5 research outputs found
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Optimizing robot placement for visit-point tasks
We present a manipulator placement algorithm for minimizing the length of the manipulator motion performing a visit-point task such as spot welding. Given a set of points for the tool of a manipulator to visit, our algorithm finds the shortest robot motion required to visit the points from each possible base configuration. The base configurations resulting in the shortest motion is selected as the optimal robot placement. The shortest robot motion required for visiting multiple points from a given base configuration is computed using a variant of the traveling salesman algorithm in the robot joint space and a point-to-point path planner that plans collision free robot paths between two configurations. Our robot placement algorithm is expected to reduce the robot cycle time during visit- point tasks, as well as speeding up the robot set-up process when building a manufacturing line
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General techniques for constrained motion planning
This report presents automatic motion planning algorithms for robotic manipulators performing a variety of tasks. Given a task and a robot manipulator equipped with a tool in its hand, the motion planners compute robot motions to complete the task while respecting manipulator kinematic constraints and avoiding collisions with objects in the robot`s work space. To handle the high complexity of the motion planning problem, a sophisticated search strategy called SANDROS is developed and used to solve many variations of the motion planning problem. To facilitate systematic development of motion planning algorithms, robotic tasks are classified into three categories according to the dimension of the manifold the robot tool has to travel: visit-point (0 dimensional), trace-curve (1 dimensional) and cover-surface (2 dimensional) tasks. The motion planner for a particular dimension is used as a sub-module by the motion planner for the next-higher dimension. This hierarchy of motion planners has led to a set of compact and systematic algorithms that can plan robot motions for many types of robotic operations. In addition, an algorithm is developed that determines the optimal robot-base configuration for minimum cycle time. The SANDROS search paradigm is complete in that it finds a solution path if one exists, up to a user specified resolution. Although its worst-case time complexity is exponential in the degrees of freedom of the manipulator, its average performance is commensurate with the complexity of the solution path. Since solution paths for most of motion planning problems consist of a few monotone segments, the motion planners based on SANDROS search strategy show approximately two-orders of magnitude improvements over existing complete algorithms
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Geometric simplification of analysis models
Analysis programs have been having to deal with more and more complex objects as the capability to model fine detail increases. This can make them unacceptably slow. This project attempts to find heuristics for removing features from models in an automatic fashion in order to reduce polygon count. The approach is not one of theoretical completeness but rather one of trying to achieve useful results with scattered practical ideas. By removing a few simple things such as screw holes, slots, chambers, and fillets, large gains can be realized. Results varied but a reduction in the number of polygons by a factor of 10 is not unusual
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Coordinating robot motion, sensing, and control in plans. LDRD project final report
The goal of this project was to develop a framework for robotic planning and execution that provides a continuum of adaptability with respect to model incompleteness, model error, and sensing error. For example, dividing robot motion into gross-motion planning, fine-motion planning, and sensor-augmented control had yielded productive research and solutions to individual problems. Unfortunately, these techniques could only be combined by hand with ad hoc methods and were restricted to systems where all kinematics are completely modeled in planning. The original intent was to develop methods for understanding and autonomously synthesizing plans that coordinate motion, sensing, and control. The project considered this problem from several perspectives. Results included (1) theoretical methods to combine and extend gross-motion and fine-motion planning; (2) preliminary work in flexible-object manipulation and an implementable algorithm for planning shortest paths through obstacles for the free-end of an anchored cable; (3) development and implementation of a fast swept-body distance algorithm; and (4) integration of Sandia`s C-Space Toolkit geometry engine and SANDROS motion planer and improvements, which yielded a system practical for everyday motion planning, with path-segment planning at interactive speeds. Results (3) and (4) have either led to follow-on work or are being used in current projects, and they believe that (2) will eventually be also