Navigational Calculation Instruments TRIZ Cases

Preplanned field and road routes with real-time obstacle updates help self-driving agricultural machines travel efficiently between fields.

Sensors track a moving landing area, predict surface attitude, and time descent so an unmanned aircraft can land reliably on mobile structures.

External sensors pre-acquire target position and orientation so moving objects can generate pickup routes faster with lower calculation load.

Sensor-based pre-flight conditioning checks expected outputs and powers aircraft systems from an auxiliary supply to improve readiness.

ML-generated location anchors help warehouse drones localize in repetitive aisles, plan efficient scan routes, and reduce inventory errors.

Waypoint-based switching between GPS and vision position estimation helps drones balance flight speed, range, and accuracy across mission phases.

Annotated maps and server-generated scan constraints help robots avoid redundant coverage, cut route training time, and support task-specific paths.

Presearched candidate routes are fused with heuristic search so robots can pass narrow obstacle channels despite map marking errors.

Sensor-based trajectory switching lets a mobile object detect target deviation and replan its approach for accurate position and attitude alignment.

Removing noncritical waypoints with a SAT solver keeps multi-robot traversal conflict-free while lowering central planning load.

Autonomous UAV deployment and sensor-based route planning cut cellular tower inspection delays, crew scheduling dependence, and site costs.

Point-cloud region checks help UAVs detect rooftop and cliff edge gaps, avoiding unsafe landing zones or stopping descent.

Vision, infrared, LIDAR, and IMU feedback guide propulsion for precise marine vessel docking and position holding in adverse weather.

Flight plan pairing identifies leader-follower aircraft, adjusts routes and timing, and validates net fuel savings from wake vortex formation flight.

Grouped optical markers encode each location through combined codes, enabling scalable indoor vehicle localization without GPS.

Pre-assessing landing areas with vehicle and mobile images helps drones avoid obstacles and land safely at remote sites.

AMRs can start material flow tasks with partial route data, then receive and prioritize delayed destination inputs during execution.

Prebuilt feasible-transition graphs with real-time edge updates cut planning time while preserving near-optimal routes around dynamic obstacles.

Dynamic lane assignment and route control let multiple pallet movers transport loads concurrently while reducing clogging and collisions.

A floating camera-based mapper uses VSLAM and image processing to inspect pipes and manholes remotely, improving defect location accuracy and safety.

Tessellated terrain cells are grouped into feasible landing sub-regions, ranked by cost, and used to guide adaptive robot landings.

A symmetric inclination-angle transform reduces asymmetric aerial vehicle heading control behavior and improves precision and control intuitiveness.

RRT-based frontier filtering ranks passable, high-revenue targets so robots can map unknown regions faster with lower calculation cost.

Manhattan orientation constraints in graph-based SLAM reduce mobile agent heading drift and improve pose mapping without direct orientation sensors.

Limited-range sensors detect row ends at field edges, then infer missing path segments for accurate agricultural vehicle navigation.

Pre-storing waypoint paths on the terminal before UAV connection cuts idle battery use and preserves flight time for mission execution.

Resolution-independent IRIS seeding builds contiguous convex free-space for faster robot path planning with better coverage in cluttered indoor environments.

Gyroscope, accelerometer, and barometer data help recover vehicle turns when GPS points are inaccurate, improving path tracking precision.

Maps dynamic and static obstacles into 2D+t interception polygons to compute conflict-free vehicle paths with fewer waypoints and faster planning.

A smart index recalculates trajectory, speed, and thrust in flight to balance cost, noise, and environmental objectives across airline operations.

Virtual reality flight planning adds height-aware path generation and first-person verification for accurate UAV navigation in complex spaces.

Stored field data and user-selected priorities generate suitable autonomous work vehicle routes without complex manual setting input.

ML-trained flight paths let aerial cameras track movement and keep video aligned with audio for hands-free dance capture.

Multi-sensor landmark detection and holonomic drive enable beacon-free construction site navigation with less supervision and better positioning.

Position deviations from batches of underwater vehicles are used to build accurate water current profiles without ADCP side-lobe gaps or added ocean noise.

AR and BIM-guided monitoring pinpoints abnormal field devices and routes maintenance staff faster while reducing manual inspection exposure.

Accounts for airflow interference and nonholonomic motion limits to plan passable rescue ship routes with lower risk and better efficiency.

By selecting the center of a map overlap area, the robot sets a reliable start point inside user-defined cleaning limits.

Sensors and onboard control let excavation vehicles detect obstacles, compare pass-through or removal routes, and keep digging with less manual input.

An onboard data manager offloads waypoint planning from the flight controller, enabling real-time UAV mission changes and path optimization.

LTE carrier-phase measurements with EKF fusion and cycle slip detection enable sub-meter UAV navigation where GNSS degrades in urban canyons.

Waypoint pruning and terrain-based smoothing create a stable aircraft altitude profile that preserves clearance while reducing fuel use and discomfort.

A drone uses dynamic spatial maps and object status updates to reroute indoors when doors, windows, or other obstacles change.

Real-time trolley position feedback and predictive simulation enable coordinated mission assignment that cuts warehouse handling time.

Estimated 3D wind conditions are used to compare drone and eVTOL routes by flight difficulty and economic efficiency.

By combining elevation data, no-fly polygons, and image frames, APAS maps UAV coverage and viewing areas for complex terrain.

By combining no-fly zone and elevation data into an operational map, APAS helps UAVs estimate coverage and viewing areas across complex terrain.

Coordinated AGVs bring cartons and modular storage units to pick cells, cutting manual cart hauling, fatigue, and delivery delays.

A STARI final approach plus obstacle-avoiding lateral path cuts landing trajectory computing time while preserving flyability and energy dissipation.

Separating horizontal and vertical Dubins paths enables accurate UAV 3D trajectory planning while keeping heading aligned at the target waypoint.