Rouse, N.; Daltorio, K. Stable Heteroclinic Channel-Based Movement Primitives: Tuning Trajectories Using Saddle Parameters. Appl. Sci.2024, 14, 2523.
Rouse, N.; Daltorio, K. Stable Heteroclinic Channel-Based Movement Primitives: Tuning Trajectories Using Saddle Parameters. Appl. Sci. 2024, 14, 2523.
Rouse, N.; Daltorio, K. Stable Heteroclinic Channel-Based Movement Primitives: Tuning Trajectories Using Saddle Parameters. Appl. Sci.2024, 14, 2523.
Rouse, N.; Daltorio, K. Stable Heteroclinic Channel-Based Movement Primitives: Tuning Trajectories Using Saddle Parameters. Appl. Sci. 2024, 14, 2523.
Abstract
Dynamic systems which underly controlled systems are expected to increase in complexity as robots, devices, and connected networks become more intelligent. While classical stable systems converge to a stable point (a sink), another type of stability is to consider a stable path rather than a single point. Such stable paths can be made of saddles which draw in trajectories from certain regions, and then push the trajectory toward the next saddle point. These are chains of saddles are called stable heteroclinic channels (SHCs), and can be used in robotic control to represent time sequences. While we have previously shown that each saddle is visualizable as a trajectory waypoint in phase space, how to increase the fidelity of the trajectory was unclear. In this paper, we hypothesized that the waypoints can be individually modified to locally vary fidelity. Specifically, we expected that increasing the saddle value (ratio of saddle eigenvalues) causes the trajectory to slow to more closely approach a particular saddle. Combined with other parameters that control speed and magnitude, a system expressed with an SHC can be modified locally, point by point, without disrupting the rest of the path, supporting their use in motion primitives. However, even more complex trajectory shape modifications are possible. While some combinations can enable a trajectory to better reach into corners, other combinations can rotate, distort and round the trajectory in the region of a corner. In our example, modifying select saddle values produced a 32% decrease in the trajectory error of a complex trajectory produced using this system. This is an effect not visible in previous 1D studies, that can lead to different learnable and tunable representations of dynamic systems.
Copyright:
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