Spinal Helical Actuation Patterns for Locomotion in Soft Robots.

Spinal-driven locomotion was first hypothesized to exist in biological systems in the 1980s. However, only recently has the concept been applied to legged robots. In implementing spinal-driven locomotion in robots to-date, researchers have focused on bending in the spine. In this article, we propose an additional mode of spinal-driven locomotion: axial torsion via helical actuation patterns. To study torsional spinal-driven locomotion, a six-legged robot with unactuated legs is used. This robot is designed to be modular to allow for changes in the physical system, such as material stiffness of the spine and legs, and has actuators that spiral around the central elastomeric spine of the robot. A model is provided to explain torsional spinal-driven locomotion. Three spinal gaits are developed to allow the robot to walk forward, through which we demonstrate that the speed of the robot can be influenced by the stiffness of the spine and legs. We also demonstrate that a single gait can be used to drive the robot forward and turn the robot left and right by adjusting the leg positions or foot friction. The results indicate that the inclusion of helical actuation patterns can assist in movement. The addition of these actuation patterns or active axial torsion to future, more complex robots with active leg control may enhance the energy efficiency of locomotion or enable fast, dynamic maneuvering.

Scalable co-optimization of morphology and control in embodied machines.

Evolution sculpts both the body plans and nervous systems of agents together over time. By contrast, in artificial intelligence and robotics, a robot's body plan is usually designed by hand, and control policies are then optimized for that fixed design. The task of simultaneously co-optimizing the morphology and controller of an embodied robot has remained a challenge. In psychology, the theory of embodied cognition posits that behaviour arises from a close coupling between body plan and sensorimotor control, which suggests why co-optimizing these two subsystems is so difficult: most evolutionary changes to morphology tend to adversely impact sensorimotor control, leading to an overall decrease in behavioural performance. Here, we further examine this hypothesis and demonstrate a technique for 'morphological innovation protection', which temporarily reduces selection pressure on recently morphologically changed individuals, thus enabling evolution some time to 'readapt' to the new morphology with subsequent control policy mutations. We show the potential for this method to avoid local optima and converge to similar highly fit morphologies across widely varying initial conditions, while sustaining fitness improvements further into optimization. While this technique is admittedly only the first of many steps that must be taken to achieve scalable optimization of embodied machines, we hope that theoretical insight into the cause of evolutionary stagnation in current methods will help to enable the automation of robot design and behavioural training-while simultaneously providing a test bed to investigate the theory of embodied cognition.

Reducing Actuator Requirements in Continuum Robots Through Optimized Cable Routing.

Continuum manipulators offer many advantages compared to their rigid-linked counterparts, such as increased degrees of freedom and workspace volume. Inspired by biological systems, such as elephant trunks and octopus tentacles, many continuum manipulators are made of multiple segments that allow large-scale deformations to be distributed throughout the body. Most continuum manipulators currently control each segment individually. For example, a planar cable-driven system is typically controlled by a pair of cables for each segment, which implies two actuators per segment. In this article, we demonstrate how highly coupled crossing cable configurations can reduce both actuator count and actuator torque requirements in a planar continuum manipulator, while maintaining workspace reachability and manipulability. We achieve highly coupled actuation by allowing cables to cross through the manipulator to create new cable configurations. We further derive an analytical model to predict the underactuated manipulator workspace and experimentally verify the model accuracy with a physical system. We use this model to compare crossing cable configurations to the traditional cable configuration using workspace performance metrics. Our work here focuses on a simplified planar robot, both in simulation and in hardware, with the goal of extending this to spiraling-cable configurations on full 3D continuum robots in future work.

Learning tensegrity locomotion using open-loop control signals and coevolutionary algorithms.

Soft robots offer many advantages over traditional rigid robots. However, soft robots can be difficult to control with standard control methods. Fortunately, evolutionary algorithms can offer an elegant solution to this problem. Instead of creating controls to handle the intricate dynamics of these robots, we can simply evolve the controls using a simulation to provide an evaluation function. In this article, we show how such a control paradigm can be applied to an emerging field within soft robotics: robots based on tensegrity structures. We take the model of the Spherical Underactuated Planetary Exploration Robot ball (SUPERball), an icosahedron tensegrity robot under production at NASA Ames Research Center, develop a rolling locomotion algorithm, and study the learned behavior using an accurate model of the SUPERball simulated in the NASA Tensegrity Robotics Toolkit. We first present the historical-average fitness-shaping algorithm for coevolutionary algorithms to speed up learning while favoring robustness over optimality. Second, we use a distributed control approach by coevolving open-loop control signals for each controller. Being simple and distributed, open-loop controllers can be readily implemented on SUPERball hardware without the need for sensor information or precise coordination. We analyze signals of different complexities and frequencies. Among the learned policies, we take one of the best and use it to analyze different aspects of the rolling gait, such as lengths, tensions, and energy consumption. We also discuss the correlation between the signals controlling different parts of the tensegrity robot.

Design and control of compliant tensegrity robots through simulation and hardware validation.

To better understand the role of tensegrity structures in biological systems and their application to robotics, the Dynamic Tensegrity Robotics Lab at NASA Ames Research Center, Moffett Field, CA, USA, has developed and validated two software environments for the analysis, simulation and design of tensegrity robots. These tools, along with new control methodologies and the modular hardware components developed to validate them, are presented as a system for the design of actuated tensegrity structures. As evidenced from their appearance in many biological systems, tensegrity ('tensile-integrity') structures have unique physical properties that make them ideal for interaction with uncertain environments. Yet, these characteristics make design and control of bioinspired tensegrity robots extremely challenging. This work presents the progress our tools have made in tackling the design and control challenges of spherical tensegrity structures. We focus on this shape since it lends itself to rolling locomotion. The results of our analyses include multiple novel control approaches for mobility and terrain interaction of spherical tensegrity structures that have been tested in simulation. A hardware prototype of a spherical six-bar tensegrity, the Reservoir Compliant Tensegrity Robot, is used to empirically validate the accuracy of simulation.