Simulation and Analysis of Microspines Interlocking Behavior on Rocky Surfaces: An In-Depth Study of the Isolated Spine

Iacoponi, Saverio; Laschi, Cecilia

doi:10.1115/1.4047725

Cited by 9 publications

(7 citation statements)

References 18 publications

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“…Among them, the f ( x ) function is the activation function; x i , ω i ( i =1,2,3) correspond to the three inputs and weights, respectively; and b is a scalar called the bias parameter [ 9 ]. The activation functions used by neural networks also usually have different types, and the use of different transfer functions will also affect the differences in the structure and function of neural networks.…”

Section: Deep Learning Target Detection Systemmentioning

confidence: 99%

Deep Learning Target Detection System for Sewage Treatment

Lin

Wang

et al. 2022

Computational Intelligence and Neuroscience

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Object detection is to identify objects and then find some objects of interest. With the development of computers, target detection has evolved from traditional detection methods to artificial intelligence methods, and the latter are mainly based on some algorithms of deep learning. This paper mainly tests the treated sewage. First, the neural network and convolutional neural network algorithms in deep learning are studied, and then a target detection system is built based on these two algorithms. Finally, the treated sewage is detected and then compared with that of the traditional target detection system. The experimental results show that the target detection system of the convolutional neural network algorithm has a very stable recognition rate for the treated sewage, swinging around 70%, and the amplitude is not large. However, the target detection system of the neural network algorithm is not very stable in the recognition rate of the treated sewage, and the recognition rate is about 60%.

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Section: Deep Learning Target Detection Systemmentioning

confidence: 99%

Deep Learning Target Detection System for Sewage Treatment

Lin

Wang

et al. 2022

Computational Intelligence and Neuroscience

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“…As part of this process, we need to estimate the maximum pull force that a boom and gripper can exert while grasping a rock feature. As noted in previous work (38)(39)(40), grasping rock surfaces with microspines is inherently stochastic but can be modeled with a defined mean and SD. In contrast to some previous applications involving arrays of microspines (22,38,46,47), we do not assume that the surface is nearly flat at the length scale of the gripper; instead, we seek rounded features that the gripper can partially enclose.…”

Section: Gripper Modeling and Grasp Prediction Through Limit Surfacesmentioning

confidence: 99%

“…Grasping or climbing rocky surfaces with spines has been addressed in various publications, including some that address space applications (22,(32)(33)(34)(35)(36)(37). The analysis of collections of spines has also been addressed in detail (33,(38)(39)(40). We drew on this work for the design and analysis of ReachBot's grippers.…”

Section: Introductionmentioning

confidence: 99%

Locomotion as manipulation with ReachBot

Chen,

Newdick,

et al. 2024

Sci. Robot.

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Caves and lava tubes on the Moon and Mars are sites of geological and astrobiological interest but consist of terrain that is inaccessible with traditional robot locomotion. To support the exploration of these sites, we present ReachBot, a robot that uses extendable booms as appendages to manipulate itself with respect to irregular rock surfaces. The booms terminate in grippers equipped with microspines and provide ReachBot with a large workspace, allowing it to achieve force closure in enclosed spaces, such as the walls of a lava tube. To propel ReachBot, we present a contact-before-motion planner for nongaited legged locomotion that uses internal force control, similar to a multifingered hand, to keep its long, slender booms in tension. Motion planning also depends on finding and executing secure grips on rock features. We used a Monte Carlo simulation to inform gripper design and predict grasp strength and variability. In addition, we used a two-step perception system to identify possible grasp locations. To validate our approach and mechanisms under realistic conditions, we deployed a single ReachBot arm and gripper in a lava tube in the Mojave Desert. The field test confirmed that ReachBot will find many targets for secure grasps with the proposed kinematic design.

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“…This concept was demonstrated with a soft robotic prototype with an inflatable body [89] and with a monopedal robot equipped with a deployable body covered by a soft skin [71]. The third approach is passive and aims at increasing the friction with the seabed using microspines [90,91] or dactyl-like feet inspired by crabs [66].…”

Section: Hydrodynamic Effectsmentioning

confidence: 99%

“…Researchers from the Crab Lab of the Case Western Reserve University have recently focused on the use of tapered, curved feet (like crab dactyl shape) paired with a distributed inward gripping method, to allow the amphibious hexapod Sebastian (figure 3(B)) to anchor to the sediment and improve its speed and cost of transport on different kinds of sediment and in the presence of waves [66,92]. Similarly, even though never demonstrated on ULRs, the use of microspines [91], can yield excellent anchoring properties, especially on rocks. Rocky terrains are arguably the most complex to traverse for benthic vehicles, yet they are usually rich in life and demonstrating stable locomotion on rocky substrate should be a priority for ULRs developers.…”

Section: Interaction With the Sedimentmentioning

confidence: 99%

Underwater legged robotics: review and perspectives

et al. 2023

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Nowadays, there is a growing awareness on the social and economic importance of the ocean. In this context, being able to carry out a diverse range of operations underwater is of paramount importance for many industrial sectors as well as for marine science and to enforce restoration and mitigation actions. Underwater robots allowed us to venture deeper and for longer time into the remote and hostile marine environment. However, traditional design concepts such as propeller driven Remotely Operated Vehicles (ROVs), Autonomous Underwater Vehicles (AUVs), or tracked benthic crawlers, present intrinsic limitations, especially when a close interaction with the environment is required. An increasing number of researchers are proposing legged robots as a bioinspired alternative to traditional designs, capable of yielding versatile multi-terrain locomotion, high stability, and low environmental disturbance. In this work, we aim at presenting the new field of underwater legged robotics in an organic way, discussing the prototypes in the state-of-the-art and highlighting technological and scientific challenges for the future. First, we will briefly recap the latest developments in traditional underwater robotics from which several technological solutions can be adapted, and on which the benchmarking of this new field should be set. Second, we will the retrace the evolution of terrestrial legged robotics, pinpointing the main achievements of the field. Third, we will report a complete state of the art on Underwater Legged Robots (ULRs) focusing on the innovations with respect to the interaction with the environment, sensing and actuation, modelling and control, and autonomy and navigation. Finally, we will thoroughly discuss the reviewed literature by comparing traditional and legged underwater robots, highlighting interesting research opportunities, and presenting use case scenarios derived from marine science applications.

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Simulation and Analysis of Microspines Interlocking Behavior on Rocky Surfaces: An In-Depth Study of the Isolated Spine

Cited by 9 publications

References 18 publications

Deep Learning Target Detection System for Sewage Treatment

Deep Learning Target Detection System for Sewage Treatment

Locomotion as manipulation with ReachBot

Underwater legged robotics: review and perspectives

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