Mowers TRIZ Cases

Dynamic control zones let work machines adjust actuator settings during operation by splitting harvested and unharvested areas as conditions change.

A battery-powered boundary signal unit replaces fixed charging stations, letting soil robots define and switch work areas with less setup effort.

A movable lock pin blocks piston retraction to hold raised agricultural feeders securely while reducing cumbersome lock operation.

Predetermined turn patterns let agricultural vehicles handle sharp field turns automatically, reducing coverage gaps and operator input.

A control unit adjusts the working unit height in multiple steps during autonomous operation, reducing user intervention while preserving mode choice.

Multiple cutting edges on both sides let a rotary cutting tool be flipped or rotated to cut wear, maintenance time, and replacements.

Valve-controlled bypass and drain routing protect a harvester header hydraulic cooler from high pressure, preventing leaks and downtime.

Location-based laser curtain modes let autonomous crop-row vehicles detect intrusions early and slow or stop to protect nearby personnel.

Upper and lower grease reservoirs feed reciprocating blades through aligned holes, keeping motion smooth while blocking water and debris.

Oil temperature is used to correct free-running hydraulic pressure estimates, improving hydraulic power accuracy despite viscosity changes.

Automatic float pressure adjustment maintains target ground contact force as conditions change, reducing scalping and missed crop pickup.

Operators can choose which harvester settings stay automated and which remain manual, improving flexibility, trust, and harvesting efficiency.

Fluid pressure is automatically reset from tilt, gauge shoe, and temperature changes to prevent scalping and bouncing during harvest.

Camera analysis of crop proportion lets a self-propelled harvester adjust working units and travel speed to support yield in changing field conditions.

Route planning uses adjacent parcel suitability to predict animal escape direction and reduce machine contact while maintaining field efficiency.

A retaining element limits clutch axial movement during reverse rotation, preventing unintended engagement and improving transmission reliability.

Position-referenced grass height, moisture, and color sensing lets robotic lawnmowers adapt mowing schedules to lawn and weather conditions.

Lodging-aware route creation lets a combine adjust reaping direction in lodged regions, improving automatic harvesting accuracy and efficiency.

An image-based target marker helps a harvester track the container at long range and steer crop flow accurately to reduce spillage.

A planetary gear and hydraulic clutch control approach keeps combine rotor start-up at constant acceleration to reduce vibration and operator discomfort.

Predetermined turn patterns help agricultural vehicles detect sharp guidance turns early and navigate within minimum turn radius limits.

Windrower GPS data is converted into swath centerlines so a combine can follow windrows more accurately with less operator effort.

Coordinated route plans align mowing, haymaking, and harvesting machines to avoid collisions, cut extra passes, and adapt to field changes.

Coordinated common and machine-specific routes keep harvesting chains collision-free, adaptive to field conditions, and lighter on soil.

Dual motors drive opposite ends of a cutter gear train at different speeds to keep one-way loading, reducing chatter and gear wear.

Captured field images are tied to robot self-position data to map and navigate areas where GNSS signals are unreliable without large map memory.

Predetermined turn patterns let agricultural vehicles detect sharp guidance turns and complete them autonomously with fewer coverage gaps.

Near real-time remote setting changes with verification feedback help combine harvesters cut adjustment delays and operator error.

Sensors detect a receiving vehicle in the unloading zone, then actuators automatically position the harvester tube for precise crop transfer.

Selectable magnetic reference values let an autonomous work machine detect ground or aerial boundary wires and avoid contact while staying on route.

Vision-based localization aligns a transfer vehicle with a haulage vehicle receiving area to automate loading and reduce spillage.

Mapped ineffective areas let a lawn mower pass sensor-flagged non-grass zones, reducing detours and improving mowing coverage.

Varying transit-zone travel paths lets robotic work tools move autonomously between areas without creating permanent trails.

Position-referenced sensing of grass height, moisture, and color lets robotic lawnmowers adapt mowing schedules and protect lawn health.

By detecting boundary position and choosing the nearer-side turn, the vehicle exits narrow areas faster while reducing stops and battery use.

Hydraulic yaw-frame alignment and electromagnetic sensing simplify header hookup, reduce manual adjustment, and improve harvester safety.

Adaptive laser curtain patterns change with row position to protect nearby workers while autonomous units navigate soft fruit crops.

Sensors detect a receiving vehicle in the unloading zone, then actuators position the crop tube automatically with less computing load.

A hinged pole folds the string trimmer for storage while routing the power cable through the hinge and blocking unsafe battery insertion.

Laser scanning builds 3D crop point clouds so the header can detect lodging and adjust harvesting automatically with less loss and manual effort.

In-situ sensing and predictive field maps let harvesters anticipate dense vegetation, adjust settings early, and maintain stable crop flow.

Fleet-aware speed control uses crop flow and harvester data to maintain safe spacing, stable formation, and efficient grain cart routing.

Crop flow sensing and fleet data guide harvester ground speed to maintain spacing, avoid overtaking, and support efficient unloading.

Cyclic receiver on-off timing lets self-propelled floor vehicles avoid electromagnetic signal overlap while maintaining accurate wire-based positioning.

Topography-linked sensor data builds predictive field maps that help agricultural machines adapt to moisture and yield variation in real time.

Dynamic hierarchies and shared optimization let farm machines act as a virtual work machine for real-time field process coordination.

In-situ sensing and field maps predict ear-size variation so deck plate spacing can be adjusted automatically to reduce grain loss.

Predicted vehicle position guides steering correction to avoid oversteer and keep autonomous work vehicles stable on target paths.

A base station sends GNSS correction values to the collar, improving boundary detection accuracy and reducing interference without physical fences.

A gimbal-based positioning unit keeps agricultural monitoring sensors aligned on uneven terrain, improving obstacle detection and autonomous steering reliability.