Navigational Calculation Instruments TRIZ Cases

A drone maps sensor positions across navigable property areas, enabling automatic, context-aware labels that replace slow manual mapping.

Pre-mapped seabed data and predicted trajectory are linked to automatically adjust an underwater vehicle path and reduce collision risk.

Preliminary boundary detection lets an agricultural machine map unknown field edges, then auto-plan routes that cut missed areas, travel distance, and energy use.

An onboard angle sensor and preloaded tether let a multicopter hold position and navigate reliably when GPS or radio links are disrupted.

Manual traversal maps part of an unknown field, then route planning generates control data to cut gaps, travel time, energy use, and soil compaction.

A subset of agents shares anomaly sensing and map matching to localize the full swarm in GPS-denied environments with lower SWaP+C.

A fade function and reduced-order closed-loop model cut trajectory latency, enabling faster aircraft obstacle avoidance without pilot input.

Predicted illuminance limit times let drone control adjust scheduled flight duration before brightness drops below safe operating thresholds.

When server links drop inside GPS-denied datacenters, a UAV uses visual tags to keep navigating and collecting monitoring data.

Dynamic flight corridors and real-time mission updates let autonomous UAVs deliver payloads reliably with less infrastructure and human error.

Task-triggered shelf label beacons enable accurate indoor positioning only when needed, cutting power use and extending label service life.

Active turning or spiral motion reduces GNSS multipath interference, improving self-position stability for mobile body control.

Grid-based cleaning maps help pool robots mark reachable blocks, cut wasted movement, improve coverage, and lower energy use.

Weighted fusion of camera, GNSS, and IMU data improves vehicle heading at standstill and low speed when drift and GNSS quality limit steering.

By optimizing lane orientation, positioning, and traversal order, this case cuts turning maneuvers and field processing time.

Precomputed path graphs cut runtime planning cost while preserving near-optimal routing and adaptation to dynamic obstacles.

A 3D virtual route interface replaces flat map input so users can set and adjust moving-body paths more intuitively and accurately.

Preplanned 3D waypoints and snap-minimized trajectories let indoor UAVs fly fast in open areas, then slow and adjust near obstacles.

By extending iso-displacement curves across flight levels, this case improves aircraft mission trajectory planning to cut flight time and fuel use.

Remote beacons and a control centre build a real-time virtual waterway map, helping ships navigate blind spots without pilot boarding.

Automated waypoint generation and obstacle-clearance guidance help rotorcraft fly precise offshore approaches with lower pilot workload.

Thermal sensor feedback lets a UAV reroute around flare stack heat, preserving inspection coverage while avoiding thermal damage.

Manual travel data is cleaned by removing oncoming-vehicle avoidance segments, producing a more appropriate automated route for agricultural machines.

Terrain-aware cost maps from satellite elevation and remote sensing data guide off-road unmanned vehicles around rugged ground and steep slopes.

Sonar micronavigation fused with INS and Kalman filtering cuts bias and drift for more accurate seafloor navigation without GPS.

Satellite-guided source detection lets robots find suitable natural energy sources and recharge by electrode insertion, extending runtime.

A display-guided setup lets a robotic garden tool map the perimeter with odometry and save remote start points for more efficient area coverage.

In-flight reference vector measurements estimate sensor zero-point offset, improving UAV heading and posture accuracy without manual calibration.

Egocentric video from a shopper's mobile terminal cuts fixed-camera load while supporting reliable item detection and accessible unmanned checkout.

A three-stage path control approach uses sensor feedback and optimization to align a mobile object with a shifted target accurately.

Miniaturized UAV magnetometers and LiDAR locate hidden abandoned wells faster than ground inspection by detecting casing magnetic anomalies.

Laser rangefinders fit passive landmark shapes to localize a mobility platform accurately without external beacons or GPS.

Signal tracking plus LIDAR and ultrasonic sensing let the caddy follow a golfer at set distance while avoiding collisions on the course.

Image analysis and building data classify rooftop type and durability to mark safe, risky, or no-landing zones for UAV rooftop landings.

Stereo-camera point clouds and Hough row detection let agricultural machines steer accurately when plant locations are unknown.

A first-pass drone scan flags areas of concern, then a return-path rescan captures denser image data without overloading facility inspection.

Priority-based route generation lets autonomous work vehicle users choose suitable travel paths without complex manual settings, reducing input time and errors.

Feasible ground cells are clustered and ranked by cost to select robot landing sub-regions for guidance, navigation, and control.

Grouped optical markers let a simple camera uniquely identify indoor positions, enabling scalable vehicle self-localization without GPS or SLAM.

Mobile bodies improve self-position accuracy by moving into view of each other and using relative position data without external correction devices.

Sensor output is adjusted to communication rate so remote agricultural machines keep usable image and LiDAR data under congestion.

Area-specific AI models are loaded from map and sensor context to improve object recognition while reducing memory and processing demand.

Pre-flight authorization checks block UAV missions with defective parts, overdue maintenance, or temporary airspace restrictions.

Dynamic mission updates and distribution-center selection help UAV fleets deliver, map, or surveil with lower cost and less fixed infrastructure.

SLAM-based 3D maps let drones switch from GPS to local route search near the destination, improving indoor delivery accuracy and flexibility.

Validated field-edge coordinates, altitude checks, and polygon approximation help drones avoid obstacles and generate safer routes.

IMU, LiDAR, and GPS fusion corrects motion-distorted point clouds to improve AMR positioning indoors and outdoors.

Maps HIRF sources and vehicle radiation tolerance into stand-off zones, enabling safer routes with less shielding weight and cost.

Real-time virtual approved pathways guide shared automated vehicles around changing obstacles and other movers with less onboard sensing load.

Trajectory planning uses disturbance and uncertainty estimates from wind, waves, and currents to generate collision-safe vessel routes near obstacles.