Navigational Calculation Instruments TRIZ Cases

Contour extraction and dilation identify navigation target points, reducing wall-following time and speeding robot exploration in unknown areas.

Coordinate transformation and azimuth control help a robot find the farthest safe path through narrow passages despite sensor and environment errors.

Fixed optical identifiers help autonomous mobile robots localize across buildings and levels, improving navigation range and reliability.

An acoustic inertial dead reckoning reference uses Doppler velocity log data and Kalman filtering to hold vessel station when GNSS is unreliable.

Tow-tug sensor fusion builds a 3D aircraft safeguarding box to detect collision risks during towing without modifying the aircraft.

Boundary-line grouping and reference-ratio selection reorient robot maps consistently, improving navigation display and user understanding.

Manhattan orientation constraints in SLAM reduce pose drift and improve mobile agent map accuracy without compass measurements.

Aerial imaging with marine video analytics expands fish search beyond vessel limits while automating underwater object detection and identification.

A single camera estimates object distance, height, and wind speed to stabilize autonomous drone flight and navigate waypoints without GPS.

Dynamic AGV routing replaces fixed conveyors to avoid warehouse clogging, reduce collisions, and speed pallet transport.

Dynamic zone boundary adjustment speeds multi-robot travel planning in narrow movement areas while preventing collisions and deadlocks.

Sensor fusion detects runway markings, centerlines, and lateral offset to guide aircraft taxiing when visibility is poor or markings are missing.

Multiple candidate start positions and orientations improve self-localization success and reduce unnecessary movement before cleaning or conveyance.

Another transporter measures load-carrying vehicle size to avoid blind spots and improve route planning in narrow passages.

Rough touch input is refined with ranging and image matching to set quay-line and docking target information more accurately.

Real-time hub data triggers mobile kiosks to move to crowded areas, cutting manual response delays during disruptions.

When sunlight enters the camera field, steering is adjusted to keep glare out of view and maintain stable automated vessel operation.

Side-mounted remote sensors let a mobile robot map room boundaries without rotary LiDAR, cutting mechanism complexity and mold constraints.

Real-time cloud analysis of UAV and robot sensor data updates control parameters to adapt to wind and weather with better navigation precision.

Stationary QR codes or radio beacons let silo work vehicles determine position accurately when GPS reception is unavailable or incomplete.

Dense and sparse onboard maps build a global graph so a UAV can avoid obstacles and return safely after signal loss or low battery.

A distance-based lower limit on object presence ranges prevents map gaps near the hull and improves docking-time object detection.

Integrated GNSS, compass, and IMU data correct radar motion errors to improve Doppler imagery and target radial speed measurement.

Two detection paths compare distance differences to correct ambient light distortion and improve moving vehicle docking accuracy.

A sensor-equipped robotic platform builds a 3D hoistway model to replace slow manual shaft inspection and improve installation precision.

Pareto-ranked aircraft routes balance acoustic impact and fuel use while meeting geographic and operational constraints.

Remote sensor beacons build a virtual harbor map at a control center, improving ship navigation in blind spots without boarding pilots.

Grid-based mapping and priority path planning let a disinfection robot build maps and disinfect continuously without separate manual steps.

By using future separation distance and ship size instead of simultaneous existence probability, collision risk zones stay realistic with lower calculation load.

RTK-GPS perimeter training replaces beacon layouts for autonomous greens mowers, reducing location ambiguity and installation effort.

Zone-based rail modeling speeds route calculation for container handling vehicles while preserving grid-aware accuracy in large automated storage systems.

Stored key-off orientation lets a work vehicle resume automatic steering immediately after key-on, even at extremely low speed.

Positioning data volume alerts help paving operators detect low GNSS reliability early and maintain construction accuracy during automatic control.

Ranks and prunes aircraft route options across noise, fuel, geography, and no-fly constraints to cut computation in airspace planning.

A tentative obstacle-free route is refined with adaptive via-point precision, cutting calculation time while preserving obstacle avoidance.

By shifting the steering reference point to the travel-side axle line, articulated vehicles track target routes more accurately and suppress meandering.

Reduced-order closed-loop models cut trajectory prediction latency, helping aircraft avoid terrain obstacles in real time.

Pre-positioned object data, road rules, and obstacle sensing help warehouse vehicles clear contested intersections with fewer delays and deadlocks.

Semantic cost maps let robots classify pedestrians and other objects, avoiding freezes and improving safe, adaptive movement in crowded spaces.

Coarse site-section detection with markings or signals guides fine total-station or LIDAR positioning to avoid errors in symmetric indoor construction areas.

VR-based 3D path planning lets UAV operators visualize obstacles in first person and adjust routes for more accurate complex flights.

Stored field area data lets different autonomous work devices reuse route boundaries, cutting repeated perimeter setup and prep time.

Target markers and a scanning module keep garden tools accurately positioned in GNSS-shadowed areas where satellite signals are unreliable.

Curvature-based path straightening and obstacle expansion help robots pass tight spaces with lower collision risk despite sensor noise.

Precomputed offline graphs and lightweight online updates speed autonomous path planning while preserving robust avoidance in partially known environments.

Adaptive PRM sampling and two-way search cut AMR path planning time and computing load while preserving global path quality.

A coarse-to-fine grid map pathfinding approach cuts search time and memory use while refining obstacle clearance in large robot workspaces.

Depth imaging and coordinate mapping correct AGV positioning errors in large work areas, enabling accurate pallet alignment and fetching.

Voronoi-based strategic waypoints and autonomous UAV roles reduce predictable patrol patterns while improving real-time threat detection.

Routes become shorter by excluding low-certainty map obstacles, while finer detection checks excluded objects during robot movement.