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A robot is a complex multiple-input and multiple-output system. This paper presented the position that
multiple performance criteria must assess the performance of a robot. Though the paper offered no proof of this, consider the errors commonly encountered when programming an industrial robot, including: joint travel limits, joint speed limits (often reflecting singularities), motor current overloads, and workspace limits. An experienced operator will consider these and other limitations (such as obstacles) when
programming the robot. A reasonable motion coordination method should at least match this level of expertise. Ultimately, higher-level criteria addressing issues of geometry, force, compliance, and energy will refine the motion and further enhance performance.
As a basis for motion coordination, this paper presented and discussed a number of categories for performance
criteria. These criteria emphasize task-based performance indicators derived from the physical description of
the manipulator. The origins of these criteria are from foundation activity in high speed mechanisms for
production machinery (Benedict and Tesar, 1978). There, the issues of precision and modeling of complex non
linear structures forced the development of a geometric understanding for mechanical structures and how to
represent them with efficient analytical tools. Thomas and Tesar (1982) showed that the concept of kinematic
influence coefficients (used in systems with 1 DOF) were effective in spatial manipulator structures with N
DOF. The five basic categories for these measures are: constraint, geometry, inertial, compliance, and
kinetic energy.
This work outlined a method of motion coordination combining closed-form reverse position analysis, local
exploration, and multi-criteria decision making. The closed-form reverse position analysis satisfies the
placement constraints on the robot’s EEF (inverse kinematics). Using closed-form reverse position analysis
leverages over two decades of work by a number of dedicated scholars. The local explorations generate a set
of motion options for evaluation by the decision making process. Finally, the decision making process
evaluates the options based on a series of performance criteria and identifies one as the next set-point
command for the robot’s servo controllers.
This method of redundancy resolution has been tested (either in simulation or experimentation) on robots with 7, 8, 9, 10, 17, and 21 DOF. This paper presented simulation results for a dual-arm robot with 17 DOF. The results show successful motion coordination incorporating multiple criteria at a rate of over 100 cycles per second on a Pentium 100 personal computer.
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