Real-time computation of the inverse dynamics of robotic manipulators is required for ensuring robust control. This thesis presents a modified Newton-Euler algorithm which makes use of symbolic programming for improved computational efficiency. A scheme for modeling the frictional effects at the joints as well as the transmissions for robotic mechanisms is outlined with an illustrative case-study for the PUMA-560 manipulator. The algorithm is parallelized using a Task Streamlining Approach' - a systematic mapping scheme using layered task graphs to create the list schedule and a simplified bin-packing heuristic algorithm to schedule the computations on a multiprocessor. The resulting computational load for dynamic torques without friction, is only 12n+9 arithmetic operations, where n is the number of links in the manipulator, indicating a promise for application to precision robot control employing a high sampling rate
