3D UAV Path Planning With Any-Angle Cost-Aware Routing
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Solution Overview
Problem
Current three-dimensional path planning algorithms for UAVs in complex urban environments are time-consuming, generate unnatural and non-smooth paths, and fail to account for non-uniform traversal costs and safety risks, making them inefficient and incompatible with vehicle dynamics.
Innovation Solution
A method that discretizes a three-dimensional environment into a graph of nodes, assigns weighting factors for traversal costs, and incorporates turning, climbing, and descending costs into an any-angle path planning algorithm to calculate a natural and smooth path compatible with UAV dynamics, using an approximation of average traversal cost to reduce computation time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Length of moving object
If traditional graph-based path planning algorithms (A*, Dijkstra) are used to find the shortest path, then the path length is minimized, but the paths are constrained by grid edges resulting in non-smooth and unnatural trajectories
Solution Approach 1:
The patent applies any-angle path planning that allows movement in arbitrary directions rather than being constrained to grid edges. This enables curved and smooth trajectories by allowing the path to cut through grid cells diagonally and change direction continuously, producing natural-looking paths that are compatible with UAV dynamics while maintaining optimality.
2Ease of operation
If any-angle path planning is used to generate smooth paths, then path naturalness is improved, but computation time increases due to line-of-sight checks for each neighbor
Solution Approach 1:
The patent implements a lazy evaluation strategy where line-of-sight checks are performed only when necessary - specifically when a grid cell is expanded and its neighbors are being evaluated. This partial action approach avoids redundant visibility checks while still ensuring path optimality, significantly reducing computation time compared to performing checks for every generated neighbor.
3Productivity
If uniform traversal cost is used in path planning, then computation is simpler and faster, but non-uniform operational costs (turning, climbing, descending) are not accounted for
Solution Approach 1:
The patent assigns different traversal costs to different grid cells based on local environmental characteristics and operational requirements. Each grid cell can have unique costs for moving in different directions, accounting for turning costs, climbing costs, and descending costs. This local quality approach enables accurate cost modeling for UAV operations while maintaining computational efficiency through the any-angle path planning framework.
4Manufacturing precision
If grid step size is reduced to improve path quality, then path accuracy and smoothness are improved, but search space complexity and computation time increase
Solution Approach 1:
The any-angle path planning algorithm allows movement in continuous directions rather than being restricted to discrete grid directions. This enables the path to cut through grid cells optimally, achieving high path accuracy and smoothness even with coarser grid resolutions. The ability to move at any angle reduces the need for fine grid discretization, thereby lowering search space complexity while maintaining path quality.
Data Source
AI summary
The present invention provides a method for planning a shortest possible three-dimensional path for autonomous flying robots to traverse from one location to the other in a geographical region, including translating a three-dimensional (3D) environment, discretizing the 3D environment into a graph of many grid cells or nodes, employing a modified any-angle path planning algorithm to calculate non-uniform traversal cost of each grid cell and by averaging the total traversal costs along the path to shorten the corresponding computation time, whilst incorporating operational costs other than the traversal cost specific to the autonomous flying robots to be traversed. The shortest possible path found by the present method does not only consider the path length, but also takes different costs of traversing and operating the flying robots into account, which increases its feasibility and flexibility to be applied in a wide variety of situations and technological areas.