Programme-Controlled Manipulator TRIZ Cases

Sensor-guided robot transformations replace manual point alignment to calibrate tool coordinates with higher accuracy and less operator dependency.

By estimating joint friction and arm-segment center of mass from motion data, robots can calibrate themselves for more precise trajectory control.

Image-based free-space and object tracking lets a robot time revolving door entry to avoid collisions without unnecessary waiting.

After cutting a content-filled material, rotating it so the cut faces downward helps keep contents off the robot and cutter.

A nested compound gear train and targeted bearing support deliver compact robotic steering with a 64:1 ratio, precise alignment, and lower friction.

A global dynamics model plus on-policy correction reduces model error in robot policy learning, improving stability and data efficiency.

A pivotable pinion holder lets lift robots switch between vertical and horizontal rails at intersections, reducing interference, delays, and wear.

A trained neural network predicts future states from object data and control sequences, then outputs new commands for real-time flexible object control.

By selecting robot actions from worker traits like dominant hand and height, the control scheme improves efficiency and reduces fatigue.

AR workspace mapping and tracked manual teaching generate robot instructions automatically, cutting programming time and cost for low-volume parts.

An external exoskeleton on a tracked mobile robot lets one operator precisely move and position heavy industrial loads across rough terrain.

By rotating the cut material so the opening faces downward, robots can discharge bag contents cleanly without sticking to the cutter or arms.

A control device selects the nearest on-path deployment position to cut off-path robot travel, improving path efficiency and collision avoidance.

When an autonomous robot cannot execute the next task, on-demand teleoperation enables human takeover and smooth return to autonomous mode.

A layered policy framework combines unbiased motion, obstacle avoidance, and joint-limit handling to keep machine movement stable and predictable.

By rotating a robot joint at constant speed under constant gravity torque, speed fluctuations reveal gear-driven kinematic errors for compensation.

Acceleration sensing at the manipulator tip corrects joint commands to reduce kinematic error and improve end effector accuracy.

Adaptive weaving width control in groove GMAW maintains weld quality when 5%+ Ni wire and base metal compositions differ.

Position and torch posture data let operators teach a weld line visually, cutting setup effort while keeping start-to-end welding consistent.

Aerial imaging and UWB anchors replace boundary wires, creating editable obstacle-aware maps and travel paths for lawn mower robots.

A central pattern library lets each palletizer auto-select scalable stacking patterns, cutting maintenance effort and adapting to varied packing conditions.

A fixed-tool RTCP frame with sensor feedback lets the robot move the workpiece while maintaining real-time tool tip offset control.

Compression, expansion, and intermediate moves let multiple robots transform joined ranks faster without complex peripheral motion.

Variable-length main and auxiliary links let a humanoid shoulder stay compact while delivering two-axis power and human-like torsional motion.

Confidence-based motion planning adapts robot scanning during object transfer when pose estimates are uncertain, improving accuracy and throughput.

Speed-based viscous resistance compensation reduces low-speed drag in robot drive shafts while preserving control accuracy and backdrivability.

Image-based posture estimation lets a robot mirror human movement while correction control adapts motion to the robot's physical limits.

By selecting a parking direction that matches the target path, the moving device avoids collision and resumes navigation with less startup delay.

Hand-gesture robot teaching is combined with visual-inertial odometry to detect obstacles and guide the arm along a safer collision-free path.

Fused ToF images and radar data improve 3D human detection near robots, cutting false stops while tracking motion and speed.

Imagery analysis identifies barcodes and obstructive item features so robotic grasp plans can pick and orient items with less rescanning and intervention.

Structured robot action logs combine instruction, sensor, and map data to cut file size while enabling fast replay and issue tracing.

Predicted actions of nearby actors feed a recursive reinforcement model so autonomous agents choose safer, energy-efficient responses.

AI-based environment sensing lets a mobile robot predict hazards and adjust movement and arm safety levels before service actions.

Users define robot goals and constraints by touching a projected workspace, cutting programming effort and easing layout changes.

Force feedback lets a robot follow a conveyor workpiece, correct tilt during contact, and complete accurate insertion before it moves out of range.

A slotted shim lets the pinion position be tuned without releasing gear mesh, speeding robot backlash adjustment and reducing assembly errors.

A rotatable grip holder lets operators reorient a handheld terminal for better visibility, switch access, and reduced fatigue.

Variable resistance guides manual robotic arm movement toward target alignment positions, improving precision while preserving positioning flexibility.

A two-stage ML controller narrows robot actions to affordable options, cutting computation while improving complex task success.

By integrating pre- and post-operation 3D scans with robot joint displacement data, this case cuts registration time while improving robustness.

A correction filter compensates for protector viscoelasticity in force sensor signals, improving stable and accurate holder grip control.

Camera vision and few-shot learning reconstruct tire position and orientation for precise gripping in loose, overlapping sorting loads.

A pre-coating test pattern and image feedback correct spray control signals early, reducing overspray, defects, and rework.

Combining vision sensor data with geometric stack models helps robots plan palletizing and depalletizing of mixed items with better stability.

Combining trajectory optimization with model-based and DRL grasp planning helps robots reposition objects and switch grasps during in-hand manipulation.

A two-jaw robotic pipe gripper holds drill pipe radially while allowing axial movement, improving rig handling speed and reducing labor risk.

Optical sensing enables on-site calibration of a construction robot manipulator, avoiding duplicate hardware while preserving precise tool positioning.

Fusing multiple sensing modes at the point of capture cuts data volume, resolves sensor conflicts, and speeds autonomous decisions.

Bridges sim-to-real robot learning with separate action and control policies, cutting training time despite friction and motion delays.