Drone Path Planning Using Frontier-Based Online Navigation
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Solution Overview
Problem
Current solutions for autonomous drone navigation in environments with static and dynamic obstacles are computationally costly and cannot be integrated into commercial drone collision avoidance systems, lacking a lightweight and efficient path planning method that does not require a map.
Innovation Solution
A simple online planning algorithm that allows drones to navigate by identifying frontier points within their field of view, using these points as waypoints to incrementally build a map and avoid obstacles, independent of specific sensors or map representations, enabling collision-free navigation in both 2D and 3D spaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional navigation algorithms are used to handle static and dynamic obstacles, then navigation capability is improved, but computational cost and memory requirements increase significantly
Solution Approach 1:
The patent segments the environment into frontier regions (unexplored areas) and explored areas, allowing the drone to focus computational resources only on identifying and navigating to frontier points rather than processing the entire environment. This segmentation reduces computational complexity while maintaining reliable obstacle avoidance and navigation capabilities.
Solution Approach 2:
The patent extracts key navigational features (frontier points) from the complete environmental map, using only these extracted points for path planning rather than processing the entire map. This extraction approach significantly reduces memory requirements and computational cost while preserving essential navigation information.
2Reliability
If complete environmental mapping is performed for autonomous navigation, then navigation accuracy is improved, but memory requirements and computational load increase
Solution Approach 1:
The patent extracts only the essential navigational information (frontier points representing boundaries between explored and unexplored regions) from the complete environmental map. This extraction allows the drone to achieve accurate navigation using minimal memory, storing only frontier point coordinates rather than complete environmental data.
Solution Approach 2:
Instead of using the complete map to determine navigation, the patent inverts the approach by using only the frontier points (the inverse of complete mapping) to guide navigation. This inversion reduces memory requirements while maintaining navigation accuracy by focusing only on critical boundary information.
3Reliability
If real-time obstacle avoidance is implemented in commercial drone systems, then collision safety is improved, but system complexity and computational burden increase beyond commercial viability
Solution Approach 1:
The patent implements a self-service mechanism where the drone autonomously identifies frontier points and generates navigation paths without requiring complex external processing or heavy computational resources. This self-service approach enables real-time obstacle avoidance in commercial drone systems by using simple, efficient algorithms that do not burden the system with excessive complexity.
Solution Approach 2:
The patent segments the complex navigation problem into simple sub-tasks: identifying frontier points, selecting the nearest frontier, and navigating to it. This segmentation transforms a complex real-time obstacle avoidance system into a series of simple, computationally efficient operations suitable for commercial drone implementation.
Data Source
AI summary
Various systems and methods for drone path planning are described herein. A system for drone path planning for a drone, the system to perform the operations: storing a current location of the drone as a current waypoint in a set of waypoints, the set of waypoints to include the current waypoint and a previous waypoint; detecting a current set of frontiers from the current location; storing the current set of frontiers in a list of active goals, the current set of frontiers associated with the current location; determining whether a target is discovered in the current set of frontiers; exploring the current set of frontiers to attempt to find the target; and exploring a previous set of frontiers from the list of active goals, when exploring the current set of frontiers does not, discover the target, the previous set of frontiers associated with the previous waypoint.