Modular Service Mobile Robot for Elderly and/or Disabled Persons Care. This robot will "live" in the home of the elderly and/or disabled person and help him/her 24 hours a day. It will be able to remind him/her to take medications, it will serve food and drinks, will turn on/off electronic devices, will alert when his/her health is getting worse and will connect to his physician, relatives or with the emergency services telephone 112.

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