Advances in Bio-Inspired Robots

This book covers three major topics, specifically Biomimetic Robot Design, Mechanical System Design from Bio-Inspiration, and Bio-Inspired Analysis on A Mechanical System. The Biomimetic Robot Design part introduces research on flexible jumping robots, snake robots, and small flying robots, while the Mechanical System Design from Bio-Inspiration part introduces Bioinspired Divide-and-Conquer Design Methodology, Modular Cable-Driven Human-Like Robotic Arm andWall-Climbing Robot. Finally, in the Bio-Inspired Analysis on A Mechanical System part, research contents on the control strategy of Surgical Assistant Robot, modeling of Underwater Thruster, and optimization of Humanoid Robot are introduced

Bio-Inspired Robotics

Modern robotic technologies have enabled robots to operate in a variety of unstructured and dynamically-changing environments, in addition to traditional structured environments. Robots have, thus, become an important element in our everyday lives. One key approach to develop such intelligent and autonomous robots is to draw inspiration from biological systems. Biological structure, mechanisms, and underlying principles have the potential to provide new ideas to support the improvement of conventional robotic designs and control. Such biological principles usually originate from animal or even plant models, for robots, which can sense, think, walk, swim, crawl, jump or even fly. Thus, it is believed that these bio-inspired methods are becoming increasingly important in the face of complex applications. Bio-inspired robotics is leading to the study of innovative structures and computing with sensory–motor coordination and learning to achieve intelligence, flexibility, stability, and adaptation for emergent robotic applications, such as manipulation, learning, and control. This Special Issue invites original papers of innovative ideas and concepts, new discoveries and improvements, and novel applications and business models relevant to the selected topics of ``Bio-Inspired Robotics''. Bio-Inspired Robotics is a broad topic and an ongoing expanding field. This Special Issue collates 30 papers that address some of the important challenges and opportunities in this broad and expanding field

Motion control of a snake robot moving between two non-parallel planes

A control method that makes the head of a snake robot follow an arbitrary trajectory on two non-parallel planes, including coexisting sloped and flat planes, is presented. We clarify an appropriate condition of contact between the robot and planes and design a controller for the part of the robot connecting the two planes that satisfies the contact condition. Assuming that the contact condition is satisfied, we derive a simplified model of the robot and design a controller for trajectory tracking of the robot’s head. The controller uses kinematic redundancy to avoid violating the limit of the joint angle and a collision between the robot and the edge of a plane. The effectiveness of the proposed method is demonstrated in experiments using an actual robot

Biological, simulation, and robotic studies to discover principles of swimming within granular media

The locomotion of organisms whether by running, flying, or swimming is the result of multiple degree-of-freedom nervous and musculoskeletal systems interacting with an environment that often flows and deforms in response to movement. A major challenge in biology is to understand the locomotion of organisms that crawl or burrow within terrestrial substrates like sand, soil, and muddy sediments that display both solid and fluid-like behavior. In such materials, validated theories such as the Navier-Stokes equations for fluids do not exist, and visualization techniques (such as particle image velocimetry in fluids) are nearly nonexistent. In this dissertation we integrated biological experiment, numerical simulation, and a physical robot model to reveal principles of undulatory locomotion in granular media. First, we used high speed x-ray imaging techniques to reveal how a desert dwelling lizard, the sandfish, swims within dry granular media without limb use by propagating a single period sinusoidal traveling wave along its body, resulting in a wave efficiency, the ratio of its average forward speed to wave speed, of approximately 0.5. The wave efficiency was independent of the media preparation (loosely and tightly packed). We compared this observation against two complementary modeling approaches: a numerical model of the sandfish coupled to a discrete particle simulation of the granular medium, and an undulatory robot which was designed to swim within granular media. We used these mechanical models to vary the ratio of undulation amplitude (A) to wavelength (λ) and demonstrated that an optimal condition for sand-swimming exists which results from competition between A and λ. The animal simulation and robot model, predicted that for a single period sinusoidal wave, maximal speed occurs for A/ λ = 0.2, the same kinematics used by the sandfish. Inspired by the tapered head shape of the sandfish lizard, we showed that the lift forces and hence vertical position of the robot as it moves forward within granular media can be varied by designing an appropriate head shape and controlling its angle of attack, in a similar way to flaps or wings moving in fluids. These results support the biological hypotheses which propose that morphological adaptations of desert dwelling organisms aid in their subsurface locomotion. This work also demonstrates that the discovery of biological principles of high performance locomotion within sand can help create the next generation of biophysically inspired robots that could explore potentially hazardous complex flowing environments.PhDCommittee Chair: Daniel I. Goldman; Committee Member: Hang Lu; Committee Member: Jeanette Yen; Committee Member: Shella Keilholz; Committee Member: Young-Hui Chan

Robots that can adapt like animals

As robots leave the controlled environments of factories to autonomously function in more complex, natural environments, they will have to respond to the inevitable fact that they will become damaged. However, while animals can quickly adapt to a wide variety of injuries, current robots cannot "think outside the box" to find a compensatory behavior when damaged: they are limited to their pre-specified self-sensing abilities, can diagnose only anticipated failure modes, and require a pre-programmed contingency plan for every type of potential damage, an impracticality for complex robots. Here we introduce an intelligent trial and error algorithm that allows robots to adapt to damage in less than two minutes, without requiring self-diagnosis or pre-specified contingency plans. Before deployment, a robot exploits a novel algorithm to create a detailed map of the space of high-performing behaviors: This map represents the robot's intuitions about what behaviors it can perform and their value. If the robot is damaged, it uses these intuitions to guide a trial-and-error learning algorithm that conducts intelligent experiments to rapidly discover a compensatory behavior that works in spite of the damage. Experiments reveal successful adaptations for a legged robot injured in five different ways, including damaged, broken, and missing legs, and for a robotic arm with joints broken in 14 different ways. This new technique will enable more robust, effective, autonomous robots, and suggests principles that animals may use to adapt to injury

Data-Driven Methods for Geometric Systems

The tools of geometric mechanics provide a compact representation of locomotion dynamics as ``the reconstruction equation''. We have found this equation yields a convenient form for estimating models directly from observation data. This convenience draws from the method's relatively rare feature of providing high accuracy models with little effort. By little effort, we point to the modeling process's low data requirements and the property that nothing about the implementation changes when substituting robot kinematics, material properties, or environmental conditions, as long as some intuitive baseline features of the dynamics are shared. We have applied data-driven geometric mechanics models toward optimizing robot behaviors both physical and simulated, exploring robots' ability to recover from injury, and efficiently creating libraries of maneuvers to be used as building blocks for higher-level robot tasks. Our methods employed the tools of data-driven Floquet analysis, providing a phase that we used as a means of grouping related measurements, allowing us to estimate a reconstruction equation model as a function of phase in the neighborhood of an observed behavior. This tool allowed us to build models at unanticipated scales of complexity and speed. Our use of a perturbation expansion for the geometric terms led to an improved estimation procedure for highly damped systems containing nontrivial but non-dominating amounts of momentum. Analysis of the role of passivity in dissipative systems led to another extension of the estimation procedure to robots with high degrees of underactuation in their internal shape, such as soft robots. This thesis will cover these findings and results, simulated and physical, and the surprising practicality of data-driven geometric mechanics.PHDRoboticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/168033/1/babitt_1.pd

Addressing Tasks Through Robot Adaptation

Developing flexible, broadly capable systems is essential for robots to move out of factories and into our daily lives, functioning as responsive agents that can handle whatever the world throws at them. This dissertation focuses on two kinds of robot adaptation. Modular self-reconfigurable robots (MSRR) adapt to the requirements of their task and environments by transforming themselves. By rearranging the connective structure of their component robot modules, these systems can assume different morphologies: for example, a cluster of modules might configure themselves into a car to maneuver on flat ground, a snake to climb stairs, or an arm to pick and place objects. Conversely, environment augmentation is a strategy in which the robot transforms its environment to meet its own needs, adding physical structures that allow it to overcome obstacles. In both areas, the presented work includes elements of hardware design, algorithms, and integrated systems, with the common goal of establishing these methods of adaptation as viable strategies to address tasks. The research takes a systems-level view of robotics, placing particular emphasis on experimental validation in hardware

Kinematics and Robot Design I, KaRD2018

This volume collects the papers published on the Special Issue “Kinematics and Robot Design I, KaRD2018” (https://www.mdpi.com/journal/robotics/special_issues/KARD), which is the first issue of the KaRD Special Issue series, hosted by the open access journal “MDPI Robotics”. The KaRD series aims at creating an open environment where researchers can present their works and discuss all the topics focused on the many aspects that involve kinematics in the design of robotic/automatic systems. Kinematics is so intimately related to the design of robotic/automatic systems that the admitted topics of the KaRD series practically cover all the subjects normally present in well-established international conferences on “mechanisms and robotics”. KaRD2018 received 22 papers and, after the peer-review process, accepted only 14 papers. The accepted papers cover some theoretical and many design/applicative aspects