In an era of hugely popular gimbal cameras like DJI's Osmo series, not to mention countless smartphone gimbals, Honor's Robot Phone is an ambitious attempt to combine that stabilization technology with the phone itself -- no peripheral needed. Its experimental new flagship phone is honestly less of a robot and more of a gimbal phone, but that would have been a terrible name. Is this the answer to countless content creators' dreams? When Honor revealed the device at MWC 2026 earlier this year, it was arguably the most exciting device on display at the Barcelona trade show. Despite the international reveal, the Robot Phone won't officially launch beyond China -- the device just went on sale there.

The Honor Robot Phone has been on our radar for a while now and it's finally available to preorder, though just in China for now. The "robot" in the name refers primarily to the automated pop-up camera gimbal, which does seem like a boon for photographers and videographers. This gimbal is made from titanium and can move in various directions, including a full 360-degree rotation. This obviously allows people to get difficult shots that normal smartphones would struggle with. It supports a wide range of cinematic movement modes.

Honor's Robot Phone Has a Gimbal-Powered Camera for Vloggers Honor's new China-exclusive Android phone features a pop-out motorized gimbal camera designed to replace your action cam. You hear it often: Smartphones have plateaued . A small boost to battery life here, a minor camera upgrade there--our glass slabs don't seem to change all that much from year to year. That's exactly the kind of sentiment Honor wants to counter with the Robot Phone, which has a motorized camera that pops up out of the top. Honor has been teasing the phone since late 2025 .

Accurate real-time waypoints estimation for the UAV-based online Terrain Following during wildfire patrol missions is critical to ensuring flight safety and enabling wildfire detection. However, existing real-time filtering algorithms struggle to maintain accurate waypoints under measurement noise in nonlinear and time-varying systems, posing risks of flight instability and missed wildfire detections during UAV-based terrain following. To address this issue, a Residual Variance Matching Recursive Least Squares (RVM-RLS) filter, guided by a Residual Variance Matching Estimation (RVME) criterion, is proposed to adaptively estimate the real-time waypoints of nonlinear, time-varying UAV-based terrain following systems. The proposed method is validated using a UAV-based online terrain following system within a simulated terrain environment. Experimental results show that the RVM-RLS filter improves waypoints estimation accuracy by approximately 88$\%$ compared with benchmark algorithms across multiple evaluation metrics. These findings demonstrate both the methodological advances in real-time filtering and the practical potential of the RVM-RLS filter for UAV-based online wildfire patrol.

Culverts on canals such as the Erie Canal, built originally in 1825, require frequent inspections to ensure safe operation. Human inspection of culverts is challenging due to age, geometry, poor illumination, weather, and lack of easy access. We introduce VISION, an end-to-end, language-in-the-loop autonomy system that couples a web-scale vision-language model (VLM) with constrained viewpoint planning for autonomous inspection of culverts. Brief prompts to the VLM solicit open-vocabulary ROI proposals with rationales and confidences, stereo depth is fused to recover scale, and a planner -- aware of culvert constraints -- commands repositioning moves to capture targeted close-ups. Deployed on a quadruped in a culvert under the Erie Canal, VISION closes the see, decide, move, re-image loop on-board and produces high-resolution images for detailed reporting without domain-specific fine-tuning. In an external evaluation by New York Canal Corporation personnel, initial ROI proposals achieved 61.4\% agreement with subject-matter experts, and final post-re-imaging assessments reached 80\%, indicating that VISION converts tentative hypotheses into grounded, expert-aligned findings.

This paper presents a novel control strategy for drone networks to improve the quality of 3D structures reconstructed from aerial images by drones. Unlike the existing coverage control strategies for this purpose, our proposed approach simultaneously controls both the camera orientation and drone translational motion, enabling more comprehensive perspectives and enhancing the map's overall quality. Subsequently, we present a novel problem formulation, including a new performance function to evaluate the drone positions and camera orientations. We then design a QP-based controller with a control barrier-like function for a constraint on the decay rate of the objective function. The present problem formulation poses a new challenge, requiring significantly greater computational efforts than the case involving only translational motion control. We approach this issue technologically, namely by introducing JAX, utilizing just-in-time (JIT) compilation and Graphical Processing Unit (GPU) acceleration. We finally conduct extensive verifications through simulation in ROS (Robot Operating System) and show the real-time feasibility of the controller and the superiority of the present controller to the conventional method.

The OBSBOT Tiny PTZ 4K webcam offers an incredible array of premium features for a reasonable price, capped off by an AI-powered ability to physically track your face as you move. The OBSBOT Tiny 4K certainly ranks among the best webcams you can buy, 4K or not, period. It offers so much: 4K video, a 60fps option (albeit at 1080p), and the real magic: an automated gimbal that physically rotates and dips the webcam to center your face. The OBSBOT Tiny 4K (sometimes sold as the OBSBOT Tiny PTZ 4K) will soon be supplemented by the Tiny 2, with a larger sensor for improved video. That should help solve one of the Tiny 4K's only shortcomings: video quality is good, just not outstanding.

The off-the-shelf drones are simple to operate and easy to maintain aerial systems. However, due to proprietary flight software, these drones usually do not provide any open-source interface which can enable them for autonomous flight in research or teaching. This work introduces a package for ROS1 and ROS2 for straightforward interfacing with off-the-shelf drones from the Parrot ANAFI family. The developed ROS package is hardware agnostic, allowing connecting seamlessly to all four supported drone models. This framework can connect with the same ease to a single drone or a team of drones from the same ground station. The developed package was intensively tested at the limits of the drones' capabilities and thoughtfully documented to facilitate its use by other research groups worldwide.

We enjoyed messing around with the games, but pretty quickly our mind wandered to what these things can do. VR is still a young space, and you quite frequently find yourself wanting an app that doesn't exist. At the top of our list: Could you view the world through a drone's camera while you flew it? We'd taken a few AI courses in the past, and we thought that single camera 3D VR might just be possible. In the world of autonomous vehicles, there is similar work underway in the form of research into "Pseudo-LIDAR".

European drone company Parrot is releasing a new drone, the Anafi AI, in the second half of 2021. Notably, it's the first to use 4G as a main data link between the drone and remote--effectively doing away with transmission limitations. It can also use a Wi-Fi connection. Parrot claims remote pilots can operate the drone Beyond Visual Line of Sight (if permission is obtained) and it won't lose connection behind obstacles such as trees or buildings. A camera with a 48MP 1/2" Quad Bayer sensor, 24mm lens, a fixed F2.0 aperture and 73º FOV is affixed to a 6-axis (3-axis mechanical, 3-axis electronic) stabilized gimbal. Parrot says that the camera offers the same precision as a drone with a 1"-type 20MP CMOS sensor while flying 1.5 times higher.