Control Below is a “computationalization” of this exact scene the same way we treated the John Deere: identify rigid bodies, work volumes, constraints, flows, and risk envelopes—then encode them as Elixir/Nx tensors so autonomous agents can monitor, predict conflicts, and emit actionable structures for operators + PMs + planners. --- 1) What’s in the scene (objects + roles) From the photo, the work zone contains these primary actors: Mobile equipment (rigid bodies) Wheel loader (foreground, right lane) Several tracked excavators (center + left + right), at least one with a hydraulic breaker (hammer) Possibly a dozer/loader further left (hard to confirm), plus additional excavators in the distance Environment (mostly static, but evolving) Demolition corridor (the cleared roadway trench / median area) Rubble piles / windrows of broken concrete (centerline and edges) Concrete barrier segments (left side, some knocked/tilted) Embankment / slope (right) Bridge / overpass structure (right side top) Dust plume (left), representing active breaking/loading Human-critical zones (implied) Equipment operator visibility limits No-go buffers around the breaker Truck loading zones (not visible here, but typically present) --- 2) Coordinate frame and world discretization To make this a tensor world, pick a single coordinate system: World frame W: +X = along the roadway (toward background) +Y = across roadway (left ↔ right) +Z = vertical Then choose one or both representations: A) Continuous state (for kinematics + collision prediction) Store each object as parametric geometry + pose. B) Grid / voxel map (for planning + semantic occupancy) Store a 2D bird’s-eye raster (or sparse voxel) with channels like: occupancy, rubble height, traversability, dust/visibility, restricted zones, etc. In practice, you’ll keep both: continuous for rigid-body interactions grid for terrain + planning + city-planner abstractions --- 3) Mechanical geometries to extract (per machine) Each machine becomes a structured record of: Base pose (position + yaw) Footprint geometry (oriented bounding box / convex hull) Articulated linkage state (boom/stick/bucket or breaker) Swept volumes (where it can move in the next N seconds) Dynamic constraints (speed, slew rate, arm reach, stopping distance) Excavator model (minimal but useful) For excavator i: Base: p_base[i] = [x, y, z] yaw[i] Links (angles): θ_boom[i], θ_stick[i], θ_tool[i] Link lengths (calibrated per model; can be approximate at first): L_boom[i], L_stick[i], L_tool[i] Tool tip position: p_tip[i] = FK(p_base, yaw, θ, L) (forward kinematics) Even if your first pass is “approximate geometry,” this already enables: reach envelopes swing hazards proximity checks against other assets and boundaries Wheel loader model Base pose + yaw Articulated bucket pose (often simpler than excavator) Forward visibility cone (important hazard feature) Stopping distance / braking envelope --- 4) Scene constraints (the stuff that turns geometry into “control”) This is the heart of what you’re asking: constraints that agents can enforce. Hard constraints (must never violate) Collision avoidance: equipment ↔ equipment, equipment ↔ structure, equipment ↔ barriers No-go around breaker: exclusion radius + fragmentation projection sector Edge protection: keep heavy equipment away from slope crest / undermined edges Overpass safety envelope: breaker operations near bridge abutments require special rules Soft constraints (optimize / prefer) Minimize travel across loose rubble Keep loader routes clear Keep excavator swing arcs out of shared lanes Dust-aware routing (visibility / respiratory risk) Work-process constraints (temporal & logistical) Breaking → piling → loading → hauling is a pipeline You want agents that detect pipeline stalls, e.g.: breaker running but no loader/trucks staged loader queuing with no clear dumping zone rubble pile exceeding target height/width constraints --- 5) Tensor data structures (what you actually feed Nx) You want tensors that are: compact batchable (many machines, many timesteps) differentiable enough for learning/inference (even if the simulator isn’t fully differentiable) Core tensors A) Rigid bodies (N machines) pose :: f32[N, 7] → [x,y,z, qx,qy,qz,qw] (or yaw-only if you want) vel :: f32[N, 3] → linear velocity omega :: f32[N, 3] → angular velocity obb :: f32[N, 3] → half-extents of oriented bounding box (length/width/height) B) Articulations (excavators subset) joint_angles :: f32[N, J] (J=3 for boom/stick/tool) link_lengths :: f32[N, J] C) Work envelopes (prediction horizon T) swept_occupancy :: u8[T, H, W] or f32[T,H,W] or continuous: swept_polygons :: f32[T, N, K, 2] (K polygon points) D) Terrain & materials (grid) grid_occ :: u8[H, W] (occupied) grid_height :: f32[H, W] (rubble height / elevation) grid_class :: u8[C, H, W] one-hot semantic classes: intact pavement, broken slab, loose rubble, barrier, slope, structure, etc. grid_risk :: f32[H, W] learned/derived risk scalar E) Constraints as tensors (so agents can “score” violations) min_sep_matrix :: f32[N, N] desired separation distances no_go_zones :: f32[Z, K, 2] polygons (Z zones, K points) lane_corridors :: f32[L, K, 2] “allowed travel” polygons --- 6) What agents do with this (multi-agent control loop) Think of this as a continuous “safety + coordination compiler”: Agent classes (you’ll probably want these) 1. Perception Agent updates object list + poses + rubble grid (from cameras/LiDAR/drone/phone pics) 2. Safety Agent checks imminent collisions, swing intrusions, edge risks, visibility hazards 3. Flow Agent (Production) detects bottlenecks; recommends staging positions and sequence 4. Geotech/Structure Agent flags undermining risk near slopes/abutments; watches rubble loading on edges 5. Planner Agent produces “next best actions” as structured tasks (not just text) Output structures for humans (operators / PMs / planners) Operator alerts: “excavator swing arc crossing loader route in 12s” PM dashboard objects: queue length, utilization, cycle time map overlays: blocked corridors, no-go violations, hazard hot spots City planner abstractions: demolition progress surfaces dust/noise exposure contours lane closure timeline predictions All of these are derivable from the same tensor state. --- 7) A concrete Nx-centric schema (Elixir-ish) Not full code, but a “shape contract” you can implement immediately: %World{ # Machines (N) pose: Nx.tensor({n, 7}, type: :f32), # x y z qx qy qz qw vel: Nx.tensor({n, 3}, type: :f32), obb: Nx.tensor({n, 3}, type: :f32), # half extents # Articulations (only for excavators; others can be zero-masked) joint_angles: Nx.tensor({n, 3}, type: :f32), link_lengths: Nx.tensor({n, 3}, type: :f32), is_excavator: Nx.tensor({n}, type: :u8), # Terrain grid (H x W) grid_height: Nx.tensor({h, w}, type: :f32), grid_class: Nx.tensor({c, h, w}, type: :u8), grid_risk: Nx.tensor({h, w}, type: :f32), # Constraints min_sep: Nx.tensor({n, n}, type: :f32), zones: Nx.tensor({z, k, 2}, type: :f32), # polygons in world XY # Derived per tick hazard_events: Nx.tensor({e, d}, type: :f32) # structured event rows } Key idea: everything is either a tensor, or an index into a tensor, so agents can batch-evaluate and learn. --- 8) Hazards specific to this scene (what to flag) Based on what’s visible: Breaker zone hazard (right-center excavator with hammer): flying debris sector dust/visibility reduction exclusion radius for other machines and pedestrians Mixed traffic in a narrow corridor multiple excavators working close together → swing overlap risk loader traveling in the same corridor → right-of-way conflicts Edge / slope undermining risk rubble removal can undercut stable surfaces agents should track “support confidence” in grid cells near edges Barrier displacement knocked/tilted barrier segments mean your prior lane boundary is invalid needs to be in the world model as “dynamic boundary objects” --- 9) How to get the geometry from images (pragmatic bootstrapping) You don’t need perfect CV on day 1. Start with layered fidelity: Phase 1 (fast) Human-assisted tagging: click machine centers + headings in a UI Use approximate OBB footprints + fixed link lengths Update state at ~1–2 Hz Phase 2 Add instance segmentation + keypoints for excavator booms Estimate joint angles from keypoints Track motion with Kalman filter (in tensor form) Phase 3 Fuse with drone photogrammetry or site LiDAR for accurate rubble grid + volumes Predict future occupancy and cycle times --- 10) The “control” abstraction What you ultimately want is: a control plane that speaks construction. So define a small set of canonical task objects agents can emit: {:break, excavator_id, region_polygon, target_fragment_size} {:pile, excavator_id, source_region, pile_region} {:load, loader_id, pile_region, truck_staging_region} {:clear_route, machine_id, corridor_polygon} {:pause, machine_id, reason_code, hazard_ref} These tasks become: prompts to operators, schedule objects for PMs, and simulation constraints for planners. --- If you want, I can turn this into a single “World Model Spec” you can paste into your repo: tensor shapes, constraint equations (distance-to-poly, OBB overlap, swing arc occupancy), and a minimal Nx step function that produces hazard_events each tick. That’s exactly the right framing: this image is now a compiled world-state, not a photo. What you’re looking at is essentially a live visualization of these Elixir/Nx structures rendered back onto reality: --- 1. Machine Rigid Bodies → pose / obb / swept_volume Each machine is represented as: On the image: Yellow wireframes = obb Transparent prisms = predicted swept_volume Dashed red arcs = swing envelopes from forward kinematics These are literally the tensors your agents would use for: collision prediction blind-spot inference right-of-way reasoning --- 2. Articulated Links → joint_angles / link_lengths For each excavator: On the image: Red dotted segments = tool-tip forward kinematics Green translucent cones = reachable work volume Orange cones = danger swept volume (breaker) That orange breaker cone is: Which feeds directly into: --- 3. Terrain Grid → grid_height / grid_class / grid_risk The entire roadway is discretized as: On the image: Blue-green mesh = traversable Yellow = degraded stability Red = high-risk rubble / dust / undermined edge This lets agents compute: Which is how you detect: slope collapse risk overloading near abutments edge creep from repeated loading --- 4. Process Flow Volumes → work_regions These translucent boxes are not geometry — they are logistical tensors: This is how agents reason about: pipeline stalls where to stage loaders when to pause breakers because no downstream capacity exists It’s the difference between: > “Machine is safe” and “Operation is economically optimal” --- 5. Constraint Matrices (the real control layer) Hidden but conceptually present: Which produce: These are what your system actually emits. Not “alerts”, but structured machine-readable risk objects. --- The key conceptual shift (this is the big one) This image is not “annotated”. It is compiled. You have: Reality → Sensors → Tensors → Constraints → Agents → Tasks → Humans And this visualization is just: > WorldState |> render() --- Why this matters for your vision This is exactly the kind of domain where autonomous systems beat humans: Not because of robotics, but because multi-agent constraint satisfaction over real geometry is intractable for human cognition. Humans see: > “Busy demolition site” Your system sees: Which means: You don’t just detect accidents. You predict counterfactual futures.