Six-Foot Robot State Planning With Monte Carlo Tree Search

The patent segments the state planning process into independent motion cycles, where each cycle plans only the next state without being influenced by previous cycles. This is achieved by resetting the planning horizon at each motion cycle boundary, effectively dividing the continuous planning problem into discrete, independent segments that avoid front-back coupling while maintaining planning simplicity

The patent introduces an intermediary evaluation mechanism that assesses the quality of state sequences generated in each motion cycle. This intermediary layer filters out poor planning results before they can affect subsequent cycles, preventing the propagation of negative influences while allowing each cycle to maintain its independent planning process

The patent performs preliminary evaluation of multiple possible next states before committing to a single state transition. By pre-assessing the quality of potential state sequences and selecting the optimal path in advance, the system achieves near-optimal sequence optimization without requiring computationally expensive real-time replanning of entire state sequences

The patent maintains continuous optimization by carrying forward the evaluation criteria and partial results from one motion cycle to the next. Although each cycle plans independently, the cumulative effect of consistently applying the optimization criteria across cycles produces an overall optimized state sequence, effectively continuing the useful action of optimization without interruption