Contact
Robert KatzschmannArtificial Intelligence Laboratory
Stanford University
Stanford, CA 94305-9010
USA
Gates Building, Room 136
Phone: +1 650 739-3837
E-mail: katzschmann@cs.stanford.edu
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ContactRobert KatzschmannArtificial Intelligence Laboratory Stanford University Stanford, CA 94305-9010 USA Gates Building, Room 136 Phone: +1 650 739-3837 E-mail: katzschmann@cs.stanford.edu
Personal Blog |
Abstract: Generating robot motion trajectories instantaneously in the moment unforeseen sensor events happen is very essential for many real-world robot applications. Using a previous work on online trajectory generation as a basis, this paper proposes an alternative approach that also takes dynamic models into account. The former class of algorithms does not take into account dynamically changing acceleration capabilities based on maximum actuator forces/torques. This paper extends target velocity-based algorithms of the previous approach by taking into consideration the entire system dynamics when generating trajectories online within one control cycle (typically 1ms or less). The extension considers the acceleration capabilities of a robot at every discrete time step assuming constant values for the maximum actuator forces/torques, thus allowing the generation of adaptive trajectory profiles during the motion of the robot. Several real-world experimental results using a seven-degree-of-freedom lightweight robot arm underline the relevance of this extension.
Abstract: A concept of online trajectory generation for robot motion control systems enabling instantaneous reactions to unforeseen sensor events was introduced in former publications. This thesis extends the existing concept by allowing time-variant kinematic motion constraints being applied online to the algorithms, so that low-level trajectory parameters can now be changed abruptly, and the system can react instantaneously within the same control cycle of typically two milliseconds or less. The formerly proposed class of algorithms does not take into account dynamically changing acceleration capabilities for given kinematic and dynamic models of robot systems. This leads to the problem that the values of the motion constraints used for the online trajectory generation algorithms have to be chosen constant in its value and relatively low compared to the actual available acceleration capabilities of the robot. This assures on the one hand that the generated motion trajectory can be performed all the way through with, if at all, negligible tracking-errors. And on the other hand, this leads to a suboptimal reactiveness of the system, since it could potentially outperform more when accelerating and decelerating. This thesis extends the algorithms of the previous approach by taking into consideration the whole system dynamics when generating trajectories online. The extension considers the acceleration capabilities of a robot by looking ahead in time along its future motion path, thus allowing the generation of adaptive trajectory profiles during the motion of the robot. Real-world experimental results using a lightweight robot arm highlight the practical relevance of this extension.