Position/Course Control In Three Dimensions TRIZ Cases

Adaptive locked and unlocked control states use attitude sensing to ease one-handed drone control while protecting safety in risky flight scenarios.

Image-based path control lets UAVs track changing objects, avoid obstacles, and keep navigating when GPS signals are weak.

Logged drone routes let untrained operators repeat site inspections consistently, reducing physical visits and supporting 3D site certification.

When routes intersect, user profile and movement history guide more convenient alternative destinations for delivery or patrol tasks.

Altitude-based deviation thresholds guide phased UAV landing decisions, improving alignment and landing stability under wind and positioning errors.

Predictive boundaries with confidence bands help coordinate mobile machines, avoid interference, and improve route planning in dynamic worksites.

Real-time velocity fusion and displacement tracking help UAVs return more accurately to takeoff points when GPS signals are weak or degraded.

Independent rotor control units use shared control laws and source switching to keep multi-rotor aircraft stable during communication failures.

Event-triggered drone control handover uses prioritized authorized entities to keep control continuous, secure, and limited to one operator.

Multiple projected trajectories are screened against terrain and envelope threats to trigger timely aircraft protection with fewer false alarms.

Preloaded 3D trajectories and a shared timecode let multiple drones stay synchronized with low bandwidth and no fixed sensors.

Sensor-driven formation changes let multiple aerial vehicles coordinate nonuniform flight paths, improving safety and mission efficiency.

Automatically sets a moving body's crane inspection route from structure, posture, position, and direction data to cut manual setup effort.

Accelerometer feedback alerts help follower aircraft stay in beneficial updraft while avoiding excessive wingtip-vortex vibration and instability.

A split pilot and control endpoint architecture keeps long-distance drone commands and video links near real time with latency under 150 ms.

Leader-guided formation changes let aerial vehicles avoid obstacles, stay synchronized, and reduce onboard sensing complexity.

UWB-equipped rescue UAVs detect survivors through debris and relay location and vital-sign data to command centers, reducing responder exposure.

Multiple projected flight paths are checked against unified threat triggers, enabling adaptive recovery action with fewer false alarms.

AMF-based distance monitoring alerts the control apparatus as aircraft near no-fly zones, avoiding geofence installation and improving prevention.

Broad-area vehicles first map a region, then higher-precision vehicles target uncovered segments to cut coverage time and resource use.

An auxiliary mobile unit sends environment data to an external server, combining viewpoints to improve autonomous navigation and self-location.

Dual-frequency sensing uses intermodulation amplitudes to measure surface distance and trigger real-time electric aircraft adjustments.

A selected drone makes a visible identification flight track, letting operators confirm the control target without looking down at the controller.

IMU lift-off data lets connected UAVs estimate relative orientation and distance, enabling fast formation control without indoor positioning.

Dynamic waypoint networks and traffic corridors route UAVs around congestion and collision risks while keeping low-altitude flights accountable.

Precomputed collision-probability maps encode aerial routes as hierarchical 3D shapes, improving autonomous navigation in complex airspace.

Sensor triangulation replaces heavy image processing to detect obstacles and plan shorter autonomous vehicle paths with clearance.

Wireless warning signals let UAVs withdraw and reroute around restricted areas, improving real-time traffic safety without static maps.

Multiple autonomous vehicle types are iteratively assigned to uncovered segments to balance coverage speed, precision, and resource use.

Independent cruise Mach and descent speed calculations help aircraft meet RTA constraints with better speed margins and fuel use.

An accessory robot sends environmental data to a server, improving map accuracy and obstacle detection while reducing onboard processing load.

A drone re-transmits radar signals with controlled flight and delay profiles to simulate realistic target trajectories for radar training and calibration.

Parallel trajectory calculation lets aircraft switch to a revised flight plan without autopilot interruption or unexpected course changes.

Radar-guided altitude and trajectory adjustment helps agricultural UAVs avoid false ground obstacles on hills, slopes, and terraces.

Selective ray-casting grid updates cut map-processing load while keeping obstacle-aware route planning responsive for movable platforms.

Dynamic virtual fences adjust UAV separation from stationary and moving objects using object class, motion, and delay data.

Parallel trajectory recalculation lets aircraft switch to updated flight plans without interrupting autopilot control or causing abrupt course changes.

By classifying intentional formation flight versus collision risk, this approach adapts safety spacing and generates evasive actions.

Remote server creates a 3D virtual model to guide lightweight drones through indoor environments.

Autonomous UAV navigation system guides air vehicles toward atmospheric thermals using onboard sensor data and discretized grid maps.

Randomized path generation prevents intruder pattern recognition while maintaining continuous coverage without fixed cameras.

A range estimation device performs own-ship maneuvering based on passive sensor data to determine target distance.

Autonomous UAV system generates a dynamic trombone landing trajectory to guide the aircraft to a safe touchdown point.

A flight management device displays temporal situations across multiple waypoints simultaneously on integrated timelines.

Centralized monitoring system manages multiple unmanned aerial vehicles during transmission line inspections.

Onboard sensor fusion determines follower velocity for formation maintenance, eliminating pilot workload from manual tracking and inter-aircraft communication.