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Please use this identifier to cite or link to this item: http://hdl.handle.net/1813/6853
Title: What Should a Roboticist do Next? A Progress Report From the Cornell Computer Science Robotics Laboratory
Authors: Donald, Bruce Randall
Pai, Dinesh K.
Xavier, Patrick G.
Keywords: computer science
technical report
Issue Date: Nov-1989
Publisher: Cornell University
Citation: http://techreports.library.cornell.edu:8081/Dienst/UI/1.0/Display/cul.cs/TR89-1053
Abstract: The most important intellectual capital of a research laboratory is its coherent vision of science, research and progress. In this progress report, we identify key areas of research, describe the progress we have made in attacking these areas, and discuss our plans for future work. Our primary goal is to bring robotics science closer to its goal of task-level planning. We approach this goal through a blend of theory, implementation, and experimentation. A major impediment to developing truly task-level robotic systems has been the very hard algorithmic problems that arise in task-level robot planning. We have identified several key areas on which to concentrate in developing new algorithmic technologies to crack these problems. Roughly speaking, these areas are: 1. Basic research in compliant motion planning under uncertainty. This research will result in algorithms and systems that can assemble mechanical parts using compliant motion strategies, despite uncertainty and errors in sensing, control, and modeling. 2. Basic research in planning with full dynamics. It is vital that robots execute tasks quickly, and take dynamics into account. Our research on kinodynamic planning provides the first provably good approximation algorithms for planning nearly time-optimal collision-avoiding paths that respect dynamics bounds. 3. Basic research in design for assembly. The design of mechanical devices and the planning to assemble them should not be independent activities. We introduce a new, fully algorithmic, combinatorially precise approach to designing devices so that they are easy to assemble and (optionally) hard to disassemble. Our analysis can be used to validate good designs, and can be iterated to generate improved designs.
URI: http://hdl.handle.net/1813/6853
Appears in Collections:Computer Science Technical Reports

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