PERFORMANCE EVALUATION OF THE UT AUTOMATED ROAD MAINTENANCE MACHINE
This final report mainly focuses on evaluating the overall performance of the University of Texas' (UT's) Automated Road Maintenance Machine (ARMM). First, the ARMM's man-machine-balanced control loop was further developed then rigorously tested and evaluated based on (1) accuracy, (2) time, and (3) quality of the resultant seal. For the efficiency evaluation, thirty pavement crack images, which included longitudinal, transverse, and block cracking, were collected from a UT research campus, and field trials were completed at five locations (Austin, San Antonio, Dallas, Corpus Christi, and Travis County) in the state of Texas. Several additional significant improvements were made to the ARMM during the course of this project. Second, the ARMM's productivity was estimated based on (1) the results of the efficiency evaluation of the man-machine control loop, (2) observations made during the series of field trials, and (3) a productivity model. The mathematical model that predicts the productivity of the ARMM under various work conditions was developed as a means for job estimating and for rating the performance of the ARMM. The ARMM's estimated productivity was then compared with the typical productivity rate associated with conventional crack sealing methods. Evaluation results from the field trials and implementation recommendations are also made in this final project report. It was concluded that the introduction of automated methods to the pavement crack-sealing process will improve productivity and quality and can reduce costs and safety risks. The latter is a direct result of reducing normal crew sizes of seven to eight workers to only three to four workers. The reduction in crew size and the increase in productivity of the sealing process translate directly into significant potential cost savings. By automatically recording work completed, the ARMM should help improve project controls; by its ability to work at night, the ARMM should reduce road user costs.
Advances in control systems for construction manipulators
Abstract Fundamental advances in sensors, actuators, and control systems technology are creating opportunities to improve the performance of traditional construction equipment. New capabilities are being developed as well. These improvements in performance and new capabilities are resulting in better safety and efficiency. However, selecting control strategies can be confusing, and measuring and predicting their performance can be difficult. This paper identifies emerging control paradigms and describes methods for measuring their performance. Many control schemes and corresponding example applications are identified, including single degree of freedom control sticks, multiple degree of freedom joysticks, operating and safety constraints, teach/learn capability, resolved motion with internal and external sensors, spatially correspondent controllers, tele-operation, graphical programming and control, and autonomous controls. Methods described for measuring performance are based on American National Standard Institute (ANSI) standard tests, applications analysis, and ergonomics. Examples focus on the University of Texas at Austin's large scale hydraulic manipulator (LSM) and Automated Road Maintenance Machine (ARMM) with the results of performance tests on these manipulators being presented.
PACIFIC RIM TRANSTECH CONFERENCE PROCEEDINGS VOLUME II. COMPARISON OF MULTI-SENSING METHODS FOR THE DETECTION OF CRACKS IN PAVEMENT SURFACES
Resesarch in pavement management systems and the development of new technologies for the automated perception of distress in pavement surfaces, and laser range sensors are described. The paper describes three methods for integrating vision and range data, with special emphasis on the reduction of time spent in range scanning. This study developed a comparison model to evaluate the three crack perception methods. The models are compared with respect to the following: time required for perception of cracks; costs; accuracy; and quality of data obtained. Conclusions drawn from the study are presented.