Unmanned Vehicle Path Planning Using Reusable State Data
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Solution Overview
Problem
Unmanned vehicles face challenges in planning efficient paths in real-time, especially when navigating through areas with obstacles, as existing methods require recalculation of vehicle states for each path, leading to delays and increased processing time.
Innovation Solution
Implementing a data structure to store and reuse vehicle-state data, including feasibility and weight assessments, allows for faster path planning by leveraging previously computed results for overlapping segments, reducing processing time and increasing the number of paths that can be evaluated within a given timeframe.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If vehicle-state data is recalculated for each path planning operation, then path planning accuracy is maintained, but path planning time increases
Solution Approach 1:
The patent pre-calculates and stores vehicle-state data (feasibility and weight assessments) in a data structure before path planning is needed. This preliminary computation allows the system to retrieve pre-evaluated vehicle-state information during actual path planning operations, eliminating the need to recalculate the same data repeatedly and significantly reducing path planning time.
Solution Approach 2:
The patent creates a copy of vehicle-state data (feasibility flags and weight values) and stores it in a reusable data structure. Instead of recalculating vehicle-state assessments for each path, the system copies and reuses the pre-computed data from the data structure, maintaining accuracy while dramatically improving computational efficiency and enabling evaluation of more paths within the same time frame.
2Reliability
If real-time path planning is implemented to avoid obstacles, then collision avoidance is improved, but processing complexity increases
Solution Approach 1:
The system performs preliminary evaluation of vehicle-state feasibility and weights before actual path planning occurs. By pre-determining which vehicle-states are feasible and assigning weights in advance, the system reduces the computational burden during real-time path planning, maintaining reliable collision avoidance while lowering processing complexity during critical real-time operations.
Solution Approach 2:
The data structure serves itself by storing and providing vehicle-state information that the path planning process needs. The pre-computed feasibility and weight data automatically becomes available during path planning without requiring complex real-time recalculations, allowing the system to maintain reliability while reducing the complexity of real-time processing through self-provided computational resources.
Data Source
AI summary
Thus, according to some examples of the presently disclosed subject matter, in order to reduce path planning time, information that has previously been calculated during planning of a path is reused during planning of other subsequent paths. Using the stored indication reduces time required for planning the path and thus enables to evaluate a greater number of optional paths within a given period of time. This can assist in increasing speed and smoothness of vehicle maneuverability.