Hybrid Robot Motion Planning With Constrained Parametric Trajectories

The patent applies dynamics by transitioning from static hard-coded trajectories to dynamic machine learning models that can adapt trajectories in real-time based on environmental feedback. The ML model continuously learns and adjusts motion parameters, enabling the robotic system to dynamically respond to changing conditions while maintaining operational simplicity through automated adaptation.

The patent utilizes parameter changes by allowing the machine learning model to modify trajectory parameters such as position, orientation, speed, and acceleration based on learned patterns and environmental conditions. This enables flexible adaptation of motion characteristics without requiring complete reprogramming of the trajectory system.

The patent merges machine learning-based adaptive planning with traditional constraint-based motion planning methods. The ML model provides flexible trajectory suggestions while constraint solvers ensure these trajectories satisfy safety, collision avoidance, and task completion requirements. This combination leverages the strengths of both approaches: adaptability from ML and reliability from constraints.

The patent introduces constraint solvers as intermediaries between the machine learning model and the final executed trajectory. The ML model generates candidate trajectories, the constraint solver validates and adjusts them to meet safety and task requirements, and the refined trajectory is then executed. This intermediary layer ensures reliability while preserving ML flexibility.

The patent applies preliminary action by pre-training the machine learning model on extensive simulation data and common scenarios before deployment. This pre-learning enables the model to quickly adapt to new situations with minimal online training, improving adaptation efficiency while maintaining reliability through the established constraint-based safety layer.

The patent implements continuous learning and adaptation through the ML model, which continuously updates its understanding of the environment and refines trajectories in real-time. This continuous adaptation maintains reliability through constraint validation while significantly improving productivity by eliminating the need for repetitive replanning in dynamic conditions.

The patent segments the motion planning problem into multiple components: high-level trajectory generation by the ML model, constraint validation by solvers, and low-level execution by controllers. This segmentation allows each component to be optimized independently, reducing overall computational complexity while maintaining adaptability through the coordinated workflow.

The patent applies partial action by having the machine learning model focus only on generating candidate trajectories and identifying key optimization parameters, rather than solving the complete motion planning problem. The constraint solvers handle the remaining validation and adjustment, dividing computational effort and reducing the burden on the ML model.