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For mobile robots, the essential units of actuation, computation, and sensing must be designed to fit within the body of the robot. Additional capabilities will largely depend upon a given activity, and should be easily reconfigurable to maximize the diversity of applications and experiments. To address this issue, we introduce a modular architecture originally developed and tested in the design and implementation of the X-RHex hexapod that allows the robot to operate as a mobile laboratory on legs. In the present paper we will introduce the specification, design and very earliest operational data of Canid, an actively driven compliant-spined quadruped whose completely different morphology and intended dynamical operating point are nevertheless built around exactly the same “Lab on Legs” actuation, computation, and sensing infrastructure. We will review as well, more briefly a second RHex variation, the XRL latform, built using the same components.

For more information: Kod*Lab

This paper describes the inspiration, design, analysis, implementation of and experimentation with the first dynamical vertical climbing robot. Biologists have proposed a pendulous climbing model that abstracts remarkable similarities in dynamic wall scaling behavior exhibited by radically different animal species. We study numerically a version of that pendulous climbing template dynamically re-scaled for applicability to utilitarian payloads with conventional electronics and actuation. This simulation study reveals that the incorporation of passive compliance can compensate for an artifact’s poorer power density and scale disadvantages relative to biology. However the introduction of additional dynamical elements raises new concerns about stability regarding both the power stroke and limb coordination that we allay via mathematical analysis of further simplified models. Combining these numerical and analytical insights into a series of design prototypes, we document the correspondence of the various models to the variously scaled platforms and report that our approximately two kilogram platform climbs dynamically at vertical speeds up to 1.5 bodylengths per second. In particular, the final 2.6 kg final prototype climbs at an average steady state speed of 0.66 m/s against gravity on a carpeted vertical wall, in rough agreement with our various models’ predictions.

This report presents the apex state achieved after performing a variety of jumps with the XRL robot. A full account of the behaviors and the theoretical basis is given in another paper, this document is intended to simply provide higher resolution copies of those figures, and present the results in numerical form.

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.

We develop robust methods that allow specification, control, and transition of a multi-legged robot’s stepping pattern—its gait—during active locomotion over natural terrain. Resulting gaits emerge through the introduction of controllers that impose appropriately-placed repellors within the space of gaits, the torus of relative leg phases, thereby mitigating against dangerous patterns of leg timing. Moreover, these repellors are organized with respect to a natural cellular decomposition of gait space and result in limit cycles with associated basins that are well characterized by these cells, thus conferring a symbolic character upon the overall behavioral repertoire. These ideas are particularly applicable to four- and six-legged robots, for which a large variety of interesting and useful (and, in many cases, familiar) gaits exist, and whose tradeoffs between speed and reliability motivate the desire for transitioning between them during active locomotion. We provide an empirical instance of this gait regulation scheme by application to a climbing hexapod, whose “physical layer” sensor-feedback control requires adequate grasp of a climbing surface but whose closed loop control perturbs the robot from its desired gait. We document how the regulation scheme secures the desired gait and permits operator selection of different gaits as required during active climbing on challenging surfaces.

Biomechanical studies suggest that animals’ abilities to tune their effective leg compliance in response to changing terrain conditions plays an important role in their agile, robust locomotion. However, despite growing interest in leg compliance within the robotics literature, little experimental work has been reported on tunable passive leg compliance in running machines. In this paper we present an empirical study into the role of leg compliance using a composite tunable leg design implemented on our dynamic hexapod, EduBot, with gaits optimized for running speed using a range of leg stiffnesses, on two different surface stiffnesses, and with two different payload configurations (0 kg and 0.91 kg). We found that leg stiffness, surface compliance, and payload had a significant impact on the robot’s final optimized speed and efficiency. These results document the value and efficacy of what we believe is the first autonomous dynamic legged robot capable of runtime leg stiffness adjustment.

For more information: Kod*Lab