Mobile service robots have become increasingly ubiquitous. However, these robots can pose potential accessibility issues and safety concerns to people with visual impairments (PVI). We sought to explore the challenges faced by PVI around mainstream mobile service robots and identify their needs. Seventeen PVI were interviewed about their experiences with three emerging robots: vacuum robots, delivery robots, and drones. We comprehensively investigated PVI's robot experiences by considering their different roles around robots -- direct users and bystanders. Our study highlighted participants' challenges and concerns about the accessibility, safety, and privacy issues around mobile service robots. We found that the lack of accessible feedback made it difficult for PVI to precisely control, locate, and track the status of the robots. Moreover, encountering mobile robots as bystanders confused and even scared the participants, presenting safety and privacy barriers. We further distilled design considerations for more accessible and safe robots for PVI.

Systems for relative localization in multi-robot systems based on ultra-wideband (UWB) ranging have recently emerged as robust solutions for GNSS-denied environments. Scalability remains one of the key challenges, particularly in ad-hoc deployments. Recent solutions include dynamic allocation of active and passive localization modes for different robots or nodes in the system. With larger-scale systems becoming more distributed, key research questions arise in the areas of security and trustability of such localization systems. This paper studies the potential integration of collaborative-decision making processes with distributed ledger technologies. Specifically, we investigate the design and implementation of a methodology for running an UWB role allocation algorithm within smart contracts in a blockchain. In previous works, we have separately studied the integration of ROS2 with the Hyperledger Fabric blockchain, and introduced a new algorithm for scalable UWB-based localization. In this paper, we extend these works by (i) running experiments with larger number of mobile robots switching between different spatial configurations and (ii) integrating the dynamic UWB role allocation algorithm into Fabric smart contracts for distributed decision-making in a system of multiple mobile robots. This enables us to deliver the same functionality within a secure and trustable process, with enhanced identity and data access management. Our results show the effectiveness of the UWB role allocation for continuously varying spatial formations of six autonomous mobile robots, while demonstrating a low impact on latency and computational resources of adding the blockchain layer that does not affect the localization process.

The Taliban's acting defence minister has said Pakistan allowed American drones to use its airspace to access Afghanistan, a charge Pakistan has recently denied following a US air strike in Kabul. Acting Minister of Defence Mullah Mohammad Yaqoob told reporters at a news conference in Kabul on Sunday that American drones have been entering Afghanistan via Pakistan. "According to our information the drones are entering through Pakistan to Afghanistan, they use Pakistan's airspace, we ask Pakistan, don't use your airspace against us," he said. Pakistan's foreign ministry did not immediately respond to a request for comment. Pakistani authorities have denied involvement in or advanced knowledge of a drone strike the United States said it carried out in Kabul in July that killed al-Qaeda leader Ayman al-Zawahiri.

We believe every farmer should have the ability to perform their own full-field aerial application. The Guardian SC1 puts that capability in every barn. With 100% repeatable and transparent coverage, our eVTOL vehicles do not compact soil, damage crops or spread disease. Fully electrically and digitally controlled, our systems are safer for operators and the environment. Application is kept on target, and actionable reporting helps you refine chemical use.

Unmanned aerial vehicles (UAVs) are being employed in many areas such as photography, emergency, entertainment, defence, agriculture, forestry, mining and construction. Over the last decade, UAV technology has found applications in numerous construction project phases, ranging from site mapping, progress monitoring, building inspection, damage assessments, and material delivery. While extensive studies have been conducted on the advantages of UAVs for various construction-related processes, studies on UAV collaboration to improve the task capacity and efficiency are still scarce. This paper proposes a new cooperative path planning algorithm for multiple UAVs based on the stag hunt game and particle swarm optimization (PSO). First, a cost function for each UAV is defined, incorporating multiple objectives and constraints. The UAV game framework is then developed to formulate the multi-UAV path planning into the problem of finding payoff-dominant equilibrium. Next, a PSO-based algorithm is proposed to obtain optimal paths for the UAVs. Simulation results for a large construction site inspected by three UAVs indicate the effectiveness of the proposed algorithm in generating feasible and efficient flight paths for UAV formation during the inspection task.

Following dozens of drone sightings -- and a handful of reports of a "guy in a jetpack" -- near Los Angeles International Airport, the Transportation Security Administration will test new technology to spot, track and identify drones in restricted airspace. The federally funded program would make LAX only the second airport using the "state-of-the-art technology," which is also being tested at Miami International Airport, with plans to expand across the country, according to the TSA's announcement Thursday. There have been 38 drone sightings this year at LAX, including one within 700 feet of an aircraft, according to the TSA. Since last year, there have been 90 visual sightings and 5,200 technical detections of drones within three miles of LAX. "While there are many beneficial uses for drones in our society, it is becoming far too common that drones are sighted near airports, which presents significant security risks and unnecessary disruptions to the traveling public," Rep. Lucille Roybal-Allard (D-Downey) said in a statement.

Nano-size drones hold enormous potential to explore unknown and complex environments. Their small size makes them agile and safe for operation close to humans and allows them to navigate through narrow spaces. However, their tiny size and payload restrict the possibilities for on-board computation and sensing, making fully autonomous flight extremely challenging. The first step towards full autonomy is reliable obstacle avoidance, which has proven to be technically challenging by itself in a generic indoor environment. Current approaches utilize vision-based or 1-dimensional sensors to support nano-drone perception algorithms. This work presents a lightweight obstacle avoidance system based on a novel millimeter form factor 64 pixels multi-zone Time-of-Flight (ToF) sensor and a generalized model-free control policy. Reported in-field tests are based on the Crazyflie 2.1, extended by a custom multi-zone ToF deck, featuring a total flight mass of 35g. The algorithm only uses 0.3% of the on-board processing power (210uS execution time) with a frame rate of 15fps, providing an excellent foundation for many future applications. Less than 10% of the total drone power is needed to operate the proposed perception system, including both lifting and operating the sensor. The presented autonomous nano-size drone reaches 100% reliability at 0.5m/s in a generic and previously unexplored indoor environment. The proposed system is released open-source with an extensive dataset including ToF and gray-scale camera data, coupled with UAV position ground truth from motion capture.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The Taliban says it has not been able to find the body of al Qaeda terrorist leader Ayman al Zawahiri after a U.S. air strike hit the home he was staying at in Afghanistan. The U.S. said it killed al Zawahiri with a drone strike in the Afghan capital of Kabul in July. The al Qaeda leader was standing on the balcony of a home owned by an aide to Sirajuddin Haqqani, top deputy of the Taliban's supreme leader Mullah Haibatallah Akhundzada.

There is an increasing demand for using Unmanned Aerial Vehicle (UAV), known as drones, in different applications such as packages delivery, traffic monitoring, search and rescue operations, and military combat engagements. In all of these applications, the UAV is used to navigate the environment autonomously - without human interaction, perform specific tasks and avoid obstacles. Autonomous UAV navigation is commonly accomplished using Reinforcement Learning (RL), where agents act as experts in a domain to navigate the environment while avoiding obstacles. Understanding the navigation environment and algorithmic limitations plays an essential role in choosing the appropriate RL algorithm to solve the navigation problem effectively. Consequently, this study first identifies the main UAV navigation tasks and discusses navigation frameworks and simulation software. Next, RL algorithms are classified and discussed based on the environment, algorithm characteristics, abilities, and applications in different UAV navigation problems, which will help the practitioners and researchers select the appropriate RL algorithms for their UAV navigation use cases. Moreover, identified gaps and opportunities will drive UAV navigation research.

We present a novel application of Goal-Directed Answer Set Programming that digitizes the model aircraft operator's compliance verification against the Academy of Model Aircrafts (AMA) safety code. The AMA safety code regulates how AMA flyers operate Unmanned Aerial Vehicles (UAVs) for limited recreational purposes. Flying drones and their operators are subject to various rules before and after the operation of the aircraft to ensure safe flights. In this paper, we leverage Answer Set Programming to encode the AMA safety code and automate compliance checks. To check compliance, we use the s(CASP) which is a goal-directed ASP engine. By using s(CASP) the operators can easily check for violations and obtain a justification tree explaining the cause of the violations in human-readable natural language. Further, we implement an algorithm to help the operators obtain the minimal set of conditions that need to be satisfied in order to pass the compliance check. We develop a front-end questionnaire interface that accepts various conditions and use the backend s(CASP) engine to evaluate whether the conditions adhere to the regulations. We also leverage s(CASP) implemented in SWI-Prolog, where SWI-Prolog exposes the reasoning capabilities of s(CASP) as a REST service. To the best of our knowledge, this is the first application of ASP in the AMA and Avionics Compliance and Certification space.