The Biologically Inspired Robotics group at Case Western Reserve University has developed several innovative designs for a new kind of robot that uses peristalsis, the method of locomotion used by earthworms. Unlike previous wormlike robots, our concept uses a continuously deformable outer mesh that interpolates the body position between discrete actuators. Here, we summarize our progress with this soft hyper-redundant robot.

While soft-bodied animals have an extraordinarily diverse set of robust behaviors, soft-bodied robots have not yet achieved this flexiblity. In this paper, we explore controlling a truly continuously deformable structure with a CPG-like network. Our recently completed soft wormlike robot with a continuously deformable outer mesh, along with a continuum analysis of peristalsis, has suggested the neural control investigated here. We use a Wilson-Cowan neuronal model in a continuum arrangement that mirrors the arrangement of muscles in an earthworm. We show that such a system is well suited to incorporate sensory input and can create both rhythmic and nonrhythmic activity. The system can be controlled using straightforward descending signals whose effects are largely decoupled and can modulate the properties from CPG-like behaviors to static waves. This approach will be useful for designing robotic systems that express multiple adaptive behavioral modes.

We have developed several innovative designs for a new kind of robot that uses peristalsis for locomotion, the same method that earthworms use, and report on the first completed prototype (Fig. 1). This form of locomotion is particularly effective in constrained spaces, and although the motion has been understood for some time, it has rarely been effectively or accurately implemented in a robotic platform. We address some reasons for this, including some common misconceptions within the field. We present a technique using a braided mesh exterior to produce fluid waves of motion along the body of a worm-like robot. We also present a new analytical model of this motion and compare predicted robot velocity to a 2-D simulation. Unlike previous mathematical models of peristaltic motion, our model suggests that friction is not a limiting factor in robot speed, but only in acceleration. The concept is highly scalable, and we present methods of construction at two different scales.

When a camera is affixed on a dynamic mobile robot, image stabilization is the first step towards more complex analysis on the video feed. This paper presents a novel electronic image stabilization (EIS) algorithm for highly dynamic mobile robotic platforms. The algorithm combines optical flow motion parameter estimation with angular rate data provided by a strapdown inertial measurement unit (IMU). A discrete Kalman filter in feedforward configuration is used for optimal fusion of the two data sources. Performance evaluations are conducted using a simulated video truth model (capturing the effects of image translation, rotation, blurring, and moving objects), and live test data. Live data was collected from a camera and IMU affixed to the DAGSI Whegs mobile robotic platform as it navigated through a hallway. Template matching, feature detection, optical flow, and inertial measurement techniques are compared and analyzed to determine the most suitable algorithm for this specific type of image stabilization. Pyramidal Lucas-Kanade optical flow using Shi-Tomasi good features in combination with inertial measurement is the EIS algorithm found to be superior. In the presence of moving objects, fusion of inertial measurement reduces optical flow root-mean-squared (RMS) error in motion parameter estimates by 40%.

DAGSI Whegs is the latest generation of full size Whegs robots. The robot is designed for collaborative work with the Air Force Institute of Technology (AFIT) in SLAM with active feature recognition. Whegs vehicles use abstracted biological principles to navigate over irregular and varied terrain with little or no low level control. Torsionally compliant devices in the drive train of each wheel-leg allow its gait to passively adapt when climbing large obstacles or steep inclines. Whegs is similar to the RHex line of robots that preceded Whegs in that the foot motion of all 6 legs is circular, but it differs in many aspects: 3 leg-spokes versus 1, 1 drive motor vs. 6, leg rotation for steering instead of skid steering, passive gait adaptation vs. active gait control, and Whegs has a body joint.

Insects and geckos use claws and adhesive pads to negotiate both rough and smooth surfaces. Climbing robots have been designed to mimic various aspects of these and other biological systems to operate in specific vertical environments. Robots that adhere to the surface through suction cups, magnetic end-effectors, or adhesive pads can climb featureless, flat, or smoothly curved surfaces. Vortex-generating climbers do not require smooth surfaces. Robots have been designed with end-effectors that match specific features of the environment, such as peg-holes, handrails, climbing-wall footholds, and poles. Robots have also been fitted with insect-inspired spines to scale rough vertical surfaces.

Current autonomous and semi-autonomous robotic platforms are limited to functioning in highly structured environments such as buildings and roads. Autonomous robots that could explore and navigate rugged terrain and highly unstructured environments such as collapsed buildings would have large dividends in civilian and military applications. In this work, we present the next generation of Whegstrade robots, DAGSI Whegstrade, which has been completed and extensively field tested. Several innovations have made the robot more rugged and well suited to autonomous operation. Specifically, an actively controlled, passively compliant body joint has been tested in three different modes of operation to judge the usefulness of the mechanism. A two-dimensional dynamic simulation of the robot has also been constructed, and has been used to study the effects of weight distribution on obstacle climbing and to investigate future autonomous climbing strategies. Moving the center of mass forward allowed the robot to climb taller obstacles. DAGSI Whegstrade can climb rectangular obstacles as tall as 2.19 times the length of a leg.