AIMC Topic: Grasshoppers

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Path planning of locust-inspired jumping robots in obstacle-dense environments using curriculum reinforcement learning.

Bioinspiration & biomimetics
Biologically-inspired jumping robots have demonstrated remarkable adaptability in complex environments, making them increasingly valuable across various fields. However, effective path planning in obstacle-dense environments for large-scale jumping r...

An AI-powered smart Agribot for detecting locusts in farmlands using IoT and deep learning.

Scientific reports
In many countries, locusts have significantly harmed agricultural production. To prevent their spread, the Agriculture Robot (Agribot) with cutting-edge technologies like the Internet of Things (IoT) and Machine Learning (ML) can be a possible soluti...

A bio-inspired visual collision detection network integrated with dynamic temporal variance feedback regulated by scalable functional countering jitter streaming.

Neural networks : the official journal of the International Neural Network Society
In pursuing artificial intelligence for efficient collision avoidance in robots, researchers draw inspiration from the locust's visual looming-sensitive neural circuit to establish an efficient neural network for collision detection. However, existin...

Multiple forces facilitate the aquatic acrobatics of grasshopper and bioinspired robot.

Proceedings of the National Academy of Sciences of the United States of America
Aquatic locomotion is challenging for land-dwelling creatures because of the high degree of fluidity with which the water yields to loads. We surprisingly found that the Chinese rice grasshopper , known for its terrestrial acrobatics, could swiftly l...

Ear-Bot: Locust Ear-on-a-Chip Bio-Hybrid Platform.

Sensors (Basel, Switzerland)
During hundreds of millions of years of evolution, insects have evolved some of the most efficient and robust sensing organs, often far more sensitive than their man-made equivalents. In this study, we demonstrate a hybrid bio-technological approach,...

A Robust Collision Perception Visual Neural Network With Specific Selectivity to Darker Objects.

IEEE transactions on cybernetics
Building an efficient and reliable collision perception visual system is a challenging problem for future robots and autonomous vehicles. The biological visual neural networks, which have evolved over millions of years in nature and are working perfe...

DeepPoseKit, a software toolkit for fast and robust animal pose estimation using deep learning.

eLife
Quantitative behavioral measurements are important for answering questions across scientific disciplines-from neuroscience to ecology. State-of-the-art deep-learning methods offer major advances in data quality and detail by allowing researchers to a...

Self-supervised learning of the biologically-inspired obstacle avoidance of hexapod walking robot.

Bioinspiration & biomimetics
In this paper, we propose an integrated biologically inspired visual collision avoidance approach that is deployed on a real hexapod walking robot. The proposed approach is based on the Lobula giant movement detector (LGMD), a neural network for loom...

Design and demonstration of a bio-inspired flapping-wing-assisted jumping robot.

Bioinspiration & biomimetics
Jumping insects such as fleas, froghoppers, grasshoppers, and locusts take off from the ground using a catapult mechanism to push their legs against the surface of the ground while using their pairs of flapping wings to propel them into the air. Such...

Morphological intelligence counters foot slipping in the desert locust and dynamic robots.

Proceedings of the National Academy of Sciences of the United States of America
During dynamic terrestrial locomotion, animals use complex multifunctional feet to extract friction from the environment. However, whether roboticists assume sufficient surface friction for locomotion or actively compensate for slipping, they use rel...