Methods

Terrain representation and characterization for planning and learning

We identify three length scales in the traversal and characterization of rough terrain:

• Long scale (several to many body-lengths)

Long scale chacterization and planning involves distances of more than a few body-lengths ahead. At this range, the characterization of the terrain is relatively coarse. Many local geometric regions and features may be occluded and the physical properties (e.g. slipperiness) of surfaces may be unknown. At this scale path planning involves a large search space, which is mitigated by the possiblity of reducing the dimensionality so that only the position and perhaps a gross estimate of body orientation is needed.

Path planning at this scale with legged robots is not very different to path planning for wheeled vehicles on rough terrain and we can adapt existing methods. In the near term, we can avoid this issue altogether by allowing a human operator to establish the overall path.

• Intermediate scale (1-3 body-lengths)

At the intermediate scale there is more information available than at greater distance and the amount of information increases rapidly as the robot starts to contact parts of the surface. At this length scale one is less concerned with the details of motion control (e.g. actuator efforts, impedances, accelerations) than in the immediate case but one does need to consider the configuration of the robot - which includes not only the overall position and orientation but also the possible placements of the feet with respect to the body.

• Immediate scale (<=1 body length)

We are looking for stances that provide stability with respect to tip-over and slippage and that also provide freedom of movement to proceed to the next stance.

Platform and sensor development

The platform that we will use for terrain navigation is a quadrupedal robot, designed for traversing difficult terrain.

For initial experiments, and for subsequent comparison with the platform that we will construct, we will use the existing RiSE platform, reconfigured as a quadruped (see Figure [QuadraRiSE]). The RiSE platform in hexapedal form has already demonstrated impressive climbing capabilities on a variety of vertical surfaces [cite SPIE, ICRA06]. It is also capable of transitions between horizontal and vertical surfaces and of climbing irregular curved surfaces such as the trunks of trees. However, its climbing demonstrations have always taken place with a skilled human operator specifying the overall trajectory and gait selection. In addition, most of the climbing has taken place on surfaces such as stucco or brick buildings which are challenging, but quite structured. Indeed, the hexapedal version of RiSE probably does not have large enough configuration spaces for its feet to do well on rough and irregular terrain. Nonetheless, the RiSE platform is a capable machine for many experiments. It has a powerful on-board processor, three degrees of freedom of force sensing at each foot and an easily extensible programming and communications environment. It can also easily support the weight of a camera system and additional sensors. For our experiments we will convert the RiSE platform to a quadruped, which climbs less well on vertical surfaces but has more room for the feet to maneuver without interfering.

In parallel with conducting experiments on the RiSE platform we will develop a new quadruped, specialized for climbing over obstacles and negotiating rough terrain. This machine will utilize much of the technology behind the gecko-inspired Stickybot platform [cite Stickybot JEB]. Like Stickybot, and the earlier Sprawl robots [cite IJRR] (see Fig. Stickybot+Sprawl) it will be fabricated, using Shape Deposition Manufacturing [cite SDMref] out of heterogeneous multi-material components with embedded sensors and other discrete components such as bearings. Llike Stickybot it will utilize embedded carbon fiber fabric where high stiffness and strength are required and soft grades of urethane polymer where compliance and structural damping are desired; and like these earlier robots it will be robust with respect to accidental falls. We also expect to reuse the basic Stickybot gait controller, and cartesian stiffness control scheme for regulating external and internal "grasp" forces between pairs of feet.

However, in contrast to Stickybot, the new platform will not be specialized for climbing smooth vertical surfaces such as glass and will not employ adhesive feet with hyperextending toes. The new platform will have more ground clearance than Stickybot and the limbs will have a larger workspace in the vertical plane. The wireless communication will also be upgrade to allow higher bandwidth transmission of sensor data and commands between the robot and a host computer and the payload capability will be increased to allow a camera and additional sensors onboard. Unlike the much heavier RiSE platform, the Stickybot platform has modest processing power on board. Consequently, all learning, motion planning and vision interpretation would have to be performed off-board via a nearby host computer. It will be determined early in the project whether to continue this approach or whether a suitable low-power processor can be found that allows more computation on board.