Design and evolution of a modular tensegrity robot platform

Bruce, Jonathan; Caluwaerts, Ken; İşçen, Atıl; Sabelhaus, Andrew P.; SunSpiral, Vytas

doi:10.1109/icra.2014.6907361

Cited by 70 publications

(63 citation statements)

References 22 publications

(26 reference statements)

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“…The Spherical Underactuated Planetary Exploration Robot (SUPERball) is a tensegrity icosahedron robot currently under development at the NASA Ames Research Center ( Figure 3) [6]. The main design goal of SUPERball is to be a more capable robot than a prior prototype called ReCTeR, to provide more reliable sensors, and to handle rougher environments.…”

Section: Superballmentioning

confidence: 99%

Learning Tensegrity Locomotion Using Open-Loop Control Signals and Coevolutionary Algorithms

et al. 2015

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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.

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Section: Superballmentioning

confidence: 99%

Learning Tensegrity Locomotion Using Open-Loop Control Signals and Coevolutionary Algorithms

et al. 2015

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“…The field of robotic space exploration has many rich and vast studies on explicit implementation concerns, including studies of individual rover systems [45], whole rover concepts [46], highly engineered teams of rovers suited for specific missions [4,47], long-term visions for the global future of space exploration [48], distributed sensor networks for making observations of environmental phenomena through a planning system [49], autonomous observation of temporal phenomena [50], or tools for managing data on space missions [51]. In contrast, in this work, we conduct laboratory-based experiments featuring a team of rovers with the generic capabilities to sense, move, and observe; we do not seek to specify how any of these systems work at the low level, but instead look at the larger implications of the need for teamwork in such a system.…”

Section: Robotic Space Explorationmentioning

confidence: 99%

Autonomous Multiagent Space Exploration with High-Level Human Feedback

Colby

Yliniemi

Tumer

2016

Journal of Aerospace Information Systems

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Robotic space-exploration missions have always pushed the limits of science and technology, and will continue to do so by their very nature. Such missions are particularly challenging, as they operate in environments with high uncertainty, light-time delays, and high mission costs. Artificial-intelligence-based multiagent systems can alleviate these concerns by 1) creating autonomous multirobot teams that can function in uncertain environments, 2) navigating and operating without time-sensitive commands from Earth-bound scientists, and 3) spreading the mission cost across multiple platforms that will eliminate the danger of total mission loss in the case of a malfunctioning robot. In this work, a novel human in-the-loop cooperative coevolutionary algorithm is presented to train a multirobot system exploring an unknown environment. Autonomous robots learn to make low-level control decisions to maximize scientific data acquisition, whereas human scientists on Earth learn the changing mission profiles and provide highlevel objectives to the robots. Results demonstrate that the algorithm reduces the number of robots needed for a particular performance level tenfold compared to traditional cooperative coevolutionary algorithms for configurations of 10 or more rovers, resulting in significantly lower mission costs. Further, the trained multirobot system is extremely robust to noise, and 10% sensor and actuator noise (with and without sensor bias) has no statistically significant impact on system performance. Finally, the system is extremely robust to robot failures; for any percentage of robot failures between 10 and 90%, the percentage loss in performance is less than the percentage of failed robots.

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“…et al, 2012;Bruce et al, 2014). In general, moving quasi-statically is advantageous since it requires less powerful actuators.…”

Section: Introductionmentioning

confidence: 99%

Path planning for active tensegrity structures

Porta

Hernández-Juan

2016

International Journal of Solids and Structures

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This paper presents a path planning method for actuated tensegrity structures with quasi-static motion. The valid configurations for such structures lay on an equilibrium manifold, which is implicitly defined by a set of kinematic and static constraints. The exploration of this manifold is difficult with standard methods due to the lack of a global parametrization. Thus, this paper proposes the use of techniques with roots in differential geometry to define an atlas, i.e., a set of coordinated local parameterizations of the equilibrium manifold. This atlas is exploited to define a rapidly-exploring random tree, which efficiently finds valid paths between configurations. However, these paths are typically long and jerky and, therefore, this paper also introduces a procedure to reduce their control effort. A variety of test cases are presented to empirically evaluate the proposed method.

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Design and evolution of a modular tensegrity robot platform

Cited by 70 publications

References 22 publications

Learning Tensegrity Locomotion Using Open-Loop Control Signals and Coevolutionary Algorithms

Learning Tensegrity Locomotion Using Open-Loop Control Signals and Coevolutionary Algorithms

Autonomous Multiagent Space Exploration with High-Level Human Feedback

Path planning for active tensegrity structures

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