Munitions are currently loaded on fighter aircraft using 1950s technology. The United States Air Force and Navy have an on-going joint program to determine the feasibility of significantly enhancing the capabilities of munitions handling equipment by incorporating emerging technologies into a new system design. The program was initiated by a study sponsored by the Munitions Material Handling Equipment focal point who also maintains overall program management. The Air Force Material Command Robotics and Automation Center of Excellence is providing technical direction for the design and development of the next generation munitions handler advanced technology demonstrator. Oak Ridge National Laboratory has developed the detailed conceptual design and is currently manufacturing system components. A fully automated solution is not technically feasible. More importantly, the munitions handling community is not looking to replace human operators, but rather enhance their efficiency while improving working conditions and reducing workload. The solution lies in the incorporation of emerging technologies into the munitions handling process.

The time is right to transition telerobotics beyond the traditional hazardous environment domain into industrial repair and remanufacturing applications. Air Force depots are prime examples of an industrial environment where small batch sizes, feature uncertainty, and varying workload conspire to make classical industrial robotic solutions impractical and telerobotics a key enabling technology. The United States Air Force, Air Force Material Command, Robotics and Automation Center of Excellence (RACE) has launched the Unified Telerobotics Architecture Project (UTAP) to champion the development of the support infrastructure necessary to foster creative development and innovative utilization of emerging telerobotic technologies for depot applications. The UTAP program is the focus of this paper. >

The feasibility of using principal base parameter analysis (PBPA) as an aid in the design and tuning of adaptive model-based controllers for industrial manipulators is investigated. Results from PBPA are utilized to select the minimal size of the adaptive parameter vector and to develop a less heuristic procedure for controller tuning. The design procedure is illustrated by a simple two link example and then extended to the first three links of a PUMA-560. Experimental analysis contrasted with an adaptive model-based control (AMBC) design augmented with PBPA to a completely heuristic procedure used in previous research. The incorporation of PBPA into the AMBC design minimized the computational complexity while reducing the time and expertise necessary to tune the controller for satisfactory tracking efficacy.>

The application of robotic technologies to aircraft servicing is addressed, specifically the concept of semi-autonomous ground-based robotic refueling. Recognition of the refueling port in realistic lighting conditions was mandatory. A brightness invariant port recognition system (BIPRS) which relies on edge detection, line merging, loop formation, and knowledge-driven recognition was developed and experimentally validated. The BIPRS identified the simulated refueling port in different lighting conditions and was invariant to port orientation and size. BIPRS development is discussed, and highlights of experimental validation are presented. The pattern recognition portion of the refueling task is clearly feasible with current technology.>

The feasibility of applying the principles of passivity-based direct adaptive control to the industrial manipulator trajectory tracking problem was rigorously investigated. The first three links of a PUMA-560 were used as a case study. Experimental analysis verified the algorithm's adaptation/learning capabilities and provided valuable insight into the implementation issues of tuning, parameter initialization and convergence, and asynchronous adaptation. Direct adaptive control clearly demonstrated the ability to enhance trajectory efficacy and significantly reduce payload sensitivity.>

A concept is proposed for utilizing artificial neural networks to enhance the high-speed tracking accuracy of robotic manipulators. Tracking accuracy is a function of the controller's ability to compensate for disturbances produced by dynamical interactions between the links. A model-based control algorithm uses a nominal model of those dynamical interactions to reduce the disturbances. The problem is how to provide accurate dynamics information to the controller in the presence of payload uncertainty and modeling error. Neural network payload estimation uses a series of artificial neural networks to recognize the payload variation associated with a degradation in tracking performance. The network outputs are combined with a knowledge of nominal dynamics to produce a computationally efficient direct form of adaptive control. The concept is validated through experimentation and analysis on the first three links of a PUMA-560 manipulator. A multilayer perceptron architecture with two hidden layers is used. Integration of the principles of neural network pattern recognition and model-based control produces a tracking algorithm with enhanced robustness to incomplete dynamic information. Tracking efficacy and applicability to robust control algorithms are discussed.

A form of robust model-based control for a robotic arm is proposed and experimentally evaluated. The model-based with quantitative feedback theory (MBQFT) technique replaces the proportional-derivative feedback loop with a controller design based on pseudocontinuous time analog quantitative feedback theory. MBQFT design produces a feedback controller that can be implemented by a series of simple backwards difference equations. MBQFT high-speed tracking accuracy was experimentally evaluated on the first three links of a PUMA-560. This robust technique increased tracking accuracy by up to a factor of four over the model-based controller performance baseline. The MBQFT tracking performance was robust to both unmodeled drive system dynamics and payload uncertainty. The nonheuristic nature of MBQFT design and tuning should allow application to a wide range of manipulators.>

A form of adaptive model-based control is proposed and experimentally evaluated. An adaptive model-based neural network controller (AMBNNC) uses multilayer perceptron artificial neural networks to estimate the payload during high-speed manipulator motion. The payload estimate adapts the feedforward compensator to unmodeled system dynamics and payload variation. The neural nets are trained through repetitive training on trajectory tracking error data. The AMBNNC was experimentally evaluated on the third link of a PUMA-560 manipulator. Tracking performance was evaluated for a wide range of payload and trajectory conditions and compared to a nonadaptive model-based controller. The superior tracking accuracy of the AMBNNC demonstrates the potential of the technique.>

Three forms of robust model-based control were experimentally evaluated. Algorithm evaluation was prompted by a requirement for controllers with good high-speed tracking accuracy in uncertain payload environments. The test case was a PUMA-560 operating over the standard test suite. The tracking performance of the robust algorithms was compared, with and without payload, to that of a nonadaptive model-based controller with fixed proportional-derivative (PD) feedback. The model-based controllers were made robust by the addition of an auxiliary input term, replacing the PD feedback with a feedback loop based on quantitative feedback theory (QFT), or an adaptive feedforward compensator based on Lyapunov theory. Experimental evaluation provided valuable insight into the potential and limitations of each method. All three techniques improved the tracking performance of the manipulator. Superior overall performance, computational simplicity, and a mathematically rigorous design and tuning procedure make the model-based controller with QFT feedback the algorithm of choice.>

The first step in the creation of a robotic manipulator controller performance database has been completed. Experimental evaluation of the trajectory-tracking performance of a PUM-600 under computed-torque control has provided valuable engineering insight into the role of dynamics compensation for manipulators with high torque amplification drive systems. Experimental results validate the assumptions that Coriolis and centrifugal forces are negligible and actuator dynamics play a central role in manipulator dynamics. Without complete modeling of the drive-system dynamics, computed-torque trajectory-tracking accuracy was inadequate for gross motion control. It is concluded that more accurate representation of the real robot system dynamics is required for realistic simulation of modern control algorithms and improved real-time performance.>

The relationship between tracking accuracy and the model used in dynamics-based control of a vertically articulated manipulator has been experimentally evaluated. High gear ratios reduce the payload sensitivity and computational complexity of the system dynamics but do not eliminate the performance enhancement from accurate compensation of dominant dynamics. Robot controllers must compensate for changes in dominant drive system, link, and payload dynamics to achieve high speed trajectory tracking accuracy. Feedforward dynamic compensation reduced the peak trajectory tracking errors of an independent-joint PD (proportional differential) controller by a factor of three. Knowledge of payload mass and centroid allowed feedforward dynamic compensation to overcome the disturbances produced by unmodeled payload dynamics. The combination of high sample rates, constant PD gains, and adaptive feedforward dynamic compensation produces a control algorithm with excellent payload independent, peak, and final error.>

Forces unmodeled by Lagrange-Euler dynamics restrict the trajectory tracking accuracy of dynamics based PUMA manipulator control laws. The ability of unmodeled force compensation techniques to eliminate those restrictions, enabling dynamics based PUMA controllers to accurately track a high speed trajectory, has been identified. Implementation of nonlinear velocity dependent friction compensation in the feedforward loop significantly improves controller efficacy. A combination of feedforward and feedback compensation techniques permits tracking accuracy sufficient for gross motion control of a PUMA manipulator without additional instrumentation. The effectiveness of proposed modern control theories can now be compared to an experimentally determined manipulator control performance baseline.

Evaluation of the effects of dynamics on real-time closed loop robotic manipulator joint control requires accurate, computationally efficient dynamics algorithms. Through a study of open loop joint torques the dominant and negligible torque components for each link of a PUMA-600 have been identified so that prudent model reduction could be accomplished. An acceleration related matrix model which requires only 94 multiplications and 59 additions while achieving accuracy to within ten percent of the complete model has been developed and justified. Reduction of the Newton-Euler PUMA- 600 dynamics to 304 multiplications and 212 additions has also been achieved without any loss of accuracy. The developed algorithms support our real-time study of the effects of dynamics on robot control.

A Hierarchical Control System with organizer, coordinator and hardware levels has been developed. A VAX-11/750 serves as the organizer. An LSI-11/23 equipped with FPF11 floating point processor is the basis of the coordinator. The original controller for a PUMA-600 robotic manipulator has been modified to serve as the hardware level. Organizer level software has been created for communication and control of the coordinator. Coordinator command and control software development has duplicated and expanded upon the functions of VAL. Real-time evaluation of modern robotic manipulator control methods on a PUMA-6O0 is now possible.

The real-time and simulation performance of the computed-torque joint control technique employing four configurations of dynamic models in controlling a six degree of freedom robotic manipulator has been thoroughly evaluated over a set of twelve different operational situations. Simulation comparisons have been conducted to predict the repercussions of neglected dynamics on computed-torque performance. Real-time tests have been conducted using a PUMA-600 and modified controller. Real-time results do not adhere to the simulation predictions. The algorithm with the simplest dynamics performs best in real-time.

Design modifications on the controller of the PUMA- 600 arm are required to apply modern control techniques in real-time. These modifications are performed on the computer-level and the joint control level of the PUMA controller. Real-time closed loop control of the arm is thus made possible. The paper compares the performance of various control algorithms in real-time and by simulation.