Lawn-Mowers TRIZ Cases

Thermal imaging and machine learning detect fawns and obstacles ahead of mowing, enabling geofenced path changes that protect wildlife and equipment.

Binocular vision detects missed grass by parallax, edges, and color so the robot mower can redirect early and avoid later obstacle misreads.

RTK GNSS correction from fixed antennas and a remote server keeps robot lawn mower positioning accurate near obstacles and weak satellite coverage.

Thermal imaging and machine learning detect animals and obstacles, enabling geofenced avoidance to prevent wildlife harm and equipment damage.

A reverse lane-change sequence on slopes reduces slipping and grass wear while keeping the mower aligned for forward cutting.

Coordinated group movement through a shared transport area cuts collision and deadlock risk while reducing area occupancy time.

Rear-led lane changes help a robotic lawn mower cut slopes systematically while reducing slip risk and grass wear during turns.

Comparing body travel with drive-wheel travel lets a robotic lawn mower detect persistent slipping and trigger corrective action.

Threshold-based loop closure lets autonomous lawn mowers build virtual boundaries accurately while avoiding repeated adjustments and lawn damage.

Candidate turn positions and reverse maneuvers let an autonomous mobile device work closer to boundaries while reducing collision risk.

An unmanned vehicle maps work zones by exploring terrain segments, classifying ground types, and using obstacles as boundaries.

Preplanned edge-based routes let multiple work machines enclose a work region with set width, improving coordination and route accuracy.

Sensors detect loop and wire signals so a robotic mower can leave its station, follow guide or boundary wires, and avoid repeated lawn tracks.

Adjustment vectors offset virtual mower boundaries inward to compensate for shadows and user deviation, reducing collisions during edge following.

Magnetic boundary-line signals help a smart mower recover direction, avoid random collisions, and exit narrow areas more reliably.

Dual route setting switches between neural methods by area shape and obstacles to minimize unreached zones while limiting processing load.

Adjustment vectors retract a mower’s virtual boundary to compensate for positioning offsets and shadow areas, reducing obstacle collisions.

By rotating an offset monocular sensor about a fixed axis, the robot derives travel distance accurately without mechanical odometry.

Arc-based lane changes let a robotic mower align with boundaries and cut grass close to edges and objects without manual trimming.

By splitting complex and open mowing areas, the server estimates work time and recommends the right mix of small and large lawnmowers.

Virtual zones and time-slot scheduling let mixed-size lawnmowers share a work area efficiently while reducing collision risk.

A dedicated auxiliary AI processor handles object recognition in a robotic lawn mower, cutting power use while improving obstacle handling.

Gradient and height data guide contour-based mowing routes that reduce slope slippage and improve mowing efficiency on uneven terrain.

Superimposed work area images on a map show completion and required status, cutting time to assess outdoor robot work zones.

A superimposed map uses color and transparency to show work areas and machine availability, making task allocation faster and clearer.

By detecting limited movement from turns, collisions, and confinement, the robot finds an exit path to escape traps and restore full area coverage.

Boundary attributes on recorded path points let a robotic mower switch edge-working modes, reducing missed grass and rework.

Preplanned target points and adaptive mowing paths help a robotic lawn mower cross obstacles with less unnecessary travel and power use.

Pre-stored map data guides autonomous mowing across multiple areas with single-pass cutting and simultaneous mulching or collection.

Per-zone time windows coordinate fast and slow lawnmowers across virtual zones, reducing collision risk while preserving mowing efficiency.

Snap-fit body assemblies let a robotic work tool stay securely attached in use while enabling fast maintenance and component access without special tools.

Preplanned map-based mowing enables single-pass grass cutting and faster transitions between work areas with less manual labor.

A moving containment zone guides random mower travel across the work region to improve coverage uniformity and avoid narrow-passage hang-ups.

Combining forward and backward paths lets a work machine reach the start position in the right posture without pivot turns that damage the surface.

When satellite positioning becomes untrusted, the controller switches to prioritized sensor channels or fused inputs to keep robotic navigation accurate.

When unknown obstacles interrupt mowing, the robot records a task breakpoint, maps the remaining area, and resumes without redundant passes.

Central server planning matches work area features and tool capabilities to coordinate robotic tasks across new, uneven, and debris-filled areas.

Battery depletion is predicted before a task segment starts, letting the robot reroute to its charging base without breaking track continuity.

Rear imaging detects missed grass after mowing, letting the control unit target rework spots and improve finish quality without remowing the whole lawn.

Reference-station correction data sharpens mobile boundary mapping, reducing position drift and improving robotic lawnmower cutting accuracy.

Correction data from a reference station sharpens mobile boundary mapping, reducing drift and improving robotic lawnmower cutting accuracy.

Alternating grass-pressing paths let an autonomous mower create lawn stripes while cutting labor and equipment cost.

Automatic guidance to an external cleaning station removes debris from the mower underside and operative parts, reducing manual cleaning.

Electromagnetic anchors guide a robotic garden tool through narrow passages and multiple zones for even mowing without complex mapping.

A base mobile unit with sealed secondary cavities accepts swappable modules, cutting model complexity and upgrade cost across changing tasks.

Sensor-based mode switching lets an auxiliary mower cutter trim edges flush with the housing while reducing injury risk near detected objects.

Maps low-GNSS-accuracy zones and guides beacon placement so self-propelled work machines keep precise positioning across the work region.

Classifying route nodes into normal and special types improves turning-point arrival detection and reduces uncompleted work regions.

Camera-based object recognition lets an autonomous mower avoid temporary obstacles while still identifying docking stations and exclusion zones.

Adaptive path parameters let a mowing robot replan around repeatedly detected obstacles, improving avoidance flexibility and efficiency.