Protecting and restoring forest ecosystems has become an important conservation issue. Although various robots have been used for field data collection to protect forest ecosystems, the complex terrain and dense canopy make the data collection less efficient. To address this challenge, an aerial platform with bio-inspired behaviour facilitated by a bio-inspired mechanism is proposed. The platform spends minimum energy during data collection by perching on tree branches. A raptor inspired vision algorithm is used to locate a tree trunk, and then a horizontal branch on which the platform can perch is identified. A tendon-driven mechanism inspired by bat claws which requires energy only for actuation, secures the platform onto the branch using the mechanism's passive compliance. Experimental results show that the mechanism can perform perching on branches ranging from 30 mm to 80 mm in diameter. The real-world tests validated the system's ability to select and adapt to target points, and it is expected to be useful in complex forest ecosystems.

This research focuses on the development of a drone equipped with pruning tools and a stereo vision camera to accurately detect and measure the spatial positions of tree branches. YOLO is employed for branch segmentation, while two depth estimation approaches, monocular and stereo, are investigated. In comparison to SGBM, deep learning techniques produce more refined and accurate depth maps. In the absence of ground-truth data, a fine-tuning process using deep neural networks is applied to approximate optimal depth values. This methodology facilitates precise branch detection and distance measurement, addressing critical challenges in the automation of pruning operations. The results demonstrate notable advancements in both accuracy and efficiency, underscoring the potential of deep learning to drive innovation and enhance automation in the agricultural sector.

Manual pruning of radiata pine trees presents significant safety risks due to their substantial height and the challenging terrains in which they thrive. To address these risks, this research proposes the development of a drone-based pruning system equipped with specialized pruning tools and a stereo vision camera, enabling precise detection and trimming of branches. Deep learning algorithms, including YOLO and Mask R-CNN, are employed to ensure accurate branch detection, while the Semi-Global Matching algorithm is integrated to provide reliable distance estimation. The synergy between these techniques facilitates the precise identification of branch locations and enables efficient, targeted pruning. Experimental results demonstrate that the combined implementation of YOLO and SGBM enables the drone to accurately detect branches and measure their distances from the drone. This research not only improves the safety and efficiency of pruning operations but also makes a significant contribution to the advancement of drone technology in the automation of agricultural and forestry practices, laying a foundational framework for further innovations in environmental management.

Visual Question Answering (VQA) is a task that requires computers to give correct answers for the input questions based on the images. This task can be solved by humans with ease but is a challenge for computers. The VLSP2022-EVJVQA shared task carries the Visual Question Answering task in the multilingual domain on a newly released dataset: UIT-EVJVQA, in which the questions and answers are written in three different languages: English, Vietnamese and Japanese. We approached the challenge as a sequence-to-sequence learning task, in which we integrated hints from pre-trained state-of-the-art VQA models and image features with Convolutional Sequence-to-Sequence network to generate the desired answers. Our results obtained up to 0.3442 by F1 score on the public test set, 0.4210 on the private test set, and placed 3rd in the competition.

Engineers have created a falcon-inspired robot that can take-off, land and grasp branches just like a real bird – and even catch objects in the air. Developed by a team at Stanford University, SNAG (stereotyped nature-inspired aerial grasper) replicates the impressive grasp of peregrine falcons. In place of bones, SNAG has a 3D-printed skeletal structure – which took 20 iterations to perfect – as well as motors and fishing line in place of muscles and tendons. Thanks to a quadcopter drone attached, SNAG can fly around in its quest to catch and carry objects and perch on various surfaces. Coupled with cameras and sensors, SNAG could be used for monitoring the climate, wildlife and natural ecosystems – as part of efforts to prevent forest fires for example – as well as for search and rescue efforts.

I hate mowing the lawn. First off, a vast expanse of unproductive grass is a waste--why have grass when you could have a garden, or an orchard, or all sorts of other useful plants? That I have to push around a device just to maintain this green wasteland makes it doubly insulting. Fortunately, this summer I unleashed the Husqvarna 435X AWD automower on my lawn. Now I never have to think about mowing it again.

Waymo from 2011 has been developing its own set of sensors from the ground up, including three different types of lidars. The company is now making these sensors available to companies outside of self-driving?--?beginning with robotics, security, agricultural technology, and more?--?so they can achieve their own technological breakthroughs. The company has announced that one of our 3D lidar sensors, called Laser Bear Honeycomb, is available to select partners. According to the company, Laser Bear Honeycomb is a best-in-class perimeter sensor. That means one Honeycomb can do the job of three other 3D sensors stacked on top of one another.

The key, according to the company, is in the use of unsupervised learning that allows an autonomous system to process data and figure out its environment. For example, you could feed the system a thousand images of stop signs -- some faded, some obscured by tree branches, etc. Stop signs, for example, are red with white borders and eight sides. The AI can learn as it goes that sometimes that red is faded and sometimes a white border will be obscured by a tree branch, but it can make changes as needed to classify stop signs as stop signs.

An orangutan has been captured performing DIY better than some humans. The incredible new footage reveals a female great ape using a saw to skilfully divide a branch in two. The talented ape uses her right hand to hold the tool and her feet to grip the tree branch like a vice. She even blows away the sawdust to inspect her work like a true craftsman. An orangutan has been captured performing DIY better than some humans.