A dynamic world model is presented on which all planning, sensing, and control functions are based. A planning system that specifies how tasks are to be performed and the method used for sensing the state of the task are presented. The objectives of the control law and the algorithm used in the controller are given. Practical considerations are addressed, and some important details, such as the use of the null task, are explained. An example task of opening a door is examined.>

Several algorithms used for the calibration of a manipulator are presented. The calibration includes the estimation of friction coefficients, position sensor calibration, and the motor torque constants. The model used for compensation of the gearing effects is described. The results of application of these algorithms to the PUMA 560 manipulator are presented.>

An adaptive controller is presented for the coordinated motion control of two manipulators handling an object of unknown mass. Global convergence in tracking of both position and internal force trajectories of the object is proved, assuming perfect models for both manipulators. The computational algorithm is similar to the Newton-Euler inverse dynamics algorithm with complexity linear in the number of links in the manipulators. A significant feature of the control method is that both manipulators use the identical computational algorithm. Thus, the concept of master/slave relationship between the two manipulators is avoided. Two simulations are presented. The first assumes an ideal model for each manipulator. As expected, the controller is stable and provides excellent tracking ability of the manipulator. The second simulation investigates the effects of modeling errors in the mass properties of each arm. The position trajectories track very close to their desired values; however, they never completely converge.>