The RiSE robot is a biologically inspired, six legged climbing robot, designed for general mobility in scansorial (vertical walls, horizontal ledges, ground level) environments. It exhibits ground reaction forces that are similar to animal climbers and does not rely on suction, magnets or other surface-dependent specializations to achieve adhesion and shear force. We describe RiSE's body and leg design as well as its electromechanical, communications and computational infrastructure. We review design iterations that enable RiSE to climb 90° carpeted, cork covered and (a growing range of) stucco surfaces in the quasi-static regime.
BigDog's goal is to be the world's most advanced quadruped robot for outdoor applications. BigDog is aimed at the mission of a mechanical mule - a category with few competitors to date: power autonomous quadrupeds capable of carrying significant payloads, operating outdoors, with static and dynamic mobility, and fully integrated sensing. BigDog is about 1 m tall, 1 m long and 0.3 m wide, and weighs about 90 kg. BigDog has demonstrated walking and trotting gaits, as well as standing up and sitting down. Since its creation in the fall of 2004, BigDog has logged tens of hours of walking, climbing and running time. It has walked up and down 25 & 35 degree inclines and trotted at speeds up to 1.8 m/s. BigDog has walked at 0.7 m/s over loose rock beds and carried over 50 kg of payload. We are currently working to expand BigDog's rough terrain mobility through the creation of robust locomotion strategies and terrain sensing capabilities.
We review a large multidisciplinary effort to develop a family of autonomous robots capable of rapid, agile maneuvers in and around natural and artificial vertical terrains such as walls, cliffs, caves, trees and rubble. Our robot designs are inspired by (but not direct copies of) biological climbers such as cockroaches, geckos, and squirrels. We are incorporating advanced materials (e.g., synthetic gecko hairs) into these designs and fabricating them using state of the art rapid prototyping techniques (e.g., shape deposition manufacturing) that permit multiple iterations of design and testing with an effective integration path for the novel materials and components. We are developing novel motion control techniques to support dexterous climbing behaviors that are inspired by neuroethological studies of animals and descended from earlier frameworks that have proven analytically tractable and empirically sound. Our near term behavioral targets call for vertical climbing on soft (e.g., bark) or rough surfaces and for ascents on smooth, hard steep inclines (e.g., 60 degree slopes on metal or glass sheets) at one body length per second.
Casual daily observation provides convincing evidence that animals offer a wealth of inspiration for legged machines. However the lessons of animal motor science are largely written in the grammar of materials properties, and their meaning hidden by the complex interaction of multiply layered functional hierarchies. This paper will review some of the lessons of biological running that we have been able to articulate and begin to prescribe rigorously as manifest in the hexapod robot RHex. Although there is a long way to go before our mathematical analysis catches up with the full range of behaviors this remarkable machine exhibits, we are nevertheless able to make increasingly precise statements about certain control principles and the role they may play in RHex's performance. This ongoing research effort serves as a test case to underscore the huge and still largely untapped potential for mining bioinspiration in legged locomotion systems.
A hybrid platform for an Unmanned Ground Vehicle (UGV), one with legs and wheels, was initially considered to yield a design that possessed a high degree of intrinsic mobility. Integrating a high level of mobility reduces the UGV's perception and computational requirements for successful semi-autonomous or autonomous terrain negotiation. An investigation into the dynamic capabilities of the hybrid design revealed a large amount of otherwise impossible behaviors. The widened scope of maneuvers enabled the simulated robot to negotiate higher obstacles, clear larger ditches and generally improved its rough terrain mobility. A scalability study was also undertaken to predict dynamic potential of various platform sizes and to aid in the selection of design specifications such as motor torque-speed curves. The hybrid design of the platform (legs with active wheels) proved invaluable in achieving these dynamic behaviors and revealed that the leg-wheel design was as fundamental to dynamic capabilities, as it was to intrinsic mobility.
The ability of an Unmanned Ground Vehicle (UGV) to successfully move about in its environment is enabled by the synergistic combination of perception, control and platform (mobility and utility). Vast effort is being expended on the former technologies but little demonstrable evidence has been produced to indicate that the latter (mobility/utility) has been considered as an integral part of the UGV systems level capability; a concept commonly referred to as intrinsic mobility. While past work described the rationale for hybrid locomotion, this paper aims to demonstrate that integrating intrinsic mobility into a UGV systems mobility element or 'vehicle' will be a key contributor to the magnitude of autonomy that the system can achieve. This paper serves to provide compelling evidence that 1) intrinsic mobility improvements provided by hybrid locomotion configurations offer the best generic mobility, that 2) strict attention must be placed on the optimization of both utility (inherent vehicle capabilities) and mobility and that 3) the establishment of measures of performance for unmanned vehicle mobility is an unmet and latent need.
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