Amid a serious truck driver shortage, convenience store chain Seven-Eleven Japan began a trial delivery service using robots on public roads in a western Tokyo suburb on Monday. In the experimental project involving two stores in the city of Hachioji, two robots at each outlet carry items ordered through the 7NOW delivery service app. The four-wheeled box-type robots, which can travel up to 6 kph, are designed to run on sidewalks while following traffic lights and dodging obstacles. After conducting the tests until February next year, Seven-Eleven Japan will consider the feasibility of the robot delivery service, which is expected to help the company cope with a driver shortage and better serve older customers who have difficulty going out shopping.

--Ensuring robust and real-time obstacle avoidance is critical for the safe operation of autonomous robots in dynamic, real-world environments. This paper proposes a neural network framework for predicting the time and collision position of an unmanned aerial vehicle with a dynamic object, using RGB and event-based vision sensors. The proposed architecture consists of two separate encoder branches, one for each modality, followed by fusion by self-attention to improve prediction accuracy. T o facilitate benchmarking, we leverage the ABCD [8] dataset collected that enables detailed comparisons of single-modality and fusion-based approaches. At the same prediction throughput of 50Hz, the experimental results show that the fusion-based model offers an improvement in prediction accuracy over single-modality approaches of 1% on average and 10% for distances beyond 0.5m, but comes at the cost of +71% in memory and + 105% in FLOPs. Notably, the event-based model outperforms the RGB model by 4% for position and 26% for time error at a similar computational cost, making it a competitive alternative. Additionally, we evaluate quantized versions of the event-based models, applying 1-to 8-bit quantization to assess the trade-offs between predictive performance and computational efficiency.

--Wildlife-induced crop damage, particularly from deer, threatens agricultural productivity. Traditional deterrence methods often fall short in scalability, responsiveness, and adaptability to diverse farmland environments. This paper presents an integrated unmanned aerial vehicle (UA V) system designed for autonomous wildlife deterrence, developed as part of the Farm Robotics Challenge. Our system combines a YOLO-based real-time computer vision module for deer detection, an energy-efficient coverage path planning algorithm for efficient field monitoring, and an autonomous charging station for continuous operation of the UA V . In collaboration with a local Minnesota farmer, the system is tailored to address practical constraints such as terrain, infrastructure limitations, and animal behavior . The solution is evaluated through a combination of simulation and field testing, demonstrating robust detection accuracy, efficient coverage, and extended operational time. Crop damage caused by wildlife, particularly deer incursions, represents a challenge for modern agriculture. Deer damage to crops is responsible for disagreements among farmers, hunters, and the Department of Natural Resources over how the deer population should be controlled [1].

Ukrainian officials said Saturday night's strikes showed Russia had no intention of stopping the war, despite international pressure for a ceasefire. "For Russia, the negotiations [on Friday] in Istanbul are just a pretence. Putin wants war," said Andriy Yermak, a top aide to the Ukrainian president. Following the talks in Turkey, Trump had suggested there would be no progress towards peace until he and Putin meet face-to-face. The US president has proposed a 30-day ceasefire agreement and threatened tougher sanctions if Russia doesn't comply.

British Prime Minister Keir Starmer has discussed the war in Ukraine with leaders of the United States, Italy, France and Germany, a 10 Downing Street spokesperson has said, in advance of US President Donald Trump's planned call with his Russian counterpart, Vladimir Putin, on Monday. The flurry of diplomacy comes shortly after inconclusive direct Russia-Ukraine talks in Istanbul, Turkiye on Friday. The leaders discussed the need for an unconditional ceasefire and for Putin to take peace talks seriously, the spokesperson said late on Sunday, adding that they also raised the use of sanctions if Russia failed to engage seriously in a ceasefire and concerted peace talks. In remarks to reporters earlier on Sunday, German Chancellor Friedrich Merz said he discussed the issue with US Secretary of State Marco Rubio while the two men were attending the inaugural mass of Pope Leo XIV at the Vatican. Merz said he also spoke at length at the Vatican with Ukraine's President Volodymyr Zelenskyy.

The fields of autonomous systems and robotics are receiving considerable attention in civil applications such as construction, logistics, and firefighting. Nevertheless, the widespread adoption of these technologies is hindered by the necessity for robust processing units to run AI models. Edge-AI solutions offer considerable promise, enabling low-power, cost-effective robotics that can automate civil services, improve safety, and enhance sustainability. This paper presents a novel Edge-AI-enabled drone-based surveillance system for autonomous multi-robot operations at construction sites. Our system integrates a lightweight MCU-based object detection model within a custom-built UAV platform and a 5G-enabled multi-agent coordination infrastructure. We specifically target the real-time obstacle detection and dynamic path planning problem in construction environments, providing a comprehensive dataset specifically created for MCU-based edge applications. Field experiments demonstrate practical viability and identify optimal operational parameters, highlighting our approach's scalability and computational efficiency advantages compared to existing UAV solutions. The present and future roles of autonomous vehicles on construction sites are also discussed, as well as the effectiveness of edge-AI solutions. We share our dataset publicly at github.com/egirgin/storaige-b950

FIRST ON FOX: Dozens of drones that traipsed over Langley Air Force base in late 2023 revealed an astonishing oversight: Military officials did not believe they had the authority to shoot down the unmanned vehicles over the U.S. homeland. A new bipartisan bill, known as the COUNTER Act, seeks to rectify that, offering more bases the opportunity to become a "covered facility," or one that has the authority to shoot down drones that encroach on their airspace. The new bill has broad bipartisan and bicameral support, giving it a greater chance of becoming law. It's led by Armed Services Committee members Tom Cotton, R-Ark., and Kirsten Gillibrand, D-N.Y., in the Senate, and companion legislation is being introduced by August Pfluger, R-Texas, and Chrissy Houlahan, D-Pa., in the House. Currently, only half of the 360 domestic U.S. bases are considered "covered facilities" that are allowed to engage with unidentified drones.

As autonomous robotic systems become increasingly mature, users will want to specify missions at the level of intent rather than in low-level detail. Language is an expressive and intuitive medium for such mission specification. However, realizing language-guided robotic teams requires overcoming significant technical hurdles. Interpreting and realizing language-specified missions requires advanced semantic reasoning. Successful heterogeneous robots must effectively coordinate actions and share information across varying viewpoints. Additionally, communication between robots is typically intermittent, necessitating robust strategies that leverage communication opportunities to maintain coordination and achieve mission objectives. In this work, we present a first-of-its-kind system where an unmanned aerial vehicle (UAV) and an unmanned ground vehicle (UGV) are able to collaboratively accomplish missions specified in natural language while reacting to changes in specification on the fly. We leverage a Large Language Model (LLM)-enabled planner to reason over semantic-metric maps that are built online and opportunistically shared between an aerial and a ground robot. We consider task-driven navigation in urban and rural areas. Our system must infer mission-relevant semantics and actively acquire information via semantic mapping. In both ground and air-ground teaming experiments, we demonstrate our system on seven different natural-language specifications at up to kilometer-scale navigation.

Formation control is essential for swarm robotics, enabling coordinated behavior in complex environments. In this paper, we introduce a novel formation control system for an indoor blimp swarm using a specialized leader-follower approach enhanced with a dynamic leader-switching mechanism. This strategy allows any blimp to take on the leader role, distributing maneuvering demands across the swarm and enhancing overall formation stability. Only the leader blimp is manually controlled by a human operator, while follower blimps use onboard monocular cameras and a laser altimeter for relative position and altitude estimation. A leader-switching scheme is proposed to assist the human operator to maintain stability of the swarm, especially when a sharp turn is performed. Experimental results confirm that the leader-switching mechanism effectively maintains stable formations and adapts to dynamic indoor environments while assisting human operator.

The RSF is trying to show that they don't need to reach Port Sudan by land in order to be able to have an impact there,