Autonomous Decision Making Process TRIZ Cases

A mobile hub uses risk data and task assignments to dispatch autonomous unmanned vehicles, cutting operator burden in hazardous missions.

Discrete beacons replace boundary wires and manual teaching, enabling accurate lawn mapping, map alignment, and clearer robot setup.

Flight path and destination weather determine whether a UAV should dispatch temperature-sensitive medication within its safe thermal control period.

A mobile sensor vehicle guides transportation vehicles in closed depots, reducing onboard sensor complexity while maintaining safe autonomous control.

Dynamic fleet sizing uses vehicle capabilities and service demand to cut idle computational waste while maintaining autonomous service coverage.

Multiple vehicle audio sensors and ML classification identify emergency sirens and estimate direction of arrival with lower latency.

A universal card automatically selects the best payment account for each purchase, reducing multi-card complexity and manual expense tracking.

V2X QoE status guides route changes and service throttling so connected vehicles avoid overloaded edge or cloud servers and maintain control reliability.

Multiple action plans are generated from surrounding feature points and ranked by position probability to keep a mobile object moving when self-location is unclear.

A server separates local and outside-market parcels at intake, avoiding unnecessary regional sorting to cut delivery time, labor, and transport waste.

Perception sensors detect misplaced luggage or wheelchairs in an automated taxi, notify the client, and trigger fees if non-compliance persists.

Autonomous vending units travel to user locations via mobile requests, expanding service coverage without fixed infrastructure.

User-set path angles and sub-region planning reduce repeated mowing, cut hardware load, and maintain high coverage in complex mowing areas.

Future time and position commands keep autonomous circuit-route vehicles evenly spaced when one is removed for charging or reintroduced.

Connected vehicles pool unused computing power, memory, and bandwidth to analyze roadway queues faster and coordinate safer responses.

Idle-time scheduling lets an autonomous vehicle drive itself to repair shops, coordinate claims, and return with minimal owner disruption.

Fixed lattice segments let a mobile robot bypass dynamic obstacles while reusing and refining control inputs to preserve imaging data quality.

Tracking sensors and ECU-guided following let a store workhorse deliver bulky goods and collect returns without extra customer trips.

Relative landmark images and transmitter signals refine mobile body positioning when GPS reception is poor and sensor drift grows.

When roadway visibility is blocked, one autonomous vehicle requests another vehicle's sensor view to fill blind spots and plan safe maneuvers.

Pre-mapped light sources and feature-aware exposure control help robots maintain pose estimation and localization as lighting changes.

A simulated clock and serially scheduled nodelets remove race conditions and latency variability in distributed system simulation.

Image-based semantic maps let autonomous ground vehicles follow sidewalks, avoid hazards, and navigate precisely when GPS is unreliable.

Location-based filtering presents only relevant UAV mission plans, reducing manual selection errors and improving consistent data collection.

Central path assignment rotates self-driving golf carts across fairway routes to prevent turf damage while extending cart access beyond asphalt roads.

Autonomous checkpoint passing and loading control remove driver fatigue risks while cutting personnel needs in highway port freight transport.

Active biasing keeps the internal drive engaged for precise 3D spherical motion, improving control on inclines and complex maneuvers.

Sensors and a multi-axis end-effector let an autonomous vehicle detect mating features and stack pallets or containers with precise alignment.

Traffic-reaction estimates let autonomous vehicles handle non-essential route changes while limiting delays, fuel use, and disruption to nearby cars.

A tethered companion autonomous vehicle delivers, follows, and returns outdoor gear, removing user burden from transport and maintenance.

Combining AI, logic, and contextual analysis improves time-based human activity prediction while reducing ambiguity in driving and group scenarios.

When a loading place fails, switchback commands reroute work vehicles between forward and reverse paths to avoid wasted travel and productivity loss.

When odometry and image-based positioning both lose accuracy, the machine moves to mapped high-accuracy zones to correct its travel position.

Triggered sharing of validated vehicle sensor data extends obstacle awareness beyond onboard sensor range for safer planning.

Membership-weighted fuzzy subsets convert discrete AI control choices into continuous outputs, improving action smoothness and precision.

Satellite fault alerts and differential base-station correction help autonomous mowers keep virtual boundaries and navigate safely.

Motorized wheel orientation and drive control help self-driving luggage navigate crowded spaces and adapt to obstacles with less user effort.

Structured schema encodings turn high-dimensional vehicle sensor data into clustered scenarios for more reliable hazard identification and classification.

A motor scythe records border coordinates during trimming, enabling accurate autonomous lawn robot navigation with fewer sensing devices.

Lane-level cells and a graph network predict future road occupancy in real time, improving driving recommendations and collision avoidance.

Synthetic data pre-training fixes policy layers and tunes feature layers so neural networks learn in-domain cues without absorbing out-of-domain artifacts.

Maps observed vehicle trajectories into an intermediate space so road agent paths stay predictable without retraining when classification sets change.

Iterative cross-validation tunes LIDAR coordinate conversion so obstacle positions stay consistent across scans without slowing real-time driving.

A unified scene graph links static and dynamic road objects with uncertainty metrics to support faster, safer vehicle trajectory decisions.

Foot motion sensing lets an autonomous follower match a person's move or stop intent more accurately than distance-only control.

Perspective-invariant state features unify multi-camera robot observations, easing navigation training across different viewpoints and dynamics.

Delayed self-reinforcement combines neighbor motion and time-delayed self-data to speed formation response and improve cohesion.

A UAV adjusts flight height from recognized road objects to capture map data faster and at lower cost than dedicated ground vehicles.

Passive optical sensor elements scattered across fields let aircraft collect water, temperature, and pH data without battery replacement or manual retrieval.

Shared map-based driving strategies let automated vehicles travel closely while preserving localization accuracy, cutting traffic density and emissions.