A mode-based sensor fusion approach to robotic stair-climbing

Steplight, S.; Egnal, Geoffrey; Jung, Soonmook; Walker, Douglas; Ce, Taylor; Ostrowski, James

doi:10.1109/iros.2000.893168

Cited by 21 publications

(24 citation statements)

References 6 publications

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“…Prior work on general autonomous stairwell negotiation also has been largely focused on simulation studies [11], with almost all empirical work confined to the traversal of a single flight and yaw control on the stairs (summarized in [4]). The only prior report we have found documenting empirical work over multiple flights of stairs assumed a very specific, simple landing geometry [12]; we intentionally target a great diversity.…”

Section: B Contributionsmentioning

confidence: 99%

“…Given the orientation of a robot's body from an IMU (in terms of a coordinate system x, y, and z), the calculation of the instantaneous uphill direction is similar to computations proposed in the prior stairwell experiments [12]. We compute the rotation α about z between x and Dη, given the direction of gravity, g, as follows…”

Section: ) Gravitational Gradient Sensormentioning

confidence: 99%

“…Thus we need one controller that can direct the robot to that goal set from say just past the top step of the final flight. This stair exit controller is simply a few walking steps triggered by the robot body pitch (as done for a different robot in [12]). …”

Section: B Autonomous Stairwell Ascentmentioning

confidence: 99%

“…To test the autonomous stairwell climbing behavior we ran it on 10 of the many different stairwells in 4 nearby buildings 12 , as Table II summarizes 13 . We distinguish behavior faults (arising from inadequacies in either the algorithm or the sensorimotor capabilities that subserve it) from robot faults (failures due to mechanical or electronic unreliability).…”

Section: B Autonomous Stairwell Ascentmentioning

confidence: 99%

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Autonomous legged hill and stairwell ascent

Johnson

Hale

Haynes

et al. 2011

2011 IEEE International Symposium on Safety, Security, and Rescue Robotics

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This paper documents near-autonomous negotiation of synthetic and natural climbing terrain by a rugged legged robot, achieved through sequential composition of appropriate perceptually triggered locomotion primitives. The first, simple composition achieves autonomous uphill climbs in unstructured outdoor terrain while avoiding surrounding obstacles such as trees and bushes. The second, slightly more complex composition achieves autonomous stairwell climbing in a variety of different buildings. In both cases, the intrinsic motor competence of the legged platform requires only small amounts of sensory information to yield near-complete autonomy. Both of these behaviors were developed using X-RHex, a new revision of RHex that is a laboratory on legs, allowing a style of rapid development of sensorimotor tasks with a convenience near to that of conducting experiments on a lab bench. Applications of this work include urban search and rescue as well as reconnaissance operations in which robust yet simple-to-implement autonomy allows a robot access to difficult environments with little burden to a human operator. Abstract -This paper documents near-autonomous negotiation of synthetic and natural climbing terrain by a rugged legged robot, achieved through sequential composition of appropriate perceptually triggered locomotion primitives. The first, simple composition achieves autonomous uphill climbs in unstructured outdoor terrain while avoiding surrounding obstacles such as trees and bushes. The second, slightly more complex composition achieves autonomous stairwell climbing in a variety of different buildings. In both cases, the intrinsic motor competence of the legged platform requires only small amounts of sensory information to yield near-complete autonomy. Both of these behaviors were developed using X-RHex, a new revision of RHex that is a laboratory on legs, allowing a style of rapid development of sensorimotor tasks with a convenience near to that of conducting experiments on a lab bench. Applications of this work include urban search and rescue as well as reconnaissance operations in which robust yet simple-to-implement autonomy allows a robot access to difficult environments with little burden to a human operator.

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Section: B Contributionsmentioning

confidence: 99%

Section: ) Gravitational Gradient Sensormentioning

confidence: 99%

Section: B Autonomous Stairwell Ascentmentioning

confidence: 99%

Section: B Autonomous Stairwell Ascentmentioning

confidence: 99%

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Autonomous legged hill and stairwell ascent

Johnson

Hale

Haynes

et al. 2011

2011 IEEE International Symposium on Safety, Security, and Rescue Robotics

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“…Although the authors model the tread depth and riser height, the robot's attitude is not estimated, therefore no heading corrections can be computed during the ascent. In [8], a tracked robot is equipped with a suite of sensors (i.e., sonar, monocular camera, and two-axis accelerometer) to estimate its orientation while on the stairs. However, their approach does not fuse all available sensor measurements, but instead uses heuristics to select the most accurate sensor.…”

Section: B Stair Traversalmentioning

confidence: 99%

Descending-stair detection, approach, and traversal with an autonomous tracked vehicle

Hesch

Mariottini

Roumeliotis

2010

2010 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-This paper presents a strategy for descending-stair detection, approach, and traversal using inertial sensing and a monocular camera mounted on an autonomous tracked vehicle. At the core of our algorithm are vision modules that exploit texture energy, optical flow, and scene geometry (lines) in order to robustly detect descending stairwells during both far-and near-approaches. As the robot navigates down the stairs, it estimates its three-degrees-of-freedom (d.o.f.) attitude by fusing rotational velocity measurements from an on-board tri-axial gyroscope with line observations of the stair edges detected by its camera. We employ a centering controller, derived based on a linearized dynamical model of our system, in order to steer the robot along safe trajectories. A real-time implementation of the described algorithm was developed for an iRobot Packbot, and results from real-world experiments are presented.

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Adaptive Robust Three-dimensional Trajectory Tracking for Actively Articulated Tracked Vehicles*

et al. 2015

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A new approach is proposed for an adaptive robust three-dimensional (3D) trajectory-tracking controller design. The controller is modeled for actively articulated tracked vehicles (AATVs). These vehicles have active subtracks, called flippers, linked to the ends of the main tracks, to extend the locomotion capabilities in hazardous environments, such as rescue scenarios. The proposed controller adapts the flippers configuration and simultaneously generates the track velocities, to allow the vehicle to autonomously follow a given feasible 3D path. The approach develops both a direct and differential kinematic model of the AATV for traversal task execution correlating the robot body motion to the flippers motion. The benefit of this approach is to allow the controller to flexibly manage all the degrees of freedom of the AATV as well as the steering. The differential kinematic model integrates a differential drive robot model, compensating the slippage between the vehicle tracks and the traversed terrain. The underlying feedback control law dynamically accounts for the kinematic singularities of the mechanical vehicle structure. The designed controller integrates a strategy selector too, which has the role of locally modifying the rail path of the flipper end points. This serves to reduce both the effort of the flipper servo motors and the traction force on the robot body, recognizing when the robot is moving on a horizontal plane surface. Several experiments have been performed, in both virtual and real scenarios, to validate the designed trajectory-tracking controller, while the AATV negotiates rubble, stairs, and complex terrain surfaces. Results are compared with both the performance of an alternative control strategy and the ability of skilled human operators, manually controlling the actively articulated components of the robot. C 2015 Wiley Periodicals, Inc.

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A mode-based sensor fusion approach to robotic stair-climbing

Cited by 21 publications

References 6 publications

Autonomous legged hill and stairwell ascent

Autonomous legged hill and stairwell ascent

Descending-stair detection, approach, and traversal with an autonomous tracked vehicle

Adaptive Robust Three-dimensional Trajectory Tracking for Actively Articulated Tracked Vehicles*

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