DJI continues its streak of innovative (and highly leaked) drones with the launch of the Flip, a lightweight and people-safe model that folds in a new direction -- downward -- to accommodate the large, shrouded propellers. The new model should appeal to beginners and experienced users alike with features like a large sensor, 4K 100p video, safety features, a three-axis gimbal and an affordable price. The company says the Flip "combine[s] the simplicity of the DJI Neo with the stunning photo capabilities of the DJI Mini," but in many ways, it's better than both. It borrows a LiDAR system from the Air 3S for obstacle detection and the Flip's propellers are protected on all sides, making it all but impossible to hurt someone with them. DJI says the support structure for the guards is made of carbon fiber string that's 1/60th the weight of polycarbonate material and just as strong.

Drones have become a "threat to national security", the prisons watchdog has said, after a surge in the amount of weapons and drugs flown into high-security jails. Charlie Taylor, the chief inspector of prisons, called for urgent action from Whitehall and the police after inquiries found that terrorism suspects and criminal gangs could escape or attack guards because safety had been "seriously compromised". His demands follow inspections at two category A prisons holding some of England and Wales's most dangerous inmates. HMP Manchester and HMP Long Lartin in Worcestershire had thriving illicit economies selling drugs, mobile phones and weapons, and basic anti-drone security measures such as protective netting and CCTV had been allowed to fall into disrepair, inspectors found. In a report released on Tuesday, Taylor said the police and prison service had "in effect ceded the airspace above two high-security prisons to organised crime gangs" despite knowing they were holding "extremely dangerous prisoners".

Mr Taylor's warnings come in damning reports into the respective conditions at the maximum security jails. His inspection teams found serious and repeated failings of security and safety, with clear evidence of gangs arranging delivery by air of items including weapons, drugs and phones to inmates. "This is a threat to national security," said Mr Taylor. "The potential for serious weapons to be able to get into our prisons in increasing numbers means that there is a risk, particularly with these Category A prisons, particularly with some of the riskiest men in the country who are either connected to organised crime gangs or they're terrorists. "The potential for them to be able to commit serious offences within prison, or potentially to be able to escape or to cause something like a hostage situation is an enormous concern."

We propose the Cooperative Aerial Robot Inspection Challenge (CARIC), a simulation-based benchmark for motion planning algorithms in heterogeneous multi-UAV systems. CARIC features UAV teams with complementary sensors, realistic constraints, and evaluation metrics prioritizing inspection quality and efficiency. It offers a ready-to-use perception-control software stack and diverse scenarios to support the development and evaluation of task allocation and motion planning algorithms. Competitions using CARIC were held at IEEE CDC 2023 and the IROS 2024 Workshop on Multi-Robot Perception and Navigation, attracting innovative solutions from research teams worldwide. This paper examines the top three teams from CDC 2023, analyzing their exploration, inspection, and task allocation strategies while drawing insights into their performance across scenarios. The results highlight the task's complexity and suggest promising directions for future research in cooperative multi-UAV systems.

In human-robot teams, human situational awareness is the operator's conscious knowledge of the team's states, actions, plans and their environment. Appropriate human situational awareness is critical to successful human-robot collaboration. In human-robot teaming, it is often assumed that the best and required level of situational awareness is knowing everything at all times. This view is problematic, because what a human needs to know for optimal team performance varies given the dynamic environmental conditions, task context and roles and capabilities of team members. We explore this topic by interviewing 16 participants with active and repeated experience in diverse human-robot teaming applications. Based on analysis of these interviews, we derive a framework explaining the dynamic nature of required situational awareness in human-robot teaming. In addition, we identify a range of factors affecting the dynamic nature of required and actual levels of situational awareness (i.e., dynamic situational awareness), types of situational awareness inefficiencies resulting from gaps between actual and required situational awareness, and their main consequences. We also reveal various strategies, initiated by humans and robots, that assist in maintaining the required situational awareness. Our findings inform the implementation of accurate estimates of dynamic situational awareness and the design of user-adaptive human-robot interfaces. Therefore, this work contributes to the future design of more collaborative and effective human-robot teams.

Modern machine learning techniques have shown tremendous potential, especially for object detection on camera images. For this reason, they are also used to enable safety-critical automated processes such as autonomous drone flights. We present a study on object detection for Detect and Avoid, a safety critical function for drones that detects air traffic during automated flights for safety reasons. An ill-posed problem is the generation of good and especially large data sets, since detection itself is the corner case. Most models suffer from limited ground truth in raw data, \eg recorded air traffic or frontal flight with a small aircraft. It often leads to poor and critical detection rates. We overcome this problem by using inpainting methods to bootstrap the dataset such that it explicitly contains the corner cases of the raw data. We provide an overview of inpainting methods and generative models and present an example pipeline given a small annotated dataset. We validate our method by generating a high-resolution dataset, which we make publicly available and present it to an independent object detector that was fully trained on real data.

The FBI recently confirmed a Canadian plane offering assistance during the California wildfires was damaged in a collision with a privately-owned drone. Police arrested three people following two drone incidents as authorities report numerous encounters with aerial operations, potentially hampering lifesaving measures as wildfires rage throughout Southern California. As of Monday afternoon, charges had not been released. Two arrests stem from one drone incident, according to Los Angeles County Sheriff Robert Luna. "If you do not have business in the evacuation areas, do not go there," Luna said in a press conference on Monday.

This paper introduces a safe swarm of drones capable of performing landings in crowded environments robustly by relying on Reinforcement Learning techniques combined with Safe Learning. The developed system allows us to teach the swarm of drones with different dynamics to land on moving landing pads in an environment while avoiding collisions with obstacles and between agents. The safe barrier net algorithm was developed and evaluated using a swarm of Crazyflie 2.1 micro quadrotors, which were tested indoors with the Vicon motion capture system to ensure precise localization and control. Experimental results show that our system achieves landing accuracy of 2.25 cm with a mean time of 17 s and collision-free landings, underscoring its effectiveness and robustness in real-world scenarios. This work offers a promising foundation for applications in environments where safety and precision are paramount.

The utilization of Unmanned Ground Vehicles (UGVs) for patrolling industrial sites has expanded significantly. These UGVs typically are equipped with perception systems, e.g., computer vision, with limited range due to sensor limitations or site topology. High-level control of the UGVs requires Coverage Path Planning (CPP) algorithms that navigate all relevant waypoints and promptly start the next cycle. In this paper, we propose the novel Fast-Revisit Coverage Path Planning (FaRe-CPP) algorithm using a greedy heuristic approach to propose waypoints for maximum coverage area and a random search-based path optimization technique to obtain a path along the proposed waypoints with minimum revisit time. We evaluated the algorithm in a simulated environment using Gazebo and a camera-equipped TurtleBot3 against a number of existing algorithms. Compared to their average revisit times and path lengths, our FaRe-CPP algorithm approximately showed a 45% and 40% reduction, respectively, in these highly relevant performance indicators.

Improving robustness to uncertainty and rejection of external disturbances represents a significant challenge in aerial robotics. Nonlinear controllers based on Incremental Nonlinear Dynamic Inversion (INDI), known for their ability in estimating disturbances through measured-filtered data, have been notably used in such applications. Typically, these controllers comprise two cascaded loops: an inner loop employing nonlinear dynamic inversion and an outer loop generating the virtual control inputs via linear controllers. In this paper, a novel methodology is introduced, that combines the advantages of INDI with the robustness of linear structured $\mathcal{H}_\infty$ controllers. A full cascaded architecture is proposed to control the dynamics of a multirotor drone, covering both stabilization and guidance. In particular, low-order $\mathcal{H}_\infty$ controllers are designed for the outer loop by properly structuring the problem and solving it through non-smooth optimization. A comparative analysis is conducted between an existing INDI/PD approach and the proposed INDI/$\mathcal{H}_\infty$ strategy, showing a notable enhancement in the rejection of external disturbances. It is carried out first using MATLAB simulations involving a nonlinear model of a Parrot Bebop quadcopter drone, and then experimentally using a customized quadcopter built by the ENAC team. The results show an improvement of more than 50\% in the rejection of disturbances such as gusts.