Random-Key Optimization for Robotic Sequencing Plans

The patent transforms the discrete sequencing problem into a continuous optimization problem by mapping sequence positions to continuous values in the range (0,1). This parameter transformation enables the use of efficient continuous optimization algorithms instead of exhaustive discrete search methods, resolving the contradiction between solution quality and computational time.

The patent replaces traditional mechanical combinatorial optimization approaches with quantum annealing computation. By formulating the sequencing problem as a Quadratic Unconstrained Binary Optimization (QUBO) model and solving it using quantum annealing, the system achieves better optimization performance with reduced computational time compared to classical exact methods.

The patent incorporates all sequencing constraints into the objective function formulation before optimization begins. By encoding constraints such as resource availability, task dependencies, and collision avoidance directly into the QUBO model's energy landscape, the method ensures feasibility is built-in from the start rather than requiring complex post-processing or iterative constraint checking.

The patent merges multiple sequencing constraints into a unified objective function. Rather than handling each constraint separately through multiple computational passes or complex constraint satisfaction algorithms, all constraints are combined into a single energy minimization problem that the quantum annealer solves in one computation, reducing overall computational complexity.