Autonomous legged hill and stairwell ascent

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. 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.

A drift-diffusion model for robotic obstacle avoidance

“…An alternative approach introduced by Borenstein and Koren [3] stems from some limitations of the previous method and focuses on moving to empty regions rather than being repelled by obstacle regions. A previous implementation on the RHex platform [22] utilizes a similar approach. Our assumptions regarding obstacle shape and distribution let us disregard the limitations described by Borenstein and Koren and implement a simple local repelling field around obstacles where, with proper choice of control parameters, any spurious fixed points introduced to the force field are guaranteed to be unstable [20].…”

Ascending stairway modeling from dense depth imagery for traversability analysis

“…Several existing methods [3]- [5] perform stairway detection but immediately initialize a traversal procedure with their respective platforms, which is not necessarily desirable in an exploration scenario. These works assume that the robot is located near the stairway, but not aligned with it.…”

“…The aforementioned climbing methods use edge detection from camera imagery [5], and range discontinuity detection from horizontal ladar [3] or vertical ladar [4] data, to detect stairs. Other stair edge detection systems have been proposed in the context of controller feedback for stair traversal [12], [13] and object detection from monocular [14] or stereo imagery [11].…”

Study of efficiency of USAR operations with assistive technologies

“…Conventional track or wheel driven single-body robots either have the required mobility but are too big to turn in narrow spaces or are small enough but fail at successfully tackling obstacles. The single-body robots such as Quince or RHex are a good compromise between mobility and terrainability [19,20], but will never excel in all kind of terrains.…”