Region-of-interest analyses of one-dimensional biomechanical trajectories: bridging 0D and 1D theory, augmenting statistical power.

One-dimensional (1D) kinematic, force, and EMG trajectories are often analyzed using zero-dimensional (0D) metrics like local extrema. Recently whole-trajectory 1D methods have emerged in the literature as alternatives. Since 0D and 1D methods can yield qualitatively different results, the two approaches may appear to be theoretically distinct. The purposes of this paper were (a) to clarify that 0D and 1D approaches are actually just special cases of a more general region-of-interest (ROI) analysis framework, and (b) to demonstrate how ROIs can augment statistical power. We first simulated millions of smooth, random 1D datasets to validate theoretical predictions of the 0D, 1D and ROI approaches and to emphasize how ROIs provide a continuous bridge between 0D and 1D results. We then analyzed a variety of public datasets to demonstrate potential effects of ROIs on biomechanical conclusions. Results showed, first, that a priori ROI particulars can qualitatively affect the biomechanical conclusions that emerge from analyses and, second, that ROIs derived from exploratory/pilot analyses can detect smaller biomechanical effects than are detectable using full 1D methods. We recommend regarding ROIs, like data filtering particulars and Type I error rate, as parameters which can affect hypothesis testing results, and thus as sensitivity analysis tools to ensure arbitrary decisions do not influence scientific interpretations. Last, we describe open-source Python and MATLAB implementations of 1D ROI analysis for arbitrary experimental designs ranging from one-sample t tests to MANOVA

Building DryVR: A verification and controller synthesis engine for cyber-physical systems and safety-critical autonomous vehicle features

To test safety of autonomous vehicles, large corporations have raced to log millions of miles of test driving on public roads. While this can improve confidence in such systems, testing alone cannot establish of absence of failure scenarios. In fact, it has also been reported that the amount of data required to guarantee a probability of 10^-9 fatality per hour of driving would require 10^9 hours of driving [1] [2], which is roughly in the order of thirty billion miles. Formal verification can give guarantees about absence of failures and potentially reduce the amount of testing needed significantly. Simulation based verification is a promising approach to provide formal safety guarantees to Cyber-Physical Systems (CPS). However, existing verification tools rely on the explicit mathematical models of the system. Detailed mathematical models are often not available or are too complex for formal verification tools. To address this issue, the DryVR approach for verification is presented in [3]. DryVR views a cyber-physical system as a combination of a white-box transition graph and a black-box simulator. This alleviates the need for complete mathematical models, but at the same time exploits models when they are available. A verification algorithm for directed acyclic time-dependent transition graph is also presented in [3]. In this thesis, we present the detailed construction of the DryVR tool with several new functionalities, which includes: (a) verification on state-dependent cyclic transition graph with guard and reset functions; (b) controller synthesis that searches transition graph for given reach-avoid specification; (c) interface that allows user to connect DryVR with arbitrary black-box simulators, and (d) integration with Jupyter Notebook [4]. We also present a case study for autonomous vehicle system in this thesis, and DryVR comes with verification and controller synthesis examples to illustrate its capabilities. The evaluation of included examples is presented in later chapter shows that both verification and controller synthesis are promising starting point for DryVR to become a comprehensive verification and synthesis toolbox for practical CPS

SDSS-V Algorithms: Fast, Collision-Free Trajectory Planning for Heavily Overlapping Robotic Fiber Positioners

Robotic fiber positioner (RFP) arrays are becoming heavily adopted in wide field massively multiplexed spectroscopic survey instruments. RFP arrays decrease nightly operational overheads through rapid reconfiguration between fields and exposures. In comparison to similar instruments, SDSS-V has selected a very dense RFP packing scheme where any point in a field is typically accessible to three or more robots. This design provides flexibility in target assignment. However, the task of collision-less trajectory planning is especially challenging. We present two multi-agent distributed control strategies that are highly efficient and computationally inexpensive for determining collision-free paths for RFPs in heavily overlapping workspaces. We demonstrate that a reconfiguration path between two arbitrary robot configurations can be efficiently found if "folded" state, in which all robot arms are retracted and aligned in a lattice-like orientation, is inserted between the initial and final states. Although developed for SDSS-V, the approach we describe is generic and so applicable to a wide range of RFP designs and layouts. Robotic fiber positioner technology continues to advance rapidly, and in the near future ultra-densely packed RFP designs may be feasible. Our algorithms are especially capable in routing paths in very crowded environments, where we see efficient results even in regimes significantly more crowded than the SDSS-V RFP design.Comment: To be published in the Astronomical Journa