Position/Course Control In Two Dimensions TRIZ Cases

Sequential dual-wavelength lighting lets a detector separate true route markings from dirt, grooves, and joints for reliable vehicle guidance.

Switching safety zone algorithms lets an autonomous floor cleaner move faster near walls by limiting direction changes while staying compliant.

Radar sensing lets an autonomous mower detect animals and humans from reflected waves, then adjust movement and tools to avoid harm.

Bounding boxes around moving vehicle structures exclude self-detected lidar points, reducing false alarms and map errors in mining vehicles.

Mapped local routes and user annotations help robots scan complex sites accurately while collecting sensor data on missing items and traffic flow.

Sensors and onboard processing map obstacles into virtual boundaries, improving robotic garden tool navigation in complex yards.

Timed access plans let multiple robots share workspace resources without collisions, deadlocks, or constant path replanning.

Potential path detection updates an episodic cognitive map with road-edge connectivity, helping mobile robots plan safer routes in complex environments.

Two coordinate-based navigation systems let a robot localize reliably on static floors and moving conveyors with smooth handoff.

Dynamic safety zones and LiDAR detection let an automated guided vehicle adjust speed and steering to avoid collisions in mixed traffic.

Magnetic sensors detect AGV stop-position misalignment from tape references, enabling fast, repeatable coupling with peripheral equipment.

Tile-based map updates keep robot traversability maps accurate in changing spaces while reducing processing and transmission overhead.

Real-time compensation refines wheeled robot control signals to handle environmental effects without repeated parameter tuning.

Topological map annotations trigger callback services so robots can handle obstacles and mission-specific actions without changing route executor code.

Time-shared ultrasonic modules with intersecting detection paths locate obstacles more precisely and help self-moving devices avoid collisions.

Image-based delivery point correction lets users adjust unattended package drop-off locations and avoid misplacement or theft.

Presence probability mapping guides unmanned machine formation and motion control to improve target capture with less manual coordination.

Vehicle acceleration opposite a detected cargo offset uses inertia and friction control to place cargo precisely without extra positioning elements.

Sensors detect obstacles and boundaries so a robot can form intuitive map zones, then refine sectoring from user feedback.

A virtual robot mirrors autonomous obstacle avoidance so operator feedback matches intended motion and reduces discomfort during remote control.

A wearable locator and vision-guided robot follows vineyard workers, carries harvested material, and weighs loads to cut strain and transport time.

Dynamic safety zone switching lets autonomous floor cleaners move faster near walls while limiting direction changes to maintain safe operation.

Magnetic coupling and spring-loaded contacts let a robotic eye cartridge be replaced easily while preserving eye motion, eyelid actuation, and video sensing.

GNSS-based control steers an autonomous wheeled robot back into its tracking attraction domain to restore stable, precise path following.

Digital geo-fence data replaces physical field markers, letting autonomous farm machines work within defined areas with less operator input.

By tracking boundary markers and holding a fixed offset, the robot guides itself along a route without large memory or lane setup effort.

Keeping zero distance, a second autonomous vehicle pushes the first past the loader to maintain continuous material flow and reduce spillage.

Fused RGB-D, LiDAR, sonar, and infrared data improves detection of small or transparent obstacles and enables faster velocity control.

An oblique rear following path helps a mobile object track a user through crowded areas while avoiding obstacles and blind spots.

Real-time AGV positions define dynamic blocking areas that prevent collisions while avoiding stop-start losses on shared routes.

When faults, weather, or detours disrupt autonomous driving, remote operators can detect risks, switch modes, and generate safer trajectories.

Image-based docking area validation flags obstacles and recommends dock repositioning to avoid charging failures and debris evacuation issues.

Adjacency sensors and motion data let robots localize and map with non-unique wall features, avoiding camera or LIDAR cost and complexity.

Multi-stage handover lets autonomous vehicles shift to remote operators while keeping assistance features active to reduce delay and control risk.

When uncertain road events lower driving confidence, remote guidance helps a driverless vehicle revise its trajectory and avoid delays.

By checking boundary signal consistency over time, the control module filters interference and keeps autonomous mower positioning stable.

Anticipatory waypoint steering lets vehicles follow virtual lanes more smoothly and quietly while avoiding rigid track guidance and complex sensor setups.

An onboard laser and camera monitor the conveyor route for obstacles, preserving flexible load-carrier transport while preventing unsafe travel.

Real-time cost-based handover decisions reassign workers and autonomous vehicles to cut travel and idle time without fixed warehouse zones.

Virtual centerline guidance from onboard sensors enables automatic runway alignment and safer aircraft takeoff in low or no visibility.

Sensor-guided dump paths let autonomous dump machines adapt dumping positions to maintain target density and avoid uneven filling.

Preplanned head-tail roles and void generation let mobile robots reconfigure formation across non-connected start and target positions.

Grid-point clustering lets a robotic cultivator target only soil zones that meet treatment criteria, improving coverage and reducing energy use.

Boundary wire distance sensing stabilizes outdoor SLAM when camera signals vary, enabling accurate mapping and more efficient area coverage.

A vehicle maps concrete floor reinforcement signals and matches profile segments to locate position and orientation without added markers.

Ownership and device-ID checks gate eUICC provisioning so only registered autonomous vehicles gain mobile network access.

Recorded onboard sensor data guides autonomous driving and reversing without extra localization sensors, reducing complexity and collision risk.

Distributed route decisions let conveying vehicles share task planning, cutting management-system load and speeding plan updates when trouble occurs.