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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Abstract-This paper proposes a novel method of applying feedback control for legged robots, by directly modifying parameters of a robot's gait pattern. Gaits are a popular means of producing stable locomotion for legged robots, through the use of cyclic feedforward motion patterns, while requiring little to no sensory information. We are interested in incorporating feedback with these systems, and make use of salient parameters, found in gait patterns, to produce behaviors that span the space of possible gaits. These concepts are applied to a robotic hexapod, which, through the use of compliant microspines on its feet, is capable of climbing hard vertical textured surfaces, such as stucco. Experimental results are obtained comparing the use of a purely feedforward gait pattern to a behavior that actively modifies gait parameters while climbing, based upon sensory data.

As robot bodies become more capable, the motivation grows to better coordinate them-whether multiple limbs attached to a body or multiple bodies assigned to a task. This paper introduces a new formalism for coordination of periodic tasks, with specific application to gait transitions for legged platforms. Specifically, we make modest use of classical group theory to replace combinatorial search and optimization with a computationally simpler and conceptually more straightforward appeal to elementary algebra. We decompose the space of all periodic legged gaits into a cellular complex indexed using "Young Tableaux", making transparent the proximity to steady state orbits and the neighborhood structure. We encounter the simple task of transitioning between these gaits while locomoting over level ground. Toward that end, we arrange a family of dynamical reference generators over the "Gait Complex" and construct automated coordination controllers to force the legged system to converge to a specified cell's gait, while assessing the relative static stability of gaits by approximating their stability margin via transit through a "Stance Complex". To integrate these two different constructs-the Gait Complex describing possible gaits, the Stance Complex defining safe locomotion-we utilize our compositional lexicon to plan switching policies for a hybrid control approach. Results include automated gait transitions for a variety of useful gaits, shown via tests on a hexapedal robot. Abstract As robot bodies become more capable, the motivation grows to better coordinate them-whether multiple limbs attached to a body or multiple bodies as signed to a task. This paper introduces a new formalism for coordination of periodic tasks. with specific application to gaitlransitions for legged platforms. Specifically. we make modest use of classical group theory to replace combinatorial search and optimization with a computationally simpler and conceptually more straightforward appeal to elementary algebra.We decompose the space of all periodic legged gaits into a cellular complex in dexed using "Young Tableaux", making transparent the proximity to steady state orbits and the neighborhood structure. We encounter the simple task of transition ing between these gaits while locomoting over level ground. Toward that end. we arrange a family of dynamical reference generators over the "Gait Complex" and construct automated coordination controllers to force the legged system to converge to a specified cell's gait, while assessing the relative static stability of gaits by ap proximating their stability margin via transit through a "Stance Complex". To in tegrate these two different constructs-the Gait Complex describing possible gaits, the Stance Complex defining safe locomotion-we utilize our compositional lexicon to plan switching policies for a hybrid control approach. Results include automated gait transitions for a variety of useful gaits, shown via tests on a hexapedal robot.

This paper revisits the concept of specific resistance, a dimensionless measure of locomotive efficiency often used to compare the transport cost of vehicles (Gabrielli & von Karman 1950), and extends its use to the vertical domain. As specific resistance is designed for comparing horizontal locomotion, we introduce a compensation term in order to offset the gravitational potential gained or lost during locomotion. We observe that this modification requires an additional, experimentally fitted model estimating the efficiency at which a system is able to transfer energy to and from gravitational potential. This paper introduces a family of such models, thus introducing methods to allow fair comparisons of locomotion on level ground, sloped, and vertical surfaces, for any vehicle which necessarily gains or loses potential energy during travel. AbstractThis paper revisits the concept of specific resistance, ε, a dimensionless measure of locomotive efficiency often used to compare the transport cost of vehicles [6], and extends its use to the vertical domain. As specific resistance is designed for comparing horizontal locomotion, we introduce a compensation term in order to offset the gravitational potential gained or lost during locomotion. We observe that this modification requires an additional, experimentally fitted model estimating the efficiency at which a system is able to transfer energy to and from gravitational potential. This paper introduces a family of such models, thus introducing methods to allow fair comparisons of locomotion on level ground, sloped, and vertical surfaces, for any vehicle which necessarily gains or loses potential energy during travel.

We discuss the gait generation and control architecture of a bioinspired climbing robot that presently climbs a variety of vertical surfaces, including carpet, cork and a growing range of stucco-like surfaces in the quasi-static regime. The initial version of the robot utilizes a collection of gaits (cyclic feed-forward motion patterns) to locomote over these surfaces, with each gait tuned for a specific surface and set of operating conditions. The need for more flexibility in gait specification (e.g., adjusting number of feet on the ground), more intricate shaping of workspace motions (e.g., shaping the details of the foot attachment and detachment trajectories), and the need to encode gait "transitions" (e.g., tripod to pentapod gait structure) has led us to separate this trajectory generation scheme into the functional composition of a phase assigning transformation of the "clock space" (the six dimensional torus) followed by a map from phase into leg joints that decouples the geometric details of a particular gait. This decomposition also supports the introduction of sensory feedback to allow recovery from unexpected event and to adapt to changing surface geometries.

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