For enabling efficient, large-scale coordination of unmanned aerial vehicles (UAVs) under the labeled setting, in this work, we develop the first polynomial time algorithm for the reconfiguration of many moving bodies in three-dimensional spaces, with provable $1.x$ asymptotic makespan optimality guarantee under high robot density. More precisely, on an $m_1\times m_2 \times m_3$ grid, $m_1\ge m_2\ge m_3$, our method computes solutions for routing up to $\frac{m_1m_2m_3}{3}$ uniquely labeled robots with uniformly randomly distributed start and goal configurations within a makespan of $m_1 + 2m_2 +2m_3+o(m_1)$, with high probability. Because the makespan lower bound for such instances is $m_1 + m_2+m_3 - o(m_1)$, also with high probability, as $m_1 \to \infty$, $\frac{m_1+2m_2+2m_3}{m_1+m_2+m_3}$ optimality guarantee is achieved. $\frac{m_1+2m_2+2m_3}{m_1+m_2+m_3} \in (1, \frac{5}{3}]$, yielding $1.x$ optimality. In contrast, it is well-known that multi-robot path planning is NP-hard to optimally solve. In numerical evaluations, our method readily scales to support the motion planning of over $100,000$ robots in 3D while simultaneously achieving $1.x$ optimality. We demonstrate the application of our method in coordinating many quadcopters in both simulation and hardware experiments.

Domestic giant Tata Steel on Wednesday said it has inked a pact with a Bengaluru-based startup for drone-based mining solutions for effective mine management. The primary goal of this collaboration is to jointly develop and offer sustainable and end-to-end integrated solutions that will focus on efficiency, safety, and productivity of open cast mining operations. "Tata Steel has signed a Memorandum of Understanding with Aarav Unmanned Systems, a Bangaluru-based startup, providing end-to-end drone solutions... for effective mine management," the company said in a statement. Tata Steel will also work jointly with AUS to provide exclusive drone-based solutions, including mine analytics and geo-technical mapping, to Tata Steel group companies across mining locations in India, it said. On the partnership, D B Sundara Ramam, Vice President, Raw Materials, Tata Steel, said: "Drone survey enabled digitalisation and other technology will assist in gathering impactful and actionable insights. We see enormous potential in redefining core mining processes such as exploration and mine planning using drone data and adequate analytics."

The Ukrainian Army on Tuesday announced it had destroyed three more tanks belonging to the Russian army using only attack drones. The Security Service of Ukraine (SSU) said their officers successfully destroyed three T-72s, Russian battle tanks commonly used by the Soviet Army in the 1970s. The SSU did not specify when and where the attack happened. It is also unclear if there were any casualties. The SSU's report on the recent attack comes just days after it released an audio of an intercepted phone call where a Russian soldier, whose identity was not revealed, told his mother that Ukrainian soldiers who attacked their unit destroyed their tanks and killed more than a dozen comrades.

China's biggest tech hub is rushing to stamp out a fresh Covid outbreak, ordering some of the country's biggest manufacturers to operate in a'closed loop' to reduce infections, state media reported. The city of Shenzhen, which borders Hong Kong, reported just 19 Covid cases Tuesday as the city's health authority said the risk of "large-scale spread is low". But Beijing's reluctance to budge from its strict zero-Covid policy had led to daily mass testing for the 13 million residents of Shenzhen for over a week and the closure of at least three subway stations by Tuesday. Top manufacturers including iPhone maker Foxconn, electric carmaker BYD, drone maker DJI and telecom equipment maker ZTE are among the companies told to operate under a "closed-loop" production system. It would restrict movement of employees for seven days, state-run business news site Yicai reported Monday.

Amid Taiwan's annual Han Kuang war games, a Chinese Unmanned Aerial Vehicle (UAV) was spotted off the island's east coast Monday. The UAV, a reconnaissance and strike vehicle, is dubbed the "Twin-Tailed Scorpion." The "Twin-Tailed Scorpion" reportedly flew from the East China Sea area through the Miyako Strait between the Japanese islands of Okinawa and Miyakojima, a statement issued by the Japanese Defense Ministry said. After entering the Pacific Ocean, the UAV turned southwest and flew past the Sakishima Islands. The UAV was then seen heading northwest toward the Bashi Channel and northeastern Taiwan, its last reported position was off the coast of Taiwan's Hualien County, deep inside the eastern sector of Taiwan's ADIZ, reported Taiwan News.

China has for the first time sent a TB001 combat and reconnaissance drone on a solo mission through Okinawa Prefecture's Miyako Strait, traveling from the East China Sea into the Pacific near Taiwan, according to the Japanese Defense Ministry. The flight of the drone, which has a maximum range of 6,000 kilometers and can carry missiles and precision guided bombs, occurred Monday from the morning through the afternoon. The Defense Ministry said it scrambled fighter jets to monitor the drone, which did not violate the country's territorial airspace. This could be due to a conflict with your ad-blocking or security software. Please add japantimes.co.jp and piano.io to your list of allowed sites.

Although Simultaneous Localization and Mapping (SLAM) has been an active research topic for decades, current state-of-the-art methods still suffer from instability or inaccuracy due to feature insufficiency or its inherent estimation drift, in many civilian environments. To resolve these issues, we propose a navigation system combing the SLAM and prior-map-based localization. Specifically, we consider additional integration of line and plane features, which are ubiquitous and more structurally salient in civilian environments, into the SLAM to ensure feature sufficiency and localization robustness. More importantly, we incorporate general prior map information into the SLAM to restrain its drift and improve the accuracy. To avoid rigorous association between prior information and local observations, we parameterize the prior knowledge as low dimensional structural priors defined as relative distances/angles between different geometric primitives. The localization is formulated as a graph-based optimization problem that contains sliding-window-based variables and factors, including IMU, heterogeneous features, and structure priors. We also derive the analytical expressions of Jacobians of different factors to avoid the automatic differentiation overhead. To further alleviate the computation burden of incorporating structural prior factors, a selection mechanism is adopted based on the so-called information gain to incorporate only the most effective structure priors in the graph optimization. Finally, the proposed framework is extensively tested on synthetic data, public datasets, and, more importantly, on the real UAV flight data obtained from a building inspection task. The results show that the proposed scheme can effectively improve the accuracy and robustness of localization for autonomous robots in civilian applications.

Abstract--This paper proposes a novel control law for accurate tracking of agile trajectories using a tailsitter flying wing unmanned aerial vehicle (UAV) that transitions between vertical take-off and landing (VTOL) and forward flight. The global control formulation enables maneuvering throughout the flight envelope, including uncoordinated flight with sideslip. Differential flatness of the nonlinear tailsitter dynamics with a simplified aerodynamics model is shown. Using the flatness transform, the proposed controller incorporates tracking of the position reference along with its derivatives velocity, acceleration and jerk, as well as the yaw reference and yaw rate. Specifically, the lack of vertical surfaces enables maneuvers such as fast skidding turns and Transitioning powered-lift aircraft combine the vertical takeoff knife edge flight where the wing points in the direction of and landing (VTOL) and hover capability of rotorcraft travel. In general, it permits uncoordinated flight, where the with the increased speed, range, and endurance of fixedwing vehicle incurs nonzero lateral velocity. Tailsitter aircraft pitch down during transition, so that their rotors naturally shift from lift generation for In this paper, we propose a novel flight control algorithm take-off to propulsion for forward flight. While the large that is specifically designed for tracking of agile trajectories attitude envelope of tailsitters may render them less suitable using the tailsitter flying wing aircraft shown in Figure 1. for manned flight, their relative mechanical simplicity The proposed controller uses differential flatness to track the makes them an appealing option for unmanned aerial vehicle reference position, velocity, acceleration, and jerk (the third (UAV) applications. Increased range and endurance with the derivative of position), as well as yaw angle and yaw rate.

Hear from top leaders discuss topics surrounding AL/ML technology, conversational AI, IVA, NLP, Edge, and more. Drones have been talked about extensively for two decades now. In many respects, that attention has been warranted. Military drones have changed the way we fight wars. Consumer drones have changed the way we film the world.

The usage of industrial drones to conduct inspections can considerably enhance enterprise operations throughout industries. These inspections improve precision, present safer choices for the workforce and drive effectivity. In response to Quadintel, the worldwide drone inspection and monitoring market was $7.47 billion in 2021 and can develop to $35.15 billion by 2030. This weblog examines 5 industries that profit from the fast-growing expertise of business drone utilization. Infrastructure is vital for a society and an economic system, however a number of the world's most industrialized nations face crumbling infrastructure, particularly ageing bridges.