Overland AI’s Post

A first-of-its kind nighttime #navigation lets you fly in darkness or low-light environments, with #computer vision interpreting #flight paths and avoiding obstacles to extend #autonomous capabilities 24/7. Bring the power of autonomous flight to tight indoor inspections, poorly-lit infrastructure and nighttime operations with capabilities that other #drones can only dream of.

Let’s follow with the second activity of the project: “The Unboxing of the components” This section covers the unboxing of key drone components: the Hexsoon drone frame, radio control, Autopilot for navigation, laser altimeter, battery checker, landing pad, Raspberry Pi for onboard processing, GPS compass, radio telemetry kit, Black box, and toolbox for assembly and maintenance.

Playtime's over! It may look like a toy from a drone's eye view, but these are early test runs of our OverDrive autonomy stack piloting a 12 ton Textron M5 through hills and vegetation last winter. No prior maps, no pre-defined path, no teleoperation. Just realtime (1x) video of high-speed point-to-point navigation using onboard sensing and compute. 👉 https://lnkd.in/ejvtTDtN

Autonomous systems, from autonomous on- and off-road platforms to unmanned aerial vehicles or marine vessels, all require the best positioning, navigation and timing (#PNT). Our firmware includes a Precise Timing Protocol (PTP) that synchronises multiple devices on the same Ethernet network to the time received from GNSS. Time in GNSS satellites is powered by an atomic clock, making it the most accurate timing source available. Leveraging GNSS time removes the need for an external clock source within your system and enables the highest level of accuracy for positioning and timing. Learn more >> https://hxgn.biz/3pnrdlu

Formulating a Mathematical Representation of the Autonomous Drone's Mission Environment: A Self-Guided Exploration of Some of the Motion Planning and Control Algorithms Used for Autonomous Quadcopter Flight: Brief No. 31: That process of establishing definitions began with my formulation of what comprised the drone’s mission environment. Since the path planning module I was to develop was required to ultimately produce an XYZ waypoint sequence, I decided to define the mission environment E as the set of all points in three-dimensional Euclidean space bounded by, in each coordinate direction, the maximum and minimum positions of all obstacles provided with the Udacity obstacle mapping data file.

🌌 Charting New Horizons: Redefining Drone Autonomy with Technology 📡 Embarking on a journey of innovation, we're charting new horizons and redefining the landscape of drone autonomy with cutting-edge technology.

Diving into Obstacle Avoidance for Autonomous Flight 🔎 As part of my self-guided exploration of motion planning for quadcopters, I'm working on a receding horizon planning (RHP) system. Here's the core idea behind my obstacle avoidance approach: Dynamic Calculations: My code constantly re-evaluates the drone's path based on new environmental data. Repulsive Forces: Obstacles exert "push" forces on the drone's artificial velocity field, influencing its trajectory. Attractive Pull: The drone is simultaneously drawn towards its goal, creating a balanced navigation system. The Bigger Picture: This is one piece of my larger project investigating autonomous quadcopter flight algorithms. I'm excited to share how these elements fit together to achieve intelligent navigation. Let's Connect: If you're passionate about autonomous flight, motion planning, or control systems, let's discuss! #AutonomousDrones #MotionPlanning #Coding