An Autonomous Subgram SMA-Based Swimmer
We present the Swima, a bioinspired 900-mg swimmer propelled by two 10-mg high-work-density (HWD) actuators driven by shape-memory alloy (SMA) wires. We integrated onboard power and computation by using a custom-built printed circuit board (PCB) and an 11-mAh 3.7-V 507-mg single-cell lithium-ion (Li-Ion) battery, which in conjunction enable autonomous swimming in excess of 18 min. The Swima can swim at speeds of up to 22.4 mm/s (0.56 Bl/s), achieves turning rates of up to 14°/s, and can follow 0-degree heading reference trajectories with root mean square (RMS) values of tracking errors of about 6.5° across multiple tests. This robot is the first subgram microswimmer with onboard power, actuation, and computation developed to date.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Feedback Control of a Single-Tail Bioinspired 59-mg Swimmer
We present an evolved steerable version of the single-tail Fish-&-Ribbon-Inspired Small Swimming Harmonic roBot (FRISSHBot), a 59-mg biologically inspired swimmer, which is driven by a new shape-memory alloy (SMA)-based …
Unraveling Syntax: Language Modeling and the Substructure of Grammars
While language models achieve impressive results, their learning dynamics are far from understood. Many domains of interest -- such as natural language syntax, coding languages, arithmetic -- are captured by context-free…
SubGram: Extending Skip-gram Word Representation with Substrings
Skip-gram (word2vec) is a recent method for creating vector representations of words ("distributed word representations") using a neural network. The representation gained popularity in various areas of natural language …
Magnetically Driven Elastic Microswimmers: Exploiting Hysteretic Collapse for Autonomous Propulsion and Independent Control
When swimming at low Reynolds numbers, inertial effects are negligible and reciprocal movements cannot induce net motion. Instead, symmetry breaking is necessary to achieve net propulsion. Directed swimming can be suppor…
Deep reinforcement learning for tracking a moving target in jellyfish-like swimming
We develop a deep reinforcement learning method for training a jellyfish-like swimmer to effectively track a moving target in a two-dimensional flow. This swimmer is a flexible object equipped with a muscle model based o…
Deep Reinforcement LearningNavigate