Past Research

IROS 2013: Towards Online Trajectory Generation Considering Robot Dynamics and Torque Limits

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. 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. Several real-world experimental results using a seven-degree-of-freedom lightweight robot arm underline the relevance of this extension.

Master thesis research: Dynamic Online Trajectory Generation – Acceleration Capabilities Considered for Real-Time Path Planning

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. Real-world experimental results using a lightweight robot arm highlight the practical relevance of this extension.