Proprioception based behavioral advances in a hexapod robot

Abstract

We report on our progress in extending the behavioral repertoire of RHex, a compliant leg hexapod robot. We introduce two new controllers, one for climbing constant slope inclinations and one for achieving higher speeds via pronking, a gait that incorporates a, substantial aerial phase. In both cases, we make use of an underlying open-loop control strategy, combined with low bandwidth feedback to modulate its parameters. The inclination behavior arises from our initial alternating tripod walking controller and adjusts the angle offsets of individual leg motion profiles based on inertial sensing of the average surface slope. Similarly, the pronking controller makes use of a "virtual" leg touchdown sensing mechanism to adjust the frequency of the open-loop pronking, effectively synchronizing the controller with the natural oscillations of the mechanical system. Experimental results demonstrate good performance on slopes inclined up to /spl sim/250 and pronking up to speeds approaching 2 body lengths per second (/spl sim/1.0 m/s). KeywordsHexapod robot, legged locomotion, clock driven system, feedforward control, biomechanics, climbing, pronking 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. NOTE: At the time of publication, author Daniel Koditschek was affiliated with the University of Michigan. Currently, he is a faculty member in the Department of Electrical and Systems Engineering at the University of Pennsylvania. Author(s)Haldun AbstractWe report on our progress in extending the behavioral repertoire of RHex, a compliant leg hexapod robot. We introduce two new controllers, one for climbing constant slope inclinations and one for achieving higher speeds via pronking, a gait that incorporates a substantial aerial phase. In both cases, we make use of an underlying openloop control strategy, combined with low bandwidth feedback to modulate its parameters. The inclination behavior arises from our initial alternating tripod walking controller and adjusts the angle offsets of individual leg motion profiles based on inertial sensing of the average surface slope. Similarly, the pronking controller makes use of a "virtual" leg touchdown sensing mechanism to adjust the frequency of the open-loop pronking, effectively synchronizing the controller with the natural oscillations of the mechanical system. Experimental results demonstrate good performance on slopes inclined up to ∼25 o and pronking up to speeds approaching 2 body lengths per second (∼1.0m/s).

Autonomous legged hill and stairwell ascent

“…Importantly, we have set these parameters once and for all, and they remain invariant throughout all the different stairwells of Table II. 6 The leg offset parameter of the gait is also automatically modified as the robot runs to match the grade as in [23]. 7 Here and elsewhere in this paper s is filtered to smooth out noise and prevent a large instantaneous change in steering command, which also gives these controllers some effective (and pragmatically welcome) hysteresis.…”

Bionic Limb Mechanism and Multi-Sensing Control for Cockroach Robots

“…The improvement of RHex 0.1 is mainly the mechanism, resulting in a 10% weight reduction. Rhex 0.2 incorporates tactile sensors to detect surrounding environment (Moore et al, 2002;Komsuglu et al, 2001). In recent years, controller with gait adaptive algorithm further enhances function of RHex (Weingarten, 2004).…”

Sensor Data Fusion for Body State Estimation in a Hexapod Robot with Dynamical Gaits

“…Given a discrete time-invariant plant with measurement , where the process noise and measurement noise are white with zero means and covariance defined by and , an EKF incorporates two steps: a time update (a priori estimate) with and (5) and a measurement update (a posteriori estimate) with (6) where is the error covariance matrix, is the so-called Kalman gain, and is the sensor measurement (partial state measured directly from sensors of the kind discussed in Section III, and to be detailed in the succeeding two subsections). Upon initializing the value of state and error covariance matrix , the EKF continuously delivers the "optimal" state estimates by consecutively performing these two updates at each time stamp.…”

“…Moreover, with the contribution of additional angular acceleration data, 5 fusion system further improves the performance of velocity estimates. 6 …”

“…Using an open loop feedforward control strategy, the machine runs at speeds exceeding five body lengths per second on even terrain [3], and negotiates badly broken and unstable surfaces, as well as stairs [4], [5]. Initial empirical studies of controllers relying on cheap and inaccurate sensory feedback cues have resulted in significantly improved performance (inclinometers on slopes [6]; leg touchdown cues over broken terrain [7]) and entirely new behaviors (body-pitch-sensitive accelerometers for flips [8] and bipedal gaits [9]; leg touchdown cues for pronking gaits [10]). Theoretical considerations and simulation evidence [11] suggest that the availability of accurate, full body state estimates, as well as force interactions with the surrounding environment throughout the stance and aerial phases of locomotion, should confer considerably greater agility still.…”