Barrett Heyneman

Smooth Vertical Surface Climbing With Directional Adhesion

Stickybot is a bioinspired robot that climbs smooth vertical surfaces such as glass, plastic, and ceramic tile at 4 cm/s. The robot employs several design principles adapted from the gecko including a hierarchy of compliant structures, directional adhesion, and control of tangential contact forces to achieve control of adhesion. We describe the design and fabrication methods used to create underactuated, multimaterial structures that conform to surfaces over a range of length scales from centimeters to micrometers. At the finest scale, the undersides of Stickybots toes are covered with arrays of small, angled polymer stalks. Like the directional adhesive structures used by geckos, they readily adhere when pulled tangentially from the tips of the toes toward the ankles; when pulled in the opposite direction, they release. Working in combination with the compliant structures and directional adhesion is a force control strategy that balances forces among the feet and promotes smooth attachment and detachment of the toes.

Whole body adhesion: hierarchical, directional and distributed control of adhesive forces for a climbing robot

We describe the design and control of a new bio-inspired climbing robot designed to scale smooth vertical surfaces using directional adhesive materials. The robot, called Stickybot, draws its inspiration from geckos and other climbing lizards and employs similar compliance and force control strategies to climb smooth vertical surfaces including glass, tile and plastic panels. Foremost among the design features are multiple levels of compliance, at length scales ranging from centimeters to micrometers, to allow the robot to conform to surfaces and maintain large real areas of contact so that adhesive forces can support it. Structures within the feet ensure even stress distributions over each toe and facilitate engagement and disengagement of the adhesive materials. A force control strategy works in conjunction with the directional adhesive materials to obtain sufficient levels of friction and adhesion for climbing with low attachment and detachment forces.

Climbing rough vertical surfaces with hierarchical directional adhesion

Prior research in biology and mechanics has shown the importance of hierarchy to the performance of dry adhesive systems on rough surfaces. The gecko utilizes several levels of hierarchy that operate on length scales from millimeters to 100s of nanometers in order to maneuver on smooth and rough vertical surfaces ranging from glass to rock. The geckos hierarchical system serves two main purposes: it permits conformation to the surface for a large effective area of contact, and it distributes the load evenly among contacting elements. We present a new two-tiered directional adhesive system that provides these capabilities for a gecko-inspired climbing robot. The distal features consist of wedge-shaped structures with a base width of 50 µm and a height of approximately 180 µm. The wedges are mounted atop angled cylindrical features, 380 µm in diameter by approximately 1 mm long. Together, the proximal and distal features bend preferentially in the direction of inclination when loaded with a tangential force, achieving a combination of directional adhesion and conformation to rough surfaces. Using this system, a four legged robot that was previously restricted to climbing smooth surfaces is able to climb vertical surfaces such as a wood panels, painted metals, and plastics. On rougher surfaces, the two-tiered system improves adhesion by a factor of five compared to the wedge features alone. The hierarchical system also improved alignment and performance for large patch sizes.

Gecko-inspired climbing behaviors on vertical and overhanging surfaces

The adhesive and frictional properties of dry adhesive materials can be described by a three-dimensional limit surface in the space of normal and tangential contact forces at the feet. We present the empirically derived limit surface for directional adhesive pads and illustrate its application to controlling the forces at the feet of a robot climbing on arbitrary slopes, including overhanging surfaces. For the directional adhesive patches that we have developed, the limit surface is convex, which permits efficient computation of the desired internal and external forces among the feet to maximize a safety margin with respect to disturbance forces on the robot. The limit surface also intersects the origin in force space, which enables efficient climbing without wasting energy in attaching and detaching the feet. These insights are applied to an experimental climbing platform demonstrating the proper use of directional adhesion and mimicking the climbing behavior seen in geckos.

Design and testing of a selectively compliant underactuated hand

Motivated by the requirements of mobile manipulation, a compliant underactuated hand, capable of locking individual joints, has been developed. Locking is accomplished with electrostatic brakes in the joints and significantly increases the maximum pullout forces for power grasps. In addition, by locking and unlocking joints, the hand can adopt configurations and grasp sequences that would otherwise require a fully actuated solution. Other features of the hand include an integrated sensing suite that uses a common transduction technology on flexible printed circuits for tactile and proprioceptive sensing. The hand is analyzed using a three-dimensional rigid body analysis package with efficient simulation of compliant mechanisms and contacts with friction. This package allows one to evaluate design tradeoffs among link lengths, required tendon tensions, spring stiffnesses and braking requirements to grasp and hold a wide range of objects. Results of grasping and pullout tests confirm the utility of the simulations.

Varying spring preloads to select grasp strategies in an adaptive hand

We describe an underactuated hand mechanism that is able to adopt a wide range of grasp types by varying the internal forces in its fingers. The adjustment is accomplished by varying the preloads of springs, which affect the grasp stability and stiffness for large and small objects. Preload adjustment can be accomplished with low power, non-backdrivable actuators in the fingers. The analysis is presented first for a planar, two-fingered hand to illustrate the trends and tradeoffs associated with variations in preload. The results are then applied numerically to a three fingered hand with three phalanges per finger. This design is a prototype for a hand to be used in an underwater oil drilling platform under conditions of low friction and uncertain object locations.

Slip classification for dynamic tactile array sensors

The manipulation of objects held in a robotic hand or gripper is accompanied by events such as making and breaking contact and slippage, between the fingertips and the grasped object and between the grasped object and external surfaces. Humans can distinguish among such events, in part, because they excite the various mechanoreceptors in the hands differently. As part of an effort to provide robots with a similar capability, we propose two features that can be extracted from dynamic tactile array data and used to discriminate between hand/object and object/world slips. Both features rely on examining how slippage affects an array of dynamic tactile sensors compared with the way it affects individual elements of the array. In comparison with approaches that require extensive training with particular combinations of objects and skin, the features work for a wide range of frequencies and grasp conditions. The performance and generalizability of the features are verified with testing on three different kinds of sensors and for a range of object textures, grasp forces and slip conditions. Both features demonstrate greater than 85% accuracy in identifying the location of slip.

Slip interface classification through tactile signal coherence

The manipulation of objects in a hand or gripper is typically accompanied by events such as slippage, between the fingers and a grasped object or between the object and external surfaces. Humans can identify such events using a combination of superficial and deep mechanoreceptors. In robotic hands, with more limited tactile sensing, such events can be hard to distinguish. This paper presents a signal processing method that can help to distinguish finger/object and object/world events based on multidimensional coherence, which measures whether a group of signals are sampling a single input or a group of incoherent inputs. A simple linear model of the fingertip/object interaction demonstrates how signal coherence can be used for slip classification. The method is evaluated through controlled experiments that produce similar results for two very different tactile sensing suites.

Biologically inspired tactile classification of object-hand and object-world interactions

During manipulation, a grasped object may slip relative to either the hand or the environment, with quite different consequences. In humans these events excite the mechanoreceptors differently, which allows them to be identified and reacted to accordingly. This paper presents a tactile sensor suite that can produce an array of signals over a wide range of frequencies and investigates combinations of sensors and frequency ranges that make it possible to distinguish object/hand slippage from object/world slippage for a variety of textures and conditions. The approach is demonstrated in controlled experiments, producing both types of slip.

Contact event detection for robotic oil drilling

To ensure safe and reliable operation in a robotic oil drilling system, it is essential to detect contact events such as impacts and slips between end-effectors and workpieces. In this challenging application, where high forces are used to manipulate heavy metal pipes in noisy environments, acoustic emissions (AE) sensors offer a promising contact sensing solution. Real-time AE signal features are used to create a multinomial contact event classifier. The sensitivity of signal features to a variety of contact events including two types of slip is presented. Results indicate that the classifier is able to robustly and dynamically classify contact events with >90% accuracy using a small set of AE signal features.