Automatic Obstacle Detection TRIZ Cases

Coordinated robotic cleaners share task status and battery data to avoid overlap, complete floor coverage, and cut wasted energy.

Boundary-aware route planning stores only path segments and treatment status, enabling complete floor coverage with less mapping overhead.

Network-linked terminal control lets a robot cleaner switch modes, report status in real time, and support voice-based remote operation.

A wide-angle lens turns point light into planar light, removing dead zones and servo scanning in cleaning robot obstacle sensing.

Membrane switch arrays and a force transmission layer let a robot bumper localize multi-angle impacts and estimate force without complex mechanics.

A base-integrated drive suppression unit and elastic caster help a robot cleaner classify obstacle height and avoid falls when sensors fail.

Coded signals from multiple transmitters let an autonomous vehicle track its relative location in cluttered spaces with more stable navigation.

A resilient compressible roller and four-bar cleaning head keep contact, pass large debris, and prevent hair wrap that can stall robotic vacuums.

A front-wide cleaning assembly and sensor-guided navigation help robotic cleaners reach walls and corners with more complete floor coverage.

Distributed sensor arrays in a compliant robot bumper detect impact position and force while avoiding bulky spring-pivot mechanisms.

Compressible resilient rollers with chevrons lift debris and admit larger objects while reducing hair wrap, stalling, and airflow loss.

A compressible chevron roller and four-bar linkage limit hair wrapping, preserve airflow, and lift the cleaner head on carpet transitions.

Sub-area maps and connection points let a robot cleaner update full cleaning paths quickly without regenerating the entire map.

Ceiling-reflected infrared signals let an autonomous robot triangulate its position and map obstacles more accurately in cluttered spaces.

A retractable auxiliary cleaner uses state sensing and controller feedback to detect brush faults and avoid obstacle-related cleaning failures.

Grid-based dirt sensing and SLAM route planning help a robot vacuum revisit dirty areas in the shortest path for more thorough cleaning.

A reduced grid map guides section-by-section path search, cutting memory and processing time while preserving obstacle avoidance.

A timed spray-and-scrub routine lets the robot wet the floor just before oscillating pad passes to remove dried soils with less manual effort.