Iran has blamed Israel for last week's drone attack on a military factory near the central city of Isfahan, promising revenge for what appeared to be the latest episode in a long-running covert war. The Iranian claim, carried by the semi-official ISNA news agency on Thursday, corroborates remarks made by United States officials following the attack. The attack came amid tension between Iran and the West over Tehran's nuclear activity and its supply of arms – including long-range "suicide drones" – for Russia's war in Ukraine, as well as months of anti-government demonstrations at home. In a letter to the United Nations chief, Iran's UN envoy, Amir Saeid Iravani, said "primary investigation suggested Israel was responsible" for Saturday night's attack, which Tehran had said caused no casualties or serious damage. "Iran reserves its legitimate and inherent right to defend its national security and firmly respond to any threat or wrongdoing of the Zionist regime [Israel] wherever and whenever it deems necessary," Iravani said in the letter.

This paper concerns fault-tolerant power transmission line inspection planning as a generalization of the multiple traveling salesmen problem. The addressed inspection planning problem is formulated as a single-depot multiple-vehicle scenario, where the inspection vehicles are constrained by the battery budget limiting their inspection time. The inspection vehicle is assumed to be an autonomous multi-copter with a wide range of possible flight speeds influencing battery consumption. The inspection plan is represented by multiple routes for vehicles providing full coverage over inspection target power lines. On an inspection vehicle mission interruption, which might happen at any time during the execution of the inspection plan, the inspection is re-planned using the remaining vehicles and their remaining battery budgets. Robustness is introduced by choosing a suitable cost function for the initial plan that maximizes the time window for successful re-planning. It enables the remaining vehicles to successfully finish all the inspection targets using their respective remaining battery budgets. A combinatorial metaheuristic algorithm with various cost functions is used for planning and fast re-planning during the inspection.

This letter concerns optimal power transmission line inspection formulated as a proposed generalization of the traveling salesman problem for a multi-route one-depot scenario. The problem is formulated for an inspection vehicle with a limited travel budget. Therefore, the solution can be composed of multiple runs to provide full coverage of the given power lines. Besides, the solution indicates how many vehicles can perform the inspection in a single run. The optimal solution of the problem is solved by the proposed Integer Linear Programming (ILP) formulation, which is, however, very computationally demanding. Therefore, the computational requirements are addressed by the combinatorial metaheuristic. The employed greedy randomized adaptive search procedure is significantly less demanding while providing competitive solutions and scales better with the problem size than the ILP-based approach. The proposed formulation and algorithms are demonstrated in a real-world scenario to inspect power line segments at the electrical substation.

This paper presents a comprehensive review of ground agricultural robotic systems and applications with special focus on harvesting that span research and commercial products and results, as well as their enabling technologies. The majority of literature concerns the development of crop detection, field navigation via vision and their related challenges. Health monitoring, yield estimation, water status inspection, seed planting and weed removal are frequently encountered tasks. Regarding robotic harvesting, apples, strawberries, tomatoes and sweet peppers are mainly the crops considered in publications, research projects and commercial products. The reported harvesting agricultural robotic solutions, typically consist of a mobile platform, a single robotic arm/manipulator and various navigation/vision systems. This paper reviews reported development of specific functionalities and hardware, typically required by an operating agricultural robot harvester; they include (a) vision systems, (b) motion planning/navigation methodologies (for the robotic platform and/or arm), (c) Human-Robot-Interaction (HRI) strategies with 3D visualization, (d) system operation planning & grasping strategies and (e) robotic end-effector/gripper design. Clearly, automated agriculture and specifically autonomous harvesting via robotic systems is a research area that remains wide open, offering several challenges where new contributions can be made.

Nano-sized drones, with palm-sized form factor, are gaining relevance in the Internet-of-Things ecosystem. Achieving a high degree of autonomy for complex multi-objective missions (e.g., safe flight, exploration, object detection) is extremely challenging for the onboard chip-set due to tight size, payload (<10g), and power envelope constraints, which strictly limit both memory and computation. Our work addresses this complex problem by combining bio-inspired navigation policies, which rely on time-of-flight distance sensor data, with a vision-based convolutional neural network (CNN) for object detection. Our field-proven nano-drone is equipped with two microcontroller units (MCUs), a single-core ARM Cortex-M4 (STM32) for safe navigation and exploration policies, and a parallel ultra-low power octa-core RISC-V (GAP8) for onboard CNN inference, with a power envelope of just 134mW, including image sensors and external memories. The object detection task achieves a mean average precision of 50% (at 1.6 frame/s) on an in-field collected dataset. We compare four bio-inspired exploration policies and identify a pseudo-random policy to achieve the highest coverage area of 83% in a ~36m^2 unknown room in a 3 minutes flight. By combining the detection CNN and the exploration policy, we show an average detection rate of 90% on six target objects in a never-seen-before environment.

Landing is the most challenging and risky aspect of multirotor drone flight, and only simple landing methods exist for autonomous drones. We explore methods for autonomous drone landing in two scenarios. In the first scenario, we examine methods for landing on known landing pads using fiducial markers and a gimbal-mounted monocular camera. This method has potential in drone applications where a drone must land more accurately than GPS can provide (e.g.~package delivery in an urban canyon). We expand on previous methods by actuating the drone's camera to track the marker over time, and we address the complexities of pose estimation caused by fiducial marker orientation ambiguity. In the second scenario, and in collaboration with the RAVEN project, we explore methods for landing on solidified lava flows in Iceland, which serves as an analog environment for Mars and provides insight into the effectiveness of drone-rover exploration teams. Our drone uses a depth camera to visualize the terrain, and we are developing methods to analyze the terrain data for viable landing sites in real time with minimal sensors and external infrastructure requirements, so that the solution does not heavily influence the drone's behavior, mission structure, or operational environments.

Ukraine's president says he met with Danish Prime Minister Mette Frederiksen in the southern Ukrainian region of Mykolaiv and discussed the effect of Russian missile and drone strikes with regional officials. Finland's foreign minister says it is maintaining its plan to join NATO at the same time as Nordic neighbour Sweden despite a potential Turkish block on the latter's bid. NATO Secretary-General Jens Stoltenberg has urged South Korea to increase military support to Ukraine, citing other countries that have changed their policy of not providing weapons to countries in conflict following Russia's invasion. The Kremlin has accused Boris Johnson of lying after the former British prime minister said President Vladimir Putin had threatened the United Kingdom with a missile attack during a phone call in the run-up to the invasion of Ukraine. Iran has summoned Ukraine's charge d'affaires in Tehran over comments by a Ukrainian official on a drone attack on a military factory in the central Iranian province of Isfahan, according to the semiofficial Tasnim news agency.

This work presents a number of techniques to improve the ability to create magnetic field maps on a UAV which can be used to quickly and reliably gather magnetic field observations at multiple altitudes in a workspace. Unfortunately, the electronics on the UAV can introduce their own magnetic fields, distorting the resultant magnetic field map. We show methods of reducing and working with UAV-induced noise to better enable magnetic fields as a sensing modality for indoor navigation. First, some gains in our flight controller create high-frequency motor commands that introduce large noise in the measured magnetic field. Next, we implement a common noise reduction method of distancing the magnetometer from other components on our UAV. Finally, we introduce what we call a compromise GPR (Gaussian process regression) map that can be trained on multiple flight tests to learn any flight-by-flight variations between UAV observation tests. We investigate the spatial density of observations used to train a GPR map then use the compromise map to define a consistency test that can indicate whether or not the magnetometer data and corresponding GPR map are appropriate to use for state estimation. The interventions we introduce in this work facilitate indoor position localization of a UAV whose estimates we found to be quite sensitive to noise generated by the UAV.

Ukraine's Prosecutor General Andriy Kostin details Ukrainian efforts to investigate war crimes and responds to concerns of corruption within his country. Ukraine's military is planning to invest nearly $550 million in drones as the country mulls its next moves in its ongoing fight against Russia, according to a Monday report. Defense Minister Oleksii Reznikov said Ukraine is increasing the procurement of Unmanned Aerial Vehicles (UAVs), or drones, for reconnaissance and assault purposes this year and plans to allocate around $547.05 million for this segment. FILE: Ukrainian Defence Minister Oleksiy Reznikov attends a meeting of the Ukraine Defence contact group as part of a NATO Defence Ministers Council at the Alliance headquarters in Brussels on October 12, 2022. In the 11 months of fighting, Ukraine has received significant supplies of UAVS from its partners, including missile-equipped Bayraktar TB2's from Turkey and a Black Hornet reconnaissance drone from Norway.

Russia has begun its "big revenge" for Ukraine's resistance to its invasion, Ukrainian President Volodymyr Zelenskyy has said, as Russian forces claimed a series of incremental gains in his country's east. Zelenskyy has been warning for weeks that Moscow aims to step up its assault on Ukraine after about two months of virtual stalemate along the front line that stretches across the south and east. While there was no sign of a broader new offensive on Monday, the administrator of Russian-controlled parts of Ukraine's eastern Donetsk province, Denis Pushilin, said Russian troops had secured a foothold in Vuhledar, a coal mining town whose ruins have been a Ukrainian bastion since the outset of the war. Pushilin's adviser, Yan Gagin, said fighters from Russian mercenary force Wagner had taken partial control of a supply road leading to Bakhmut, a city that has been the focus of a Russian offensive for months. A day earlier, the head of Wagner said his fighters had secured Blahodatne, a village just north of Bakhmut.