Trajectory Generation

Online Trajectory Generation Considering Robot Dynamics and Torque Limits

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. The former class of algorithms does not take into account dynamically changing acceleration capabilities based on maximum actuator forces/torques. This work extends target velocity-based algorithms of the previous approach. Several real-world experimental results using a seven-degree-of-freedom lightweight robot arm underline the relevance of this extension.

  • [PDF] [DOI] R. K. Katzschmann, T. Kroger, T. Asfour, and O. Khatib, “Towards online trajectory generation considering robot dynamics and torque limits,” in Ieee international conference on intelligent robots and systems, 2013, pp. 5644-5651.
    [Bibtex]
    @inproceedings{katzschmann2013towards,
    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 considers dynamic models. 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 1 ms or less). The extension includes 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.},
    author = {Katzschmann, Robert K and Kroger, Torsten and Asfour, Tamim and Khatib, Oussama},
    booktitle = {IEEE International Conference on Intelligent Robots and Systems},
    doi = {10.1109/IROS.2013.6697174},
    pages = {5644--5651},
    title = {{Towards online trajectory generation considering robot dynamics and torque limits}},
    year = {2013}
    }

Dynamic Online Trajectory Generation – Acceleration Capabilities Considered for Real-Time Path Planning

Online trajectory generation for robot motion control systems enables instantaneous reactions to unforeseen sensor events. This thesis extends this existing concept by allowing time-variant kinematic motion constraints being applied online to the algorithms. 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. Real-world experimental results using a lightweight robot arm highlight the practical relevance of this extension.

  • [PDF] R. K. Katzschmann, “Dynamic Online Trajectory Generation Acceleration Capabilities Considered for Real-Time Path Planning,” Diploma Thesis / Master Thesis PhD Thesis, 2013.
    [Bibtex]
    @phdthesis{katzschmann2013dynamic,
    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.},
    author = {Katzschmann, Robert K.},
    pages = {103},
    school = {Karlsruhe Institute of Technology / Stanford University},
    title = {{Dynamic Online Trajectory Generation Acceleration Capabilities Considered for Real-Time Path Planning}},
    type = {Diploma Thesis / Master Thesis},
    year = {2013}
    }