Accurate and safety-quantifiable localization is of great significance for safety-critical autonomous systems, such as unmanned ground vehicles (UGV) and unmanned aerial vehicles (UAV). The visual odometry-based method can provide accurate positioning in a short period but is subjected to drift over time. Moreover, the quantification of the safety of the localization solution (the error is bounded by a certain value) is still a challenge. To fill the gaps, this paper proposes a safety-quantifiable line feature-based visual localization method with a prior map. The visual-inertial odometry provides a high-frequency local pose estimation which serves as the initial guess for the visual localization. By obtaining a visual line feature pair association, a foot point-based constraint is proposed to construct the cost function between the 2D lines extracted from the real-time image and the 3D lines extracted from the high-precision prior 3D point cloud map. Moreover, a global navigation satellite systems (GNSS) receiver autonomous integrity monitoring (RAIM) inspired method is employed to quantify the safety of the derived localization solution. Among that, an outlier rejection (also well-known as fault detection and exclusion) strategy is employed via the weighted sum of squares residual with a Chi-squared probability distribution. A protection level (PL) scheme considering multiple outliers is derived and utilized to quantify the potential error bound of the localization solution in both position and rotation domains. The effectiveness of the proposed safety-quantifiable localization system is verified using the datasets collected in the UAV indoor and UGV outdoor environments.

The 1$^{\text{st}}$ Workshop on Maritime Computer Vision (MaCVi) 2023 focused on maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicle (USV), and organized several subchallenges in this domain: (i) UAV-based Maritime Object Detection, (ii) UAV-based Maritime Object Tracking, (iii) USV-based Maritime Obstacle Segmentation and (iv) USV-based Maritime Obstacle Detection. The subchallenges were based on the SeaDronesSee and MODS benchmarks. This report summarizes the main findings of the individual subchallenges and introduces a new benchmark, called SeaDronesSee Object Detection v2, which extends the previous benchmark by including more classes and footage. We provide statistical and qualitative analyses, and assess trends in the best-performing methodologies of over 130 submissions. The methods are summarized in the appendix. The datasets, evaluation code and the leaderboard are publicly available at https://seadronessee.cs.uni-tuebingen.de/macvi.

Utilizing autonomous drones or unmanned aerial vehicles (UAVs) has shown great advantages over preceding methods in support of urgent scenarios such as search and rescue (SAR) and wildfire detection. In these operations, search efficiency in terms of the amount of time spent to find the target is crucial since with the passing of time the survivability of the missing person decreases or wildfire management becomes more difficult with disastrous consequences. In this work, it is considered a scenario where a drone is intended to search and detect a missing person (e.g., a hiker or a mountaineer) or a potential fire spot in a given area. In order to obtain the shortest path to the target, a general framework is provided to model the problem of target detection when the target's location is probabilistically known. To this end, two algorithms are proposed: Path planning and target detection. The path planning algorithm is based on Bayesian inference and the target detection is accomplished by means of a residual neural network (ResNet) trained on the image dataset captured by the drone as well as existing pictures and datasets on the web. Through simulation and experiment, the proposed path planning algorithm is compared with two benchmark algorithms. It is shown that the proposed algorithm significantly decreases the average time of the mission.

There's ion propulsion drones, and then this AI racing drone, developed by University of Zurich researchers. Human drone pilots were invited to the Robotics and Perception Group for a friendly race, with each one getting pit against various AI drones, starting with one using 36 tracking cameras. The camera is used to capture 400 fps of video, in which the AI drone uses in combination with four tracking markers. This footage is then sent to a vision and navigation system capable of translating it into flight commands. These are then sent to the drone in real-time over a wireless connection.

Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. The model is able to transfer knowledge between a simulated environment and real-world settings.

Recent technological advancements in space, air and ground components have made possible a new network paradigm called "space-air-ground integrated network" (SAGIN). Unmanned aerial vehicles (UAVs) play a key role in SAGINs. However, due to UAVs' high dynamics and complexity, the real-world deployment of a SAGIN becomes a major barrier for realizing such SAGINs. Compared to the space and terrestrial components, UAVs are expected to meet performance requirements with high flexibility and dynamics using limited resources. Therefore, employing UAVs in various usage scenarios requires well-designed planning in algorithmic approaches. In this paper, we provide a comprehensive review of recent learning-based algorithmic approaches. We consider possible reward functions and discuss the state-of-the-art algorithms for optimizing the reward functions, including Q-learning, deep Q-learning, multi-armed bandit (MAB), particle swarm optimization (PSO) and satisfaction-based learning algorithms. Unlike other survey papers, we focus on the methodological perspective of the optimization problem, which can be applicable to various UAV-assisted missions on a SAGIN using these algorithms. We simulate users and environments according to real-world scenarios and compare the learning-based and PSO-based methods in terms of throughput, load, fairness, computation time, etc. We also implement and evaluate the 2-dimensional (2D) and 3-dimensional (3D) variations of these algorithms to reflect different deployment cases. Our simulation suggests that the $3$D satisfaction-based learning algorithm outperforms the other approaches for various metrics in most cases. We discuss some open challenges at the end and our findings aim to provide design guidelines for algorithm selections while optimizing the deployment of UAV-assisted SAGINs.

A "Festival of Robotics" conjured images of dancing androids and canape-serving cyborgs, or at least one of those Boston Dynamics monstrosities that resembles a fleshless Terminator but moves like a gymnast. Held on a wet day at BT's Adastral Park R&D facility, it did feature one of Boston Dynamics' mechanized dogs, which performed some lively robot dressage before it scampered off at pitbull speed, presumably on a kill mission. But there was not much festival atmosphere. "The rain has had a squashing impact on our ability to have a beer tent and open summer garden, but we will have dancing and other exciting things such as robot wars," said a spokesperson at a mid-morning presentation. Perhaps the robots came out to dance and fight in the evening, long after reporters had departed.

Aerial package transportation often requires complex spatial and temporal specifications to be satisfied in order to ensure safe and timely delivery from one point to another. It is usually efficient to transport versatile payloads using multiple UAVs that can work collaboratively to achieve the desired task. The complex temporal specifications can be handled coherently by applying Signal Temporal Logic (STL) to dynamical systems. This paper addresses the problem of waypoint navigation of a multi-UAV payload system under temporal specifications using higher-order time-varying control barrier functions (HOCBFs). The complex nonlinear system of relative degree two is transformed into a simple linear system using input-output feedback linearization. An optimization-based control law is then derived to achieve the temporal waypoint navigation of the payload. The controller's efficacy and real-time implementability are demonstrated by simulating a package delivery scenario inside a high-fidelity Gazebo simulation environment.

After recapturing Kherson city, Ukraine kept Russian forces guessing about their next move, pinning down occupying troops in defensive positions and rendering them unavailable for offensive operations. Some 30,000 Russian troops that withdrew from the west bank of the Dnieper river earlier this month were entrenching themselves in the Zaporizhia and Kherson regions during the 39th week of the war, deputy head of Ukrainian military intelligence Major-General Vadym Skibitskyi, told the Kyiv Post. "[The Russians] are waiting for our liberation offensive, that's why they have created a defensive line in Kherson, another on the administrative border of [Kherson and] Crimea, and another in the northern Crimea region," Skibitskiy said. "The enemy is on the defensive in the Zaporizhzhia direction," said Ukraine's general staff. "In the Kryvyi Rih and Kherson directions, the enemy is creating an echeloned defence system, improving fortification equipment and logistical support of advanced units, and not stopping artillery fire at the positions of our troops and settlements on the right bank of the Dnipro River."

The coupling disturbance between the manipulator and the unmanned aerial vehicle (UAV) deteriorates the control performance of system. To get high performance of the aerial manipulator, a robust fractional order fast terminal sliding mode control (FOFTSMC) strategy based on mutable inertia parameters is proposed in this paper. First, the dynamics of aerial manipulator with consideration of the coupling disturbance is derived by utilizing mutable inertia parameters. Then, based on the dynamic model, a robust FOFTSMC algorithm is designed to make the system fly steadily under coupling disturbance. Furthermore, stability analysis is conducted to prove the convergence of tracking errors. Finally, comparative simulation results are given to show the validity and superiority of the proposed scheme.