Aerocraft Waypoint Planning for Smooth Obstacle-Avoiding Paths
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Solution Overview
Problem
Current automatic control aerocraft path planning methods using smooth curves are inadequate for reasonably avoiding obstacles and ensuring path smoothness, leading to suboptimal waypoint selection.
Innovation Solution
A cooperative path planning method that initializes and updates the positions of selected points based on evaluation indices, considering historical and global optimal positions, self-learning, and global learning probabilities, to determine the next waypoint while avoiding obstacles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If smooth curve construction method is used for path planning, then path smoothness is improved, but obstacle avoidance capability deteriorates
Solution Approach 1:
The path planning problem is segmented into multiple candidate paths generated by different algorithms (smooth curve method and another method). Instead of relying on a single method, the system divides the solution space into multiple discrete path options that can be evaluated and combined, allowing both smoothness and obstacle avoidance to be optimized through selection rather than construction of a single continuous curve.
Solution Approach 2:
The system changes the evaluation parameters by introducing an evaluation index that quantifies both path smoothness and obstacle avoidance performance. By transforming qualitative path characteristics into measurable parameters, the system can objectively compare and select from multiple candidate paths, resolving the contradiction between smoothness and obstacle avoidance through parameter-based optimization.
2Stability of the object's composition
If smooth curve construction method is used for path planning, then path continuity is improved, but waypoint selection accuracy deteriorates
Solution Approach 1:
The system implements feedback through the evaluation index mechanism, where candidate paths are generated, evaluated against multiple criteria including continuity and waypoint accuracy, and then selected based on comprehensive performance assessment. This feedback loop allows the system to identify optimal waypoints by evaluating their contribution to both path continuity and selection accuracy, rather than relying solely on smooth curve construction.
Solution Approach 2:
The system transforms the waypoint selection problem into a parameter optimization problem by defining evaluation indices that measure both path continuity and waypoint accuracy. By changing from direct geometric construction to parameter-based evaluation and selection, the system can simultaneously optimize multiple competing requirements including continuity and accuracy.
3Device complexity
If traditional path planning method is used, then computational simplicity is improved, but path optimization capability deteriorates
Solution Approach 1:
The path planning process is segmented into distinct stages: candidate path generation by multiple algorithms, evaluation of candidates using defined indices, and selection of optimal paths. This segmentation allows the system to maintain computational efficiency at each stage while achieving superior overall optimization, avoiding the need for a single complex optimization algorithm.
Solution Approach 2:
The system employs multiple path planning algorithms (smooth curve method and another method) that serve universal functions of generating candidate paths. This multi-functional approach allows the system to leverage the strengths of different algorithms without committing to the complexity of a single sophisticated method, achieving better optimization through diverse candidate generation.
Data Source
AI summary
This disclosure provides a cooperative path planning method and device for automatic control aerocraft and aerocraft system. The method includes: determining the current evaluation index of each selected point according to its current position, the target position of the aerocraft and the position of obstacles in the selected area; updating the adjustment displacement of each selected point according to its adjustment displacement, current position and historical optimal position, as well as the global optimal position; updating the current position of each selected point according to its current position and the updated adjustment displacement; after the current position is updated for a set number of times, selecting the position corresponding to the optimal evaluation index from the current positions and global optimal position of all selected points as the next waypoint of the aerocraft.