This paper reports on autonomous ascent by a legged robotic platform in outdoor forested terrain. Two controllers govern the integration of online Inertial Measurement Unit (IMU) and Light Detection And Ranging (LIDAR) sensor signals into commands for climbing by means of an abstracted (unicycle) representation of the platform in support of different performance goals: a kinematic version for endurance and a dynamic version for speed. These control laws, backed by a suite of formal correctness guarantees, encourage a stripped down sensory suite supporting a simplified world model whose departures from the actual physical environment are handled by the mechanical competence of the legged platform. Both behaviors are implemented on a version of the legged RHex platform, and experiments spanning almost a kilometer (thousands of body lengths) in various challenging settings are conducted.

We propose a dynamical reference generator equipped with an augmented transient "replanning" subsystem that modulates a feedback controller's efforts to force a mechanical plant to track the reference signal. The replanner alters the reference generator's output in the face of unanticipated disturbances that drive up the tracking error. We demonstrate that the new reference generator cannot destabilize the tracker, that tracking errors converge in the absence of disturbance, and that the overall coupled reference-tracker system cannot be destabilized by disturbances of bounded energy. We report the results of simulation studies exploring the performance of this new design applied to a two dimensional point mass particle interacting with fixed but unknown terrain obstacles. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Pennsylvania's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.This conference paper is available at ScholarlyCommons: http://repository.upenn.edu/ese_papers/644 Dynamical Trajectory Replanning for Uncertain EnvironmentsShai Revzen* B. Denizİlhan* Daniel E. Koditschek* Abstract-We propose a dynamical reference generator equipped with an augmented transient "replanning" subsystem that modulates a feedback controller's efforts to force a mechanical plant to track the reference signal. The replanner alters the reference generator's output in the face of unanticipated disturbances that drive up the tracking error. We demonstrate that the new reference generator cannot destabilize the tracker, that tracking errors converge in the absence of disturbance, and that the overall coupled reference-tracker system cannot be destabilized by disturbances of bounded energy. We report the results of simulation studies exploring the performance of this new design applied to a two dimensional point mass particle interacting with fixed but unknown terrain obstacles.

We develop a stochastic framework for modeling and analysis of robot navigation in the presence of obstacles. We show that, with appropriate assumptions, the probability of a robot avoiding a given obstacle can be reduced to a function of a single dimensionless parameter which captures all relevant quantities of the problem. This parameter is analogous to the Peclet number considered in the literature on mass transport in advection-diffusion fluid flows. Using the framework we also compute statistics of the time required to escape an obstacle in an informative case. The results of the computation show that adding noise to the navigation strategy can improve performance. Finally, we present experimental results that illustrate these performance improvements on a robotic platform. Abstract-We develop a stochastic framework for modeling and analysis of robot navigation in the presence of obstacles. We show that, with appropriate assumptions, the probability of a robot avoiding a given obstacle can be reduced to a function of a single dimensionless parameter which captures all relevant quantities of the problem. This parameter is analogous to the Péclet number considered in the literature on mass transport in advection-diffusion fluid flows. Using the framework we also compute statistics of the time required to escape an obstacle in an informative case. The results of the computation show that adding noise to the navigation strategy can improve performance. Finally, we present experimental results that illustrate these performance improvements on a robotic platform.

SummaryThis paper documents autonomous multi-floor stairwell ascent by a legged robot. This is made possible through empirically deployed sequential composition of several reactive controllers, with perceptually triggered transitions. This composition relies on simplified assumptions regarding the robot’s sensory capabilities, its level of mobility, and the environment it operates in. The discrepancies between these assumptions and the physical reality are capably handled by the intrinsic motor competence of the robot. This behavior is implemented on the legged RHex platform and experiments spanning 10 different stairwells with various challenges are conducted.

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