A-Star Path Search Optimality with Dynamic Cost Updates
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Solution Overview
Problem
The A-Star algorithm fails to guarantee an optimal path when the surrounding situation of a movable body changes, such as due to changes in flight safety risk or goal location, during the path search process.
Innovation Solution
A path searching apparatus and method that predicts future surrounding situations using a predicting unit and updates the estimated smallest cost value h* when a change is detected, ensuring the optimal path is determined by considering future scenarios and adjusting the path accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If A-Star algorithm is used to search for optimal path, then the optimal path can be determined when surrounding situation is static, but the optimal path cannot be guaranteed when surrounding situation changes during searching
Solution Approach 1:
The patent applies dynamics by making the path searching system adaptive to changing environments. The estimating unit dynamically updates the cost function parameters (g(n) and h(n)) based on real-time surrounding situation changes detected during the A-Star search process. This allows the algorithm to maintain optimality guarantees even when obstacles, risks, or goal locations change, transforming a static algorithm into a dynamic one that responds to environmental variations.
Solution Approach 2:
The patent implements feedback by continuously monitoring surrounding situation changes and using this information to update cost estimates. The detecting unit monitors changes in obstacles, risks, and goal locations, and this feedback is fed back to the estimating unit which recalculates g(n) and h(n) values. This closed-loop feedback mechanism ensures that the path search remains optimal despite dynamic environmental changes.
2Reliability
If the estimated smallest cost value h*(n) is updated when surrounding situation changes, then the optimal path can be guaranteed in dynamic environment, but the computing load increases
Solution Approach 1:
The patent applies local quality by updating cost estimates selectively rather than globally. The detecting unit identifies specific changes in the surrounding situation (obstacles, risks, goal locations), and the estimating unit updates only the affected g(n) and h(n) values for relevant nodes in the search space. This localized update approach reduces unnecessary computations compared to a full re-search, thereby lowering computing load while maintaining optimality.
Solution Approach 2:
The patent uses preliminary action by pre-calculating and storing cost estimates g(n) and h(n) before the actual path search begins. When surrounding situation changes occur, the system builds upon these pre-computed values rather than starting from scratch. The estimating unit adjusts the pre-calculated estimates based on detected changes, which is more efficient than performing complete re-computations, thus reducing computing load.
Data Source
AI summary
A path searching apparatus includes: a path searching unit that searches for an optimal path of a movable body that performs traveling from a start node to a goal node by performing a path searching process based on A-Star algorithm, and a predicting unit that predicts a surrounding situation of the movable body at each of times in future. The path searching unit determines whether a first surrounding situation involves a change from a second surrounding situation, on a basis of the predicted surrounding situation, and upon determining that the first surrounding situation involves the change, updates an estimated smallest cost value h* of a node on which the searching has been already performed, on a basis of the first surrounding situation.