Control Safety Arrangements TRIZ Cases

A sensor-guided drone locates occupants, avoids obstructions, and leads them along the safest exit path during a property emergency.

Neural prediction fixes key integer decisions in motion planning, cutting MIP burden while preserving collision-free trajectories under constraints.

Sensors and a vehicle controller detect flight component failures and trigger adaptive maneuvers for stable control and safe landing.

Calculates bucket position plus required swing and offset to align excavation edges accurately and avoid collisions in turning work vehicles.

Two-stage prediction replaces delayed vehicle data with refined state estimates, improving remote driving accuracy under latency.

Using travel way line images with inertial data and maps, this case improves aircraft position and heading estimation when GNSS is unreliable.

Dual-mode sensor sampling raises crash detection to 1 kHz during abnormal flight to separate noise from true impact signals and save power.

Environment cameras and proximity sensing help a stuck autonomous robot receive recovery instructions and safely resume autonomous driving.

A detector network predicts GNSS outages and supports rerouting, reducing backup navigation weight for UAV and UAM operations.

Biometric sensing tracks pilot attention and sleep depth, enabling controlled rest, haptic alerts, and automated response to incapacitation.

Preplanned waypoint and shortest-path return logic helps a UAV avoid obstacles, save battery, and regain operator control after link loss.

When intruders halt the mobile carrier, the drone shifts its landing position to the stop point, preserving safety with fewer operation stoppages.

Route planning checks points for people, vehicles, and obstacles so package-carrying drones can avoid drop-risk areas during transport.

Dual activators on the UAV controller require deliberate launch, landing, and stop inputs to reduce accidental flight commands.

Dynamic scheduling assigns maintenance tasks to available operating windows, adapting to changing conditions with less user input.

Target host selection and tunneling agents enable live workload migration across embedded hosts with different safety levels and I/O.

Correlated sensor channels and contingency planning help aircraft control systems detect unreliable inputs and react to actuator failure.

Vision models cross-check aircraft navigation state and uncertainty against images to verify calibration and enable safer automated actions.

Map-defined operating and exclusion zones let autonomous construction vehicles plan task paths, avoid hazards, and support emergency stops.

Pre-flight and ongoing rule checks ground defective or overdue UAVs while authorizing compliant flight plans and geospatial routes.

Mapped work zones and exclusion areas guide autonomous construction vehicles to plan paths, avoid obstacles, and limit manual operation in hazards.

Multi-layer flight boundaries trigger site landing or immediate descent when a UAV deviates from its mission path or loses guidance.

Real-time battery and temperature monitoring lets a UAV impose flight limits before over-discharge or loss of control occurs.

By combining flight control and attitude-heading reference functions, this case cuts UAM size, weight, power, and cost while preserving accuracy.

A monitor module detects unsafe UAV mission conditions and switches flight control from mission commands to a safety module for hazard prevention.

Multiple sensors and AI fuse ramp-area data to warn electric taxi aircraft and ground crews of blind-spot hazards before collisions.

Field sensor checks compare current target scans with stored reference data to verify calibration accuracy without manual test setups.

Sequenced release, restriction, and indicator tags let materials handling vehicles detect tag errors and trigger remediation in restricted zones.

Real-time position and battery data are used to estimate safe return energy and trigger UAV protection commands before power runs too low.

Map-defined operating and exclusion zones let construction vehicles plan safe autonomous paths, avoid obstacles, and reduce manual intervention.

Environment cameras and a higher-level controller guide a stuck autonomous mobile robot out of emergency stop and resume driving after sensor safety checks.

Terrain-map path planning and proactive rotation help autonomous mowers keep traction on slopes and avoid obstacles before getting stuck.

A dual-channel flight control scheme checks multi-drive command allocation through deallocation, cutting software complexity while preserving fail-safe operation.

Sensor, IMU, and dual-loop control with torque mixing keep electric aircraft stable and controllable during component malfunctions.

Unsupervised learning on flight time-series logs detects unknown vehicle anomalies and flags missions for inspection or corrective commands.

Modified control schemes let a six-propulsion UAV stay controllable after motor failure and land safely in degraded flight.

When the ground link fails, the UV detects a trigger event and switches to AI-assisted autonomous navigation to continue missions or return safely.

Sensor and control data detect irregularities and switch autonomous farm machines between safe and normal states without constant supervision.

A vision processor replicates collision signals so an autonomous mower can steer and pause cutting near obstacles with less reliance on GPS and markers.

A reader module separates diagnostic and layout tags, modulates diagnostic tag power, and flags missing reads to catch RFID faults early.

Human presence detection and mobile warnings keep drone rotors inactive until the clearance area is empty, reducing bystander injury risk.

A collision detection unit triggers motor power cut-off to prevent unsafe drone restarts and post-crash damage.

LIDAR, cameras, and ML let an AMR capture site imagery, quantify completion, and flag safety hazards in real time.

During preventive RAs, the autopilot caps conflicting vertical speed commands and triggers corrective maneuvers to maintain TCAS separation limits.

A supervisory monitor analyzes subsystem, context, and control data to assess autonomous vehicle control status in unexpected conditions.

An LED matrix on a multicopter shows real-time flight direction and motion state, improving observer awareness and collision avoidance.