Electromagnetic Wave Reradiation TRIZ Cases

By matching stored and current obstacle edge paths, the robot corrects odometry drift without cameras or laser radars.

Angled ToF sensors create redundant detection regions that reduce image-sensor cost and processing load in robot mapping and localization.

Sensor fusion with UWB, TOF, and lidar lets the cart follow users, avoid obstacles, and stay stable on inclines through battery placement.

Specific pulse spacing lets LiDAR send rapid measurement pulses without aliasing, extending range while preserving unambiguous transit time.

Selective LIDAR channel activation creates multi-resolution point clouds that ease autonomous vehicle processing load without losing detail in critical areas.

Dynamic angular resolution targets reflective features with higher pulse rate or beam slew control to improve obstacle detection without full-scan overhead.

Multiple location sources are weighted by reliability to correct odometry drift and unstable visual cues in autonomous navigation.

Electro-optic steering with polarizable particles replaces moving scanners to widen field of regard and raise scan speed with lower power.

Prism-guided ordinary total stations replace slow gyroscope north seeking, enabling fast, precise 3D coordinate measurement in harsh coal faces.

Driving-state-based mode selection adjusts scan parameters to improve vehicle distance measurement accuracy and LIDAR relevance.

Depth-coded 3D roadway targets improve long-range lidar identification under short exposure times while limiting processing demands.

Mobile calibration targets let autonomous vehicles recalibrate multiple sensors in the field, cutting downtime and restoring alignment.

AM or TOF modulation added to FMCW LiDAR separates range and Doppler data, reducing short-range aliasing and wait time.

Adaptive pulse rate and beam slew control sharpen LiDAR point clouds around reflective features without slowing full-scene scanning.

Gradient-based sensitivity analysis identifies calibration parameters that most affect prediction outputs, improving robustness to sensor mismatch.

LiDAR improves docking-object distance accuracy while short-range sonar detects nearby buoys and stakes to judge docking feasibility.

Individualized control signals compensate spring stiffness variation in MEMS micro-mirror arrays to improve rotation uniformity and imaging resolution.

Voxel occupancy from LiDAR plus camera semantics builds a 3D map for autonomous positioning without complex object recognition retraining.

Dual lasers injection-locked to one electro-optic resonator cut speckle noise and improve LiDAR SNR and range in rain, snow, and fog.

Masking disturbance waves and limiting scan sectors helps unmanned movers avoid false object detection when laser beams overlap.

Static-object radar clustering improves host vehicle velocity estimation, separating moving targets to support more reliable automated driving.

Portable luminance command markers let an autonomous cart redefine no-entry areas quickly without floor tags or route recalculation.

Triggered optical targets align with LIDAR pulses to automate vehicle sensor calibration, reducing manual setup cost and complexity.

Optical sensors count grid track crossings and gauge the next crossing distance, giving remotely operated vehicles simpler, more precise route control.

Meshed LiDAR and camera data give AI-based ramp monitoring better awareness of aircraft, vehicles, personnel, and static hazards in low visibility.

Multiple laser rangefinders, including motor-integrated sensors, expand object detection and enable autonomous path changes to avoid collisions.

Orientation feedback adjusts each oscillating mirror drive to keep a LIDAR beam-steering array in phase for accurate 3D scanning.

Back-calculated sensor-space values verify derived safety data against measurements, improving fail-safe evaluation speed and reliability.

Lidar and camera sensing create object-specific virtual bumpers, helping autonomous vehicles keep safe distance with lower sensor complexity.

Selective LiDAR rescanning raises angular resolution around reflective features, improving edge and distant object detection without full-scan slowdown.

Credibilist occupancy, non-occupancy, and unknown-state grids improve vehicle localization and obstacle mapping under contradictory sensor data.

Intermittently blocked sensor beams and frame-based processing separate obstacles from rain, snow, and dust for all-weather autonomous driving.

Adaptive beat-frequency sampling improves close-range FMCW distance measurement accuracy without overloading processing capacity or data throughput.

Offline linear calibration computes per-channel multipliers and bias values to align Lidar intensity returns with surface reflectivity.