This award supports investigation of the problem of sensor- referenced action planning and control for robotic systems in a workstation setting, superimposed on model-based planning and control which has been the norm for robotic system control. The paradigm is to model the known and sense the unknown in the task space and implement the planning and control accordingly. Tasks will be formulated in terms of events sensed in the task space, which in many cases are represented by a pattern of fused information from different sensors. Ultimately, this machine intelligence approach will provide high-level decision making capability for automation, promote system integration, and lead to more user-friendly planning and decision/control systems for robotic workstations. To accomplish this, a number of problems must be addressed. Initially, the research will address a new type of motion description and efficient algorithmic formulation of kinematic and dynamic action capabilities of robot arms. The motion description uses a phase-space approach (velocity vs. position) instead of the usual state-space (position vs. time) approach. Preliminary results indicate that motion description and planning in phase-space is more compatible with sensor referenced control that is time-based planning and control. The algorithmic formulation of action capabilities used the known parameters, both of the arms and the task environment in order to provide a model-based foundation for sensor-referenced intelligent action planning and control.
