AR Visualization for Tuning Robotic Picking Boundaries

The patent creates virtual copies of physical parts and the robotic system itself. Virtual parts are generated with known properties and positions, allowing the vision system to be tested and tuned against perfect digital twins rather than imperfect physical parts. This copying approach enables rapid iteration and validation without the time-consuming process of working with actual parts throughout commissioning.

The system performs preliminary configuration and tuning actions in the virtual environment before deploying to physical operation. By pre-validating vision systems, pre-tuning parameters, and pre-optimizing robot trajectories in simulation, the actual commissioning time for physical systems is dramatically reduced. All preparatory work is completed beforehand in the virtual workspace.

Virtual parts replace physical parts during the tuning process. Instead of consuming real parts to test different throughput rates and system configurations, the same virtual parts can be endlessly reused and reconfigured. This eliminates the cost of parts consumed during commissioning while maintaining the ability to fully test productivity scenarios.

The system uses its own virtual environment to perform self-validation and self-tuning. The robotic system can automatically test its own parameters, validate its vision system, and optimize its performance using virtual parts and simulated workpieces, reducing the need for external labor and expertise during the commissioning phase.

The patent creates virtual representations of physical parts that retain all relevant geometric and physical properties. These virtual copies can be manipulated, positioned, and configured in ways that are difficult or impossible with physical parts, while still maintaining the accuracy needed to validate picking operations. The virtual model faithfully reproduces the real part characteristics.

All difficult tuning and validation work is performed preliminarily in the virtual environment where changes can be made instantly and reversibly. Once the system is tuned and validated in simulation with high reliability, the same configuration is deployed to physical operation, eliminating the need to repeat difficult tuning procedures with actual parts.

Each robot has its own virtual twin and set of virtual parts in the simulation environment. This allows independent tuning and validation of each robot's performance, boundaries, and parameters before integration. The virtual environment can easily replicate the complexity of multi-robot coordination without the physical constraints and interference that make real-world multi-robot systems difficult to balance and coordinate.

Load-balancing and coordination algorithms are developed, tested, and optimized preliminarily in the virtual environment where multiple robot agents can be simulated simultaneously. The virtual system allows rapid iteration of coordination strategies and boundary definitions for each robot, enabling complex multi-robot systems to be tuned and balanced before physical deployment, significantly reducing on-site commissioning complexity.