Ukraine's forces advanced on the southern and eastern fronts during the 79th week of the country's war with Russia, reaching what experts believe to be the rear of Russia's first line of defence. But they also faced a Russian advance in the northeast that created a dilemma about where to field available troops. Ukrainian forces driving towards occupied Melitopol confirmed their recapture of Robotyne, an important Russian stronghold in western Zaporizhia, which they entered on August 20. Southern forces spokesman Oleksandr Shtupun said on August 28 that forces were busy demining the area, securing their positions and winning more territory southeast of the settlement. Geolocated footage placed them 5km (3.1 miles) southeast of Robotyne.

An artificial intelligence has consistently beaten champion drone pilots in races for the first time, achieving lap times no human was able to match. The technology could be used to speed up drones carrying out everyday tasks. The sport of drone racing involves humans piloting small quadcopters around a course at speeds of more than 100 kilometres per hour, with the vehicles subject to g-forces of up to 5 g.

Having trounced humans at everything from chess and Go, to StarCraft and Gran Turismo, artificial intelligence (AI) has raised its game and laid waste world champions at a physical sport. The latest mortals to feel the sting of AI-induced defeat are three expert drone racers who were beaten by an algorithm that learned to fly a drone around a 3D race course at breakneck speeds without crashing. Developed by researchers at the University of Zurich, the Swift AI won 15 out of 25 races against world champions and clocked the fastest lap on a course where drones reach speeds of 50mph (80km/h) and endure accelerations up to 5g, enough to make many people black out. "Our result marks the first time that a robot powered by AI has beaten a human champion in a real physical sport designed for and by humans," said Elia Kaufmann, a researcher who helped to develop Swift. First-person view drone racing involves flying a drone around a course dotted with gates that must be passed through cleanly to avoid a crash.

Ukraine bade farewell to legendary fighter pilot Andriy Pilshchykov, known by his call sign "Juice", who was killed with two other pilots during a training flight last week. A Ukrainian flag was draped over 29-year-old Pilshchykov's coffin and his cap placed on top. Russian forces shot down two Ukrainian drones over the Black Sea, the Russian state RIA news agency reported citing the Ministry of Defence. The mission of Ukraine's president in Russian-occupied Crimea said that Moscow was preparing to start a new round of mobilisation for the Russian army in the territory. The United Kingdom's defence ministry said Russia had boosted salaries and benefits for its soldiers making military service "increasingly lucrative".

Russian transport aircraft have been reported damaged in a drone attack on an airport in Russia's western city of Pskov – located near the borders of Latvia and Estonia – where explosions, a large blaze and gunfire were reported, a local official and state media said. Russia's state-run TASS news agency, quoting emergency services, said early on Wednesday morning that four Il-76 heavy transport aircraft, which have long been the workhorse of the Russian military, were damaged at the airfield in Pskov, located roughly 800km (some 500 miles) from the border with Ukraine. "The defence ministry is repelling a drone attack in Pskov's airport," the regional Governor Mikhail Vedernikov said on the Telegram messaging app, posting a video of a large fire, with sounds of explosions and sirens in the background. Vedernikov, who was at the scene of the attack, said that "according to preliminary information, there are no victims". The scale of the damage to the airport was being assessed, he said.

Safe corridor-based Trajectory Optimization (TO) presents an appealing approach for collision-free path planning of autonomous robots, offering global optimality through its convex formulation. The safe corridor is constructed based on the perceived map, however, the non-ideal perception induces uncertainty, which is rarely considered in trajectory generation. In this paper, we propose Distributionally Robust Safe Corridor Constraints (DRSCCs) to consider the uncertainty of the safe corridor. Then, we integrate DRSCCs into the trajectory optimization framework using Bernstein basis polynomials. Theoretically, we rigorously prove that the trajectory optimization problem incorporating DRSCCs is equivalent to a computationally efficient, convex quadratic program. Compared to the nominal TO, our method enhances navigation safety by significantly reducing the infeasible motions in presence of uncertainty. Moreover, the proposed approach is validated through two robotic applications, a micro Unmanned Aerial Vehicle (UAV) and a quadruped robot Unitree A1.

Although Ukraine has not said it carried out specific drone strikes, President Volodymyr Zelensky has previously said that attacks on Russian territory are an "inevitable, natural and absolutely fair process" as the war with Russia continues.

Russian air defences shot down three Ukrainian drones flying over the Russian regions of Tula and Belgorod, the Ministry of Defence has reported, in the latest attempted attacks on targets inside Russian territory. Two drones "were destroyed" by air defences over the Tula region south of Moscow, the defence ministry said in a statement on the Telegram messaging app early on Tuesday morning. Another drone was "destroyed by air defence forces" over the Belgorod region, which borders Ukraine, at about 11pm Moscow time (20:00 GMT) on Monday, the ministry said in a separate statement. The ministry did not say whether there had been damage or casualties as a result of the drone raids. Moscow and other Russian regions have been hit by a barrage of Ukrainian drone attacks in recent weeks with Ukrainian President Volodymyr Zelenskyy saying late last month that the war would be returning to Russia.

Neural rendering has garnered substantial attention owing to its capacity for creating realistic 3D scenes. However, its applicability to extensive scenes remains challenging, with limitations in effectiveness. In this work, we propose the Drone-NeRF framework to enhance the efficient reconstruction of unbounded large-scale scenes suited for drone oblique photography using Neural Radiance Fields (NeRF). Our approach involves dividing the scene into uniform sub-blocks based on camera position and depth visibility. Sub-scenes are trained in parallel using NeRF, then merged for a complete scene. We refine the model by optimizing camera poses and guiding NeRF with a uniform sampler. Integrating chosen samples enhances accuracy. A hash-coded fusion MLP accelerates density representation, yielding RGB and Depth outputs. Our framework accounts for sub-scene constraints, reduces parallel-training noise, handles shadow occlusion, and merges sub-regions for a polished rendering result. This Drone-NeRF framework demonstrates promising capabilities in addressing challenges related to scene complexity, rendering efficiency, and accuracy in drone-obtained imagery.

This paper proposes a multi-object tracking (MOT) algorithm for traffic monitoring using a drone equipped with optical and thermal cameras. Object detections on the images are obtained using a neural network for each type of camera. The cameras are modelled as direction-of-arrival (DOA) sensors. Each DOA detection follows a von-Mises Fisher distribution, whose mean direction is obtain by projecting a vehicle position on the ground to the camera. We then use the trajectory Poisson multi-Bernoulli mixture filter (TPMBM), which is a Bayesian MOT algorithm, to optimally estimate the set of vehicle trajectories. We have also developed a parameter estimation algorithm for the measurement model. We have tested the accuracy of the resulting TPMBM filter in synthetic and experimental data sets.