RT @chesterzelaya: extremely excited to be announcing @uddupa to the @thedroneforge team! . Ud brings:. > ⁠9+ yrs of AI experience .> PhD f….

< Conclusion > . GMM-based control offers a subtle but powerful shift. Instead of reacting to changing density fields through classical clustering methods, it uses probability distributions and velocities to flow with them more accurately . When the world moves, this

< Sim Performance & Field Test >. Scenerio: 5 drones tracking a 5 component moving GMM plume. They compared methods:. > Lloyd (static).> Lloyd (dynamic).> GMM Controller (this paper). They all performed similarly when the plume was stationary. However when the plume began to

< Control Law Summary >. The controller has two terms:. 1. A weighted average of GMM source velocities (which anticipates motion). 2. A centroid tracking term with gain B (pulls the agent toward current center of mass). 🧵5/7

< The Setup >. Each agent:. > Knows the velocities of the GMM sources.> Computes a Voronoi region from local neighbors.> Tracks its own region's density-weighted centroid. The objective is to minimize the total weighted distance from each point in the domain to its nearest agent

< The New Approach >. This paper proposes a new approach. a time-varying controller for dyanamic coverage modeled using Gaussian Mixture Models. Each GMM component moves with known velocity, mimicking drifting heat, has, or information fields. This allows there to be less

< Classical Coverage Control and Limitations >. Traditional coverage control like Lloyd's algorithm, deploys agents to density-weighted centroids. But when density functions move, old methods break. > They lag the target.> Ignore time dependency altogether.> Often rely on

< Single-Agent Coverage Algorithm > . How do we guide a robot agent or swarm over a shifting terrain of 'importance'? . When coverage demands evolve with time, like tracking chemical plumes or dynamic hotspots, static control laws for short. In this new paper, "GMM-Based

<Why This Matters >. This isn't just an academic benchmark win. A robust model that turns any photo into a metric 3D model is a foundational tool. This could supercharge applications in:. > Augmented Reality.> Robotics & SLAM.> Autonomous Driving.> 3D Content Creation & Image

<The Results: Seeing is Believing>. The results speak for themselves. In side-by-side comparisons, MoGe-2's output is consistently more accurate and detailed. Look at the car: the ground truth is 2.94m, MoGe-2 predicts 2.98m. Compare that to competitors who are either off on

<How It Works: The Architecture>. MoGe-2 builds on the powerful DINOv2 vision foundation model. The key insight is its decoupled architecture. The main network branch focuses on producing a scale-less (affine-invariant) point map, preserving accurate relative geometry. Meanwhile,

<The Challenge: Scale & Sharpness>. Getting 3D from one image is tricky. Is it a toy car or a real one? Most models don't know (scale ambiguity). And real-world training data is often noisy, leading to blurry 3D outputs. MoGe-2 tackles this with two key ideas:. > Decoupled

< MoGe-2: Accurate 3D Metric Reconstruction from Monocular Image > . Researchers just dropped a new SOTA model that reconstructs a full 3D scene from a single 2D image. The real game-changing contribution is the ability to:. > Get geometric shape correct.> Provide accurate world

< TLDR >. Drones can now safely coordinate mid-flight without leaking any route data. Using encrypted math over encrypted paths. Algorithm runs on raspberry pi compute . This is how we set secure, scalable, multi-agent drone networks . 🧵8/8.

< Benchmarks >. They tested on raspberry pi's and got 30% faster than previous SOTA and 10x less network bandwidth. This system scales to many drones, doesn't require global map, works with arbitrary GPS routes, and can adapt real time. It'll be ideal for the future of aerial

< Security Breakdown > . Even if a malicious drone logs all collisions:. > It still can't pinpoint the other's path.> No time, direction, or speed is exposed. Brute force attacks are mitigated by:. > Range limits.> Battery constraints.> Temporal mismatch. 🧵6/8.

< What Happens Mid Flight >. Every drone monitors its Protocol Initiation Range. If another drone enters into their airspace. > They run an encrypted intersection protocol.> If any path segments overlap -> one drone shifts altitude temporarily .> No trajectory is recomputed or

< Why Homomorphic Encryption> > . Homomorphic encryption lets you do math on encrypted data. No decryption needed. No data revealed. The researchers use:. > Paillier encryption for additive operations.> A two-party protocol for encrypted multiplication. This enables comparing GPS

< The Setup >. Two drones (e.g from Amazon and UPS) enter the same airspace. Instead of exchanging routes, each drone:. > Encrypts its path as a set of 2D line segments.> Compares them homomorphically using the other's public key.> Detects collision without ever seeing the

< Privacy Preserving Coordination >. Traditional collision avoidance requires global route sharing, that's a non-starter when privacy is on the line. Flight paths expose:. > Customer address patterns.> Business ops intel.> Higher-value drop off zones. This paper proposes a fix: