Autonomous Decision Making Process TRIZ Cases

Autonomous navigation, locked compartments, and biometric access reduce delivery time, physical strain, and item security risks.

A mobile robot maps its environment, learns routes, and executes scheduled item delivery tasks with less manual coordination.

A 3D point cloud and sensor-fused pose updates let autonomous machines stay within work boundaries without costly boundary wires.

Collision time and location estimation lets a mobile robot adjust path or speed to cross roads safely around moving vehicles.

Compile-time DAG ordering by node depth avoids runtime graph reconfiguration, cutting compute and energy use in AV property prediction.

Opposed over-180° optics with edge overlap let a UAV detect parallax disparity and hold more precise distance and speed near objects.

Historical driving data trains a neural model to predict vehicle acceleration and torque without detailed component data, speeding simulation reuse.

Distributed segment controllers split motion and force commands to cut central control cost and improve update rates for multi-mover cart tracks.

Pre-adapted, reusable perturbations speed adversarial training while improving classifier robustness against misclassification attacks.

Electronic field maps and wireless task assignment let under-canopy robot teams weed, plant, and scout with less soil compaction and chemical use.

Autonomous event detection activates aircraft hands-free control so external systems can operate onboard functions when pilot incapacity disrupts manual flight.

Mobile robots place and adjust sensor targets around a turntable to speed vehicle sensor calibration and improve consistency.

A transition region plus waiting-time filter separates true stop arrivals and departures from GPS and geofence false triggers in fleet tracking.

On-board image processing and traversability scoring help field robots route around uneven, muddy terrain when connectivity is limited.

Inclined corner routes let an autonomous combine harvester cover unworked field edges while reducing manual turning effort and travel time.

A weight-sensitive UAV pad halts takeoff when gross weight exceeds a flight-specific limit, improving landing-site safety and charging readiness.

Smartphone AR mapping traces complex pool shapes and depth to calculate volume accurately and improve automatic cleaner operation.

Offsets guidance reference points by slope and working depth so farm implements track accurately on hillsides and avoid crop damage.

Real-time ground images let the controller assess grading results and adjust vehicle travel and working conditions for better field finish.

Shelf-based feature tracking replaces floor QR codes to correct carrier deviation in warehouse aisles and avoid shelving collisions.

Inclined corner routes and a defined turning area let a combine harvester reap field edges autonomously with less reversing and less missed land.

Simulated driving episodes expose autonomous vehicle control models to off-trajectory states, reducing causality bias from real-world training data.

Temporary landmarks are added and removed from depot maps in real time, improving industrial truck navigation accuracy, safety, and reliability.

Deep reinforcement learning with LSTM helps USV swarms make faster collision-avoidance decisions with adaptive behavior and COLREGS compliance.

Ergonomic handles and capacitive or torque sensing let a mobile robot switch from autonomous travel to easy manual positioning in tight spaces.

Cell-based interactive path planning breaks complex non-convex areas into manageable regions, cutting computation time and repeated traversals.

Inject virtual objects into sensor data so autonomous vehicles can face realistic hazards safely, repeatedly, and at lower test cost.

GA-based mission assignment uses device status, external conditions, and terrain-aware CNN analysis to improve multi-robot decisions in changing environments.

Conditional convoying lets aligned AMRs share intersections safely, reducing deadlocks and improving warehouse material flow.

Remote constraint files update local map rules so autonomous vehicles can avoid closures and reroute safely in changing traffic conditions.

Data-driven course planning uses approximate dynamic programming and neural networks to cut deflection overshoot, control effort, and energy use.

Hub fullness monitoring and autonomous neighborhood pickup reduce service delays by dynamically routing waste to a central collection station.

On-site 3D mapping lets a work machine plan and follow a virtual path without preloaded maps, improving trenching accuracy and adaptability.

Coordinated scheduling, crane, ship, warehouse, and vehicle control enables unmanned container loading and unloading with higher efficiency and safety.

Preheating hydraulic oil keeps viscosity in range, preserving steering response and path-following in unmanned vehicles operating in cold regions.

Operator-confirmed loading automation uses vision detection and selective input to avoid collisions when fog or reflections reduce edge accuracy.

Programmable logic monitors invalid autopilot output and shifts UAV control to a backup process without adding separate hardware.

Stored LiDAR scans are matched after wake-up to detect vehicle movement and restore localization faster without manual fleet recalibration.

RFID-guided access and movable-barrier bypass let autonomous vehicles service servers in secure data center zones without human entry.

Sensors, mapping, and path planning let this mobile robot carry items autonomously through homes or commercial spaces with less human intervention.

Dynamic U-turn route selection balances work efficiency, computation time, and ground protection during autonomous site coverage.

Diagnostic data lets an autonomous rideshare vehicle detect maintenance needs, route itself for service, and request backup rides.

A distributed ledger logs autonomous driving events and triggers smart contracts to enforce liability objectively across drivers, insurers, and manufacturers.

A hybrid physics and neural network model captures steering residual dynamics to improve autonomous vehicle steering accuracy.

A GAN turns virtual driving video into realistic training data while preserving frame consistency, cutting annotation cost and improving CNN credibility.

Relay vehicles forward base-station driving data over direct links, extending hazard and traffic updates beyond cellular coverage.

Automatic UAV matching compares payload, distance, terrain, and release needs to cut selection errors, wasted flights, and vehicle damage.

Recipient height and arm-length data guide where to place packages in a vehicle, improving placement accuracy and reducing delivery errors.

Distributed wind and tide data from piers, vessels, drones, and satellites improves autonomous vessel navigation and berthing accuracy.

A motor-controlled crawler carries adjustable multi-angle imaging equipment to inspect above-ground pipelines in one pass with less labor and radiation exposure.