This paper presents a novel autonomous drone-based smoke plume tracking system capable of navigating and tracking plumes in highly unsteady atmospheric conditions. The system integrates advanced hardware and software and a comprehensive simulation environment to ensure robust performance in controlled and real-world settings. The quadrotor, equipped with a high-resolution imaging system and an advanced onboard computing unit, performs precise maneuvers while accurately detecting and tracking dynamic smoke plumes under fluctuating conditions. Our software implements a two-phase flight operation, i.e., descending into the smoke plume upon detection and continuously monitoring the smoke movement during in-plume tracking. Leveraging Proportional Integral-Derivative (PID) control and a Proximal Policy Optimization based Deep Reinforcement Learning (DRL) controller enables adaptation to plume dynamics. Unreal Engine simulation evaluates performance under various smoke-wind scenarios, from steady flow to complex, unsteady fluctuations, showing that while the PID controller performs adequately in simpler scenarios, the DRL-based controller excels in more challenging environments. Field tests corroborate these findings. This system opens new possibilities for drone-based monitoring in areas like wildfire management and air quality assessment. The successful integration of DRL for real-time decision-making advances autonomous drone control for dynamic environments.

The next ubiquitous computing platform, following personal computers and smartphones, is poised to be inherently autonomous, encompassing technologies like drones, robots, and self-driving cars. Ensuring reliability for these autonomous machines is critical. However, current resiliency solutions make fundamental trade-offs between reliability and cost, resulting in significant overhead in performance, energy consumption, and chip area. This is due to the "one-size-fits-all" approach commonly used, where the same protection scheme is applied throughout the entire software computing stack. This paper presents the key insight that to achieve high protection coverage with minimal cost, we must leverage the inherent variations in robustness across different layers of the autonomous machine software stack. Specifically, we demonstrate that various nodes in this complex stack exhibit different levels of robustness against hardware faults. Our findings reveal that the front-end of an autonomous machine's software stack tends to be more robust, whereas the back-end is generally more vulnerable. Building on these inherent robustness differences, we propose a Vulnerability-Adaptive Protection (VAP) design paradigm. In this paradigm, the allocation of protection resources - whether spatially (e.g., through modular redundancy) or temporally (e.g., via re-execution) - is made inversely proportional to the inherent robustness of tasks or algorithms within the autonomous machine system. Experimental results show that VAP provides high protection coverage while maintaining low overhead in both autonomous vehicle and drone systems.

Quick, efficient and with a bird's eye view of any scene, more police departments are embracing the use of drones to carry out law enforcement work, with some blue cities now even using them to respond to 911 calls. Around 1,500 police departments across the country are currently using drones in some form, according to a report by the Electronic Frontier Foundation, a digital privacy group, with agencies deploying the technology for crowd control purposes, missing people searches, tracking fleeing suspects or mapping crime scenes. Steep budget cuts and dwindling staff numbers in blue cities, in particular, make drones both an effective and cost-saving tool for police in Democratic strongholds. A law enforcement official sets up a drone during a manhunt for suspect Robert Card following a mass shooting on Oct. 27, 2023, in Monmouth, Maine. Today's police drones are much bigger than regular drones commonly used for recreational purposes, with much longer battery lives and features such as thermal sensors, loudspeakers, spotlights or beacons.

With the advancement of Internet of Things (IoT) technology, its applications span various sectors such as public, industrial, private and military. In particular, the drone sector has gained significant attention for both commercial and military purposes. As a result, there has been a surge in research focused on vulnerability analysis of drones. However, most security research to mitigate threats to IoT devices has focused primarily on networks, firmware and mobile applications. Of these, the use of fuzzing to analyse the security of firmware requires emulation of the firmware. However, when it comes to drone firmware, the industry lacks emulation and automated fuzzing tools. This is largely due to challenges such as limited input interfaces, firmware encryption and signatures. While it may be tempting to assume that existing emulators and automated analysers for IoT devices can be applied to drones, practical applications have proven otherwise. In this paper, we discuss the challenges of dynamically analysing drone firmware and propose potential solutions. In addition, we demonstrate the effectiveness of our methodology by applying it to DJI drones, which have the largest market share.

An efficient characterization of scientifically significant locations is essential prior to the return of humans to the Moon. The highest resolution imagery acquired from orbit of south-polar shadowed regions and other relevant locations remains, at best, an order of magnitude larger than the characteristic length of most of the robotic systems to be deployed. This hinders the planning and successful implementation of prospecting missions and poses a high risk for the traverse of robots and humans, diminishing the potential overall scientific and commercial return of any mission. We herein present the design of a lightweight, compact, autonomous, and reusable lunar reconnaissance drone capable of assisting other ground-based robotic assets, and eventually humans, in the characterization and high-resolution mapping (~0.1 m/px) of particularly challenging and hard-to-access locations on the lunar surface. The proposed concept consists of two main subsystems: the drone and its service station. With a total combined wet mass of 100 kg, the system is capable of 11 flights without refueling the service station, enabling almost 9 km of accumulated flight distance. The deployment of such a system could significantly impact the efficiency of upcoming exploration missions, increasing the distance covered per day of exploration and significantly reducing the need for recurrent contacts with ground stations on Earth.

The majority of Axon's AI ethics board have resigned after the company announced that it's developing taser-equipped drones. In response to yet another shooting in a US school, Axon founder and CEO Rick Smith began thinking about how the company could help put a stop to the all too regular occurrence. The shooting kicked off the usual debate over whether stricter gun laws are needed. Unfortunately, we all know nothing is likely to really change and we'll be back to rehashing the same arguments the next time more children lose their lives. "In the aftermath of these events, we get stuck in fruitless debates. We need new and better solutions," Smith said in a statement.

Dutch cress grower Rob Baan has enlisted high-tech helpers to tackle a pest in his greenhouses: palm-sized drones seek and destroy moths that produce caterpillars that can chew up his crops. "I have unique products where you don't get certification to spray chemicals and I don't want it," Baan said in an interview in a greenhouse bathed in the pink glow of LED lights that help his seedlings grow. His company, Koppert Cress, exports aromatic seedlings, plants and flowers to top-end restaurants around the world. A keen adopter of innovative technology in his greenhouses, Baan turned to PATS Indoor Drone Solutions, a startup that is developing autonomous drone systems as greenhouse sentinels, to add another layer of protection for his plants. The drones themselves are basic, but they are steered by smart technology aided by special cameras that scan the airspace in greenhouses.

A group of scientists in Canada have announced a new initiative to use drones to plant new trees cheaply and quickly in part of an effort to fight against the negative effects of climate change and deforestation. Called Flash Forest, the team began testing its drone delivery systems in August, with a test flight that saw them successfully plant 100 trees with a drone. Those results were so encouraging they've expanded their goals to use their specially designed drone systems to plant a billion new trees by 2028. 'Every year the planet loses 13 billion trees and regains less than half of that,' the team's Bryce Jones said in a fundraising video announcing the project. 'We started Flash Forest with the goal of healing the planet's lungs and taking that job seriously.

Unmanned drones equipped with thermal imaging cameras could save dolphins, according to a new scientific study out of NZ and the UK. According to the study, the endangered Maui and Hector's dolphins in New Zealand and other marine mammal species around the world could be saved from caught up accidentally when fishing for other species. The successful study proved aerial thermal detection and identification of Maui and Hector's dolphins, and other marine mammals would be possible from both manned and unmanned aircraft and could be used on drones. Martin Stanley from Ocean Life Survey, who led the study, has designed and developed an unmanned remotely operated thermal imaging drone system that can be used for marine mammal study and protection. The thermal drone system can be operated from vessels such as fishing boats to provide real-time detection of and protection to marine mammals.

Drone-based surveillance still makes many people uncomfortable, but that isn't stopping research into more effective airborne watchdogs. Scientists have developed an experimental drone system that uses AI to detect violent actions in crowds. The team trained their machine learning algorithm to recognize a handful of typical violent motions (punching, kicking, shooting and stabbing) and flag them when they appear in a drone's camera view. The technology could theoretically detect a brawl that on-the-ground officers might miss, or pinpoint the source of a gunshot. As The Verge warned, the technology definitely isn't ready for real-world use.