Ukraine said its air defence systems shot down 16 of about 30 drones launched by Russia on Sunday. Authorities said civilian infrastructure and grain storage warehouses were damaged in the Cherkasy region as well as the southern Mykolaiv and eastern Dnipropetrovsk regions. Russia's defence ministry said its forces' air defences in eastern Ukraine had intercepted five United States-made HIMARS shells, an air-launched JDAM bomb and 37 Ukrainian drones. Kyiv began a counteroffensive in June to retake Ukrainian land occupied by Russia since it launched its full-scale invasion of the country in February 2022. Russia's defence ministry said it shot down six Ukrainian drones over Russian regions and two Ukrainian missiles over Crimea, which Moscow annexed from Ukraine in 2014.

In this paper, we present a differentiable, decision-oriented learning technique for a class of vehicle routing problems. Specifically, we consider a scenario where a team of Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) are persistently monitoring an environment. The UGVs are occasionally taken over by humans to take detours to recharge the depleted UAVs. The goal is to select routes for the UGVs so that they can efficiently monitor the environment while reducing the cost of interventions. The former is modeled as a monotone, submodular function whereas the latter is a linear function of the routes of the UGVs. We consider a scenario where the former is known but the latter depends on the context (e.g., wind and terrain conditions) that must be learned. Typically, we first learn to predict the cost function and then solve the optimization problem. However, the loss function used in prediction may be misaligned with our final goal of finding good routes. We propose a \emph{decision-oriented learning} framework that incorporates task optimization as a differentiable layer in the prediction phase. To make the task optimization (which is a non-monotone submodular function) differentiable, we propose the Differentiable Cost Scaled Greedy algorithm. We demonstrate the efficacy of the proposed framework through numerical simulations. The results show that the proposed framework can result in better performance than the traditional approach.

As a parametric motion representation, B\'ezier curves have significant applications in polynomial trajectory optimization for safe and smooth motion planning of various robotic systems, including flying drones, autonomous vehicles, and robotic manipulators. An essential component of B\'ezier curve optimization is the optimization objective, as it significantly influences the resulting robot motion. Standard physical optimization objectives, such as minimizing total velocity, acceleration, jerk, and snap, are known to yield quadratic optimization of B\'ezier curve control points. In this paper, we present a unifying graph-theoretic perspective for defining and understanding B\'ezier curve optimization objectives using a consensus distance of B\'ezier control points derived based on their interaction graph Laplacian. In addition to demonstrating how standard physical optimization objectives define a consensus distance between B\'ezier control points, we also introduce geometric and statistical optimization objectives as alternative consensus distances, constructed using finite differencing and differential variance. To compare these optimization objectives, we apply B\'ezier curve optimization over convex polygonal safe corridors that are automatically constructed around a maximal-clearance minimal-length reference path. We provide an explicit analytical formulation for quadratic optimization of B\'ezier curves using B\'ezier matrix operations. We conclude that the norm and variance of the finite differences of B\'ezier control points lead to simpler and more intuitive interaction graphs and optimization objectives compared to B\'ezier derivative norms, despite having similar robot motion profiles.

This paper considers energy-aware route planning for a battery-constrained robot operating in environments with multiple recharging depots. The robot has a battery discharge time $D$, and it should visit the recharging depots at most every $D$ time units to not run out of charge. The objective is to minimize robot's travel time while ensuring it visits all task locations in the environment. We present a $O(\log D)$ approximation algorithm for this problem. We also present heuristic improvements to the approximation algorithm and assess its performance on instances from TSPLIB, comparing it to an optimal solution obtained through Integer Linear Programming (ILP). The simulation results demonstrate that, despite a more than $20$-fold reduction in runtime, the proposed algorithm provides solutions that are, on average, within $31\%$ of the ILP solution.

This paper considers collision avoidance for vehicles with first-order nonholonomic constraints maintaining nonzero forward speeds, moving within dynamic environments. We leverage the concept of control barrier functions (CBFs) to synthesize control inputs that prioritize safety, where the safety criteria are derived from the velocity obstacle principle. Existing instantiations of CBFs for collision avoidance, e.g., based on maintaining a minimal distance, can result in control inputs that make the vehicle stop or even reverse. The proposed formulation effectively separates speed control from steering, allowing the vehicle to maintain a forward motion without compromising safety. This is beneficial for ensuring that the vehicle advances towards its desired destination, and it is moreover an underlying requirement for certain vehicles such as marine vessels and fixed-wing UAVs. Theoretical safety guarantees are provided, and numerical simulations demonstrate the efficiency of the strategy in environments containing moving obstacles.

The planning and control of a robot swarm in a complex environment have attracted increasing attention. To this end, the idea of virtual tubes has been taken up in our previous work. Specifically, a virtual tube with varying widths has been planned to avoid collisions with obstacles in a complex environment. Based on the planned virtual tube for a large number of speed-constrained robots, the average forward speed and density along the virtual tube are further planned in this paper to ensure safety and improve efficiency. Compared with the existing methods, the proposed method is based on global information and can be applied to traversing narrow spaces for speed-constrained robot swarms. Numerical simulations and experiments are conducted to show that the safety and efficiency of the passing-through process are improved. A video about simulations and experiments is available on https://youtu.be/lJHdMQMqSpc.

To plan the trajectories of a large and heterogeneous swarm, sequential or synchronous distributed methods usually become intractable, due to the lack of global connectivity and clock synchronization, Moreover, the existing asynchronously distributed schemes usually require recheck-like mechanisms instead of inherently considering the other' moving tendency. To this end, we propose a novel asynchronous protocol to allocate the agents' derivable space in a distributed way, by which each agent can replan trajectory depending on its own timetable. Properties such as collision avoidance and recursive feasibility are theoretically shown and a lower bound of protocol updating is provided. Comprehensive simulations and comparisons with five state-of-the-art methods validate the effectiveness of our method and illustrate the improvement in both the completion time and the moving distance. Finally, hardware experiments are carried out, where 8 heterogeneous unmanned ground vehicles with onboard computation navigate in cluttered scenarios at a high agility.

Causal modelling offers great potential to provide autonomous agents the ability to understand the data-generation process that governs their interactions with the world. Such models capture formal knowledge as well as probabilistic representations of noise and uncertainty typically encountered by autonomous robots in real-world environments. Thus, causality can aid autonomous agents in making decisions and explaining outcomes, but deploying causality in such a manner introduces new challenges. Here we identify challenges relating to causality in the context of a drone system operating in a salt mine. Such environments are challenging for autonomous agents because of the presence of confounders, non-stationarity, and a difficulty in building complete causal models ahead of time. To address these issues, we propose a probabilistic causal framework consisting of: causally-informed POMDP planning, online SCM adaptation, and post-hoc counterfactual explanations. Further, we outline planned experimentation to evaluate the framework integrated with a drone system in simulated mine environments and on a real-world mine dataset.

NATO Secretary General Jens Stoltenberg said on a visit to Kyiv that Ukrainian forces are "gradually gaining ground" in their counteroffensive against Russian forces. "Every metre that Ukrainian forces regain is a metre that Russia loses," he said at a joint press conference with Ukrainian President Volodymyr Zelenskyy. Russia said it destroyed 11 Ukrainian drones overnight in an attack that saw one combat drone drop explosives on a power substation, cutting a local power supply in Russia's Kursk region. Russian President Vladimir Putin signed a decree setting out his country's routine autumn conscription campaign, which will see 130,000 people called up for statutory military service. Adult men in Russia are required to do a yearlong military service between the ages of 18 and 27 or equivalent training while pursuing higher education.

"We needed weapons, and we needed them fast," 3D says, sitting beneath the stalactites in a dimly lit cave, somewhere deep in the jungle in eastern Myanmar. The space reverberates with the hum of 3D printers--the devices that gave 3D his nom de guerre. He spoke on condition that WIRED would not reveal his real name or show his face. "My parents would kill me if they would know what I'm up to," he says. Not only does 3D face the risk of arrest, torture, or execution for his part in the revolution, the military would not hesitate to arrest his parents if they were to discover 3D's identity.