In the critical 72 hours after an earthquake or explosion, a race against the clock begins to find survivors. When a powerful earthquake hit central Italy on 24 August 2016, killing 299 people, over 5 000 emergency workers were mobilised in search and rescue efforts that saved dozens from the rubble in the immediate aftermath. The pressure to move fast can create risks for first responders, who often face unstable environments with little information about the dangers ahead. But this type of rescue work could soon become safer and more efficient thanks to a joint effort by EU and Japanese researchers. Rescue organisations, research institutes and companies from both Europe and Japan worked together from 2019 to 2023 to develop a new generation of tools blending robotics, drone technology and chemical sensing to transform how emergency teams operate in disaster zones.

--Efficient path optimization for drones in search and rescue operations faces challenges, including limited visibility, time constraints, and complex information gathering in urban environments. We present a comprehensive approach to optimize UA V-based search and rescue operations in neighborhood areas, utilizing both a 3D AirSim-ROS2 simulator and a 2D simulator . The path planning problem is formulated as a partially observable Markov decision process (POMDP), and we propose a novel "Shrinking POMCP" approach to address time constraints. In the AirSim environment, we integrate our approach with a probabilistic world model for belief maintenance and a neu-rosymbolic navigator for obstacle avoidance. The 2D simulator employs surrogate ROS2 nodes with equivalent functionality. We compare trajectories generated by different approaches in the 2D simulator and evaluate performance across various belief types in the 3D AirSim-ROS simulator . Experimental results from both simulators demonstrate that our proposed shrinking POMCP solution achieves significant improvements in search times compared to alternative methods, showcasing its potential for enhancing the efficiency of UA V-assisted search and rescue operations. Search and rescue (SAR) operations are critical, time-sensitive missions conducted in challenging environments like neighborhoods, wilderness [1], or maritime settings [2]. These resource-intensive operations require efficient path planning and optimal routing [3]. In recent years, Unmanned Aerial V ehicles (UA Vs) have become valuable SAR assets, offering advantages such as rapid deployment, extended flight times, and access to hard-to-reach areas. Equipped with sensors and cameras, UA Vs can detect heat signatures, identify objects, and provide real-time aerial imagery to search teams [4]. However, the use of UA Vs in SAR operations presents unique challenges, particularly in path planning and decision-making under uncertainty. Factors such as limited battery life, changing weather conditions, and incomplete information about the search area complicate the task of efficiently coordinating UA V movements to maximize the probability of locating targets [3].

Remember watching'The Jetsons' and wishing you had your own Rosey, the robotic maid? She did the cooking, the cleaning and everything else for George, Jane, Judy, Elroy and their dog, Astro. The idea of robot companions doing the mundane parts of life for us for a long time seemed fantastical - but not anymore. Today robots build our cars, sort our packages, and organize warehouses, but unless you work in one of those industries, you probably rarely interact with one. That could all change soon.

This paper details the challenges in applying two computer vision systems, an EfficientDET supervised learning model and the unsupervised RX spectral classifier, to 98.9 GB of drone imagery from the Wu-Murad wilderness search and rescue (WSAR) effort in Japan and identifies 3 directions for future research. There have been at least 19 proposed approaches and 3 datasets aimed at locating missing persons in drone imagery, but only 3 approaches (2 unsupervised and 1 of an unknown structure) are referenced in the literature as having been used in an actual WSAR operation. Of these proposed approaches, the EfficientDET architecture and the unsupervised spectral RX classifier were selected as the most appropriate for this setting. The EfficientDET model was applied to the HERIDAL dataset and despite achieving performance that is statistically equivalent to the state-of-the-art, the model fails to translate to the real world in terms of false positives (e.g., identifying tree limbs and rocks as people), and false negatives (e.g., failing to identify members of the search team). The poor results in practice for algorithms that showed good results on datasets suggest 3 areas of future research: more realistic datasets for wilderness SAR, computer vision models that are capable of seamlessly handling the variety of imagery that can be collected during actual WSAR operations, and better alignment on performance measures.

A former OceanGate Expeditions customer who took a trip to see the Titanic wreckage two years ago described the dive as a "kamikaze operation." An international search and rescue operation is ongoing for five crew members on OceanGate's Titan sub, which went missing Sunday on a planned deep sea tourist expedition. Arthur Loibl, a retired German businessman and adventurer who went on the same trip in 2021, shared his experience with OceanGate in an interview with The Associated Press. "You have to be a little bit crazy to do this sort of thing," Loibl said. He explained that the idea of touring the Titanic wreckage came to him on a trip to the South Pole in 2016.

Researchers at Istituto Italiano di Tecnologia (IIT-Italian Institute of Technology) in Genova has realized a new soft robot inspired by the biology of earthworms,which is able to crawl thanks to soft actuators that elongate or squeeze, when air passes through them or is drawn out. The prototype has been described in the international journal Scientific Reports of the Nature Portfolio, and it is the starting point for developing devices for underground exploration, but also search and rescue operations in confined spaces and the exploration of other planets. Nature offers many examples of animals, such as snakes, earthworms, snails, and caterpillars, which use both the flexibility of their bodies and the ability to generate physical travelling waves along the length of their body to move and explore different environments. Some of their movements are also similar to plant roots. Taking inspiration from nature and, at the same time, revealing new biological phenomena while developing new technologies is the main goal of the BioInspired Soft robotics lab coordinated by Barbara Mazzolai, and this earthworm-like robot is the latest invention coming from her group.

On February 6, 2023, earthquakes measuring 7.7 and 7.6 hit South Eastern Turkey, affecting 10 cities and resulting in more than 42,000 deaths and 120,000 injured as of February 21. A few hours after the earthquake, a group of programmers started a Discord server to roll out an application called afetharita, literally meaning, disaster map. This application would serve search & rescue teams and volunteers to find survivors and bring them help. The need for such an app arose when survivors posted screenshots of texts with their addresses and what they needed (including rescue) on social media. Some survivors also tweeted what they needed so their relatives knew they were alive and that they need rescue.

If you're a construction worker, Boston Dynamics' humanoid robot could soon be coming for your job. New footage from the Massachusetts firm shows Atlas, its scary six-foot-tall bipedal humanoid bot, helping out with construction tasks. Atlas was already able to run and jump, but the footage shows it can now also grip objects, making it an ideal companion on the building site. In the new clip Atlas can be seen laying wood over scaffolding, holding and carrying a tool bag and performing an extravagant mid-air flip. Atlas the most human-like robot in Boston Dynamic's line-up, first unveiled to the public in July 2013.

Leaping around an obstacle course and pulling off backflips, this eerily human-like robot is only too happy to show off its parkour skills. Named Atlas, the humanoid was filmed by Boston Dynamics -- the firm behind the famous robotic dog Spot. The incredible footage shows the two-legged robot impressively maintaining its balance as it takes on a series of jumps, vaults and balance beams. They were set up by Boston Dynamics engineers to experiment with new behaviours for Atlas, as well as developing its whole-body athletics through a variety of rapidly changing, high-energy activities. Boston Dynamics engineers created the obstacle course to develop Atlas' whole-body athletics through a variety of rapidly changing, high-energy activities The humanoid, which was first unveiled to the public in July 2013, measures 1.5m (4.9ft) tall and weighs 75kg (11.8st).

A 65-year-old woman in Utah's Snow Canyon State Park got some unexpected help from a drone operated by the local sheriff's department, after injuring her ankle while hiking with friends. While walking near the edge of Island in the Sky, a famous canyoneering and rock climbing route, she slipped and fell several feet, injuring her ankle to the point where she could no longer stand or support her own weight. The group of three friends she was with called the sheriff's search and rescue team rather than attempt to carry her back down the steep and sandy trail themselves. Search and rescue workers from the Washington Country Sheriff's Department in Utah used a drone to deliver then 660 feet of twine to help setup a rappelling system to get an injured hiker down from a clifftop The sheriff's team decided to bring the woman down from the 400-foot-tall cliff, the equivalent of 40 stories, by strapping her to a stretcher and using a rappelling system to guide her down. The only problem was they didn't have enough rope to reach actually reach the ground.