Haptic feedback enhances collision avoidance by providing directional obstacle information to operators in unmanned aerial vehicle (UAV) teleoperation. However, such feedback is often rendered via haptic joysticks, which are unfamiliar to UAV operators and limited to single-directional force feedback. Additionally, the direct coupling of the input device and the feedback method diminishes the operators' control authority and causes oscillatory movements. To overcome these limitations, we propose AeroHaptix, a wearable haptic feedback system that uses high-resolution vibrations to communicate multiple obstacle directions simultaneously. The vibrotactile actuators' layout was optimized based on a perceptual study to eliminate perceptual biases and achieve uniform spatial coverage. A novel rendering algorithm, MultiCBF, was adapted from control barrier functions to support multi-directional feedback. System evaluation showed that AeroHaptix effectively reduced collisions in complex environment, and operators reported significantly lower physical workload, improved situational awareness, and increased control authority.

This paper presents the OmniRace approach to controlling a racing drone with 6-degree of freedom (DoF) hand pose estimation and gesture recognition. To our knowledge, it is the first-ever technology that allows for low-level control of high-speed drones using gestures. OmniRace employs a gesture interface based on computer vision and a deep neural network to estimate a 6-DoF hand pose. The advanced machine learning algorithm robustly interprets human gestures, allowing users to control drone motion intuitively. Real-time control of a racing drone demonstrates the effectiveness of the system, validating its potential to revolutionize drone racing and other applications. Experimental results conducted in the Gazebo simulation environment revealed that OmniRace allows the users to complite the UAV race track significantly (by 25.1%) faster and to decrease the length of the test drone path (from 102.9 to 83.7 m). Users preferred the gesture interface for attractiveness (1.57 UEQ score), hedonic quality (1.56 UEQ score), and lower perceived temporal demand (32.0 score in NASA-TLX), while noting the high efficiency (0.75 UEQ score) and low physical demand (19.0 score in NASA-TLX) of the baseline remote controller. The deep neural network attains an average accuracy of 99.75% when applied to both normalized datasets and raw datasets. OmniRace can potentially change the way humans interact with and navigate racing drones in dynamic and complex environments. The source code is available at https://github.com/SerValera/OmniRace.git.

Former U.S. ambassador to NATO Kay Bailey Hutchinson discusses Biden's recent effort to show American allies that he is fit to serve as president and Ukrainian President Zelenskyy's concern about delaying action against Russia. Struggling with manpower shortages, overwhelming odds and uneven international assistance, Ukraine hopes to find a strategic edge against Russia in an abandoned warehouse or a factory basement. An ecosystem of laboratories in hundreds of secret workshops is leveraging innovation to create a robot army that Ukraine hopes will kill Russian troops and save its own wounded soldiers and civilians. Defense startups across Ukraine -- about 250 according to industry estimates -- are creating the killing machines at secret locations that typically look like rural car repair shops. Employees at a startup run by entrepreneur Andrii Denysenko can put together an unmanned ground vehicle called the Odyssey in four days at a shed used by the company.

In air-ground collaboration scenarios without GPS and prior maps, the relative positioning of drones and unmanned ground vehicles (UGVs) has always been a challenge. For a drone equipped with monocular camera and an UGV equipped with LiDAR as an external sensor, we propose a robust and real-time relative pose estimation method (LVCP) based on the tight coupling of vision and LiDAR point cloud information, which does not require prior information such as maps or precise initial poses. Given that large-scale point clouds generated by 3D sensors has more accurate spatial geometric information than the feature point cloud generated by image, we utilize LiDAR point clouds to correct the drift in visual-inertial odometry (VIO) when the camera undergoes significant shaking or the IMU has a low signal-to-noise ratio. To achieve this, we propose a novel coarse-to-fine framework for LiDAR-vision collaborative localization. In this framework, we construct point-plane association based on spatial geometric information, and innovatively construct a point-aided Bundle Adjustment (BA) problem as the backend to simultaneously estimate the relative pose of the camera and LiDAR and correct the VIO drift. In this process, we propose a particle swarm optimization (PSO) based sampling algorithm to complete the coarse estimation of the current camera-LiDAR pose. In this process, the initial pose of the camera used for sampling is obtained based on VIO propagation, and the valid feature-plane association number (VFPN) is used to trigger PSO-sampling process. Additionally, we propose a method that combines Structure from Motion (SFM) and multi-level sampling to initialize the algorithm, addressing the challenge of lacking initial values.

In the rapidly evolving landscape of digital content creation, the demand for fast, convenient, and autonomous methods of crafting detailed 3D reconstructions of humans has grown significantly. Addressing this pressing need, our AirNeRF system presents an innovative pathway to the creation of a realistic 3D human avatar. Our approach leverages Neural Radiance Fields (NeRF) with an automated drone-based video capturing method. The acquired data provides a swift and precise way to create high-quality human body reconstructions following several stages of our system. The rigged mesh derived from our system proves to be an excellent foundation for free-view synthesis of dynamic humans, particularly well-suited for the immersive experiences within gaming and virtual reality.

This article presents a three-dimensional nonlinear trajectory tracking control strategy for unmanned aerial vehicles (UAVs) in the presence of spatial constraints. As opposed to many existing control strategies, which do not consider spatial constraints, the proposed strategy considers spatial constraints on each degree of freedom movement of the UAV. Such consideration makes the design appealing for many practical applications, such as pipeline inspection, boundary tracking, etc. The proposed design accounts for the limited information about the inertia matrix, thereby affirming its inherent robustness against unmodeled dynamics and other imperfections. We rigorously show that the UAV will converge to its desired path by maintaining bounded position, orientation, and linear and angular speeds. Finally, we demonstrate the effectiveness of the proposed strategy through various numerical simulations.

Deep reinforcement learning (DRL) has achieved remarkable progress in online path planning tasks for multi-UAV systems. However, existing DRL-based methods often suffer from performance degradation when tackling unseen scenarios, since the non-causal factors in visual representations adversely affect policy learning. To address this issue, we propose a novel representation learning approach, \ie, causal representation disentanglement, which can identify the causal and non-causal factors in representations. After that, we only pass causal factors for subsequent policy learning and thus explicitly eliminate the influence of non-causal factors, which effectively improves the generalization ability of DRL models. Experimental results show that our proposed method can achieve robust navigation performance and effective collision avoidance especially in unseen scenarios, which significantly outperforms existing SOTA algorithms.

In unseen and complex outdoor environments, collision avoidance navigation for unmanned aerial vehicle (UAV) swarms presents a challenging problem. It requires UAVs to navigate through various obstacles and complex backgrounds. Existing collision avoidance navigation methods based on deep reinforcement learning show promising performance but suffer from poor generalization abilities, resulting in performance degradation in unseen environments. To address this issue, we investigate the cause of weak generalization ability in DRL and propose a novel causal feature selection module. This module can be integrated into the policy network and effectively filters out non-causal factors in representations, thereby reducing the influence of spurious correlations between non-causal factors and action predictions. Experimental results demonstrate that our proposed method can achieve robust navigation performance and effective collision avoidance especially in scenarios with unseen backgrounds and obstacles, which significantly outperforms existing state-of-the-art algorithms.

A squad of soldiers is under attack and pinned down by rockets in the close quarters of urban combat. One of them makes a call over his radio, and within moments a fleet of small autonomous drones equipped with explosives fly through the town square, entering buildings and scanning for enemies before detonating on command. One by one the suicide drones seek out and kill their targets. A voiceover on the video, a fictional ad for multibillion-dollar Israeli weapons company Elbit Systems, touts the AI-enabled drones' ability to "maximize lethality and combat tempo". While defense companies like Elbit promote their new advancements in artificial intelligence (AI) with sleek dramatizations, the technology they are developing is increasingly entering the real world.

Mapping and navigation have gone hand-in-hand since long before robots existed. Maps are a key form of communication, allowing someone who has never been somewhere to nonetheless navigate that area successfully. In the context of multi-robot systems, the maps and information that flow between robots are necessary for effective collaboration, whether those robots are operating concurrently, sequentially, or completely asynchronously. In this paper, we argue that maps must go beyond encoding purely geometric or visual information to enable increasingly complex autonomy, particularly between robots. We propose a framework for multi-robot autonomy, focusing in particular on air and ground robots operating in outdoor 2.5D environments. We show that semantic maps can enable the specification, planning, and execution of complex collaborative missions, including localization in GPS-denied settings. A distinguishing characteristic of this work is that we strongly emphasize field experiments and testing, and by doing so demonstrate that these ideas can work at scale in the real world. We also perform extensive simulation experiments to validate our ideas at even larger scales. We believe these experiments and the experimental results constitute a significant step forward toward advancing the state-of-the-art of large-scale, collaborative multi-robot systems operating with real communication, navigation, and perception constraints.