UAV Depth-Map Path Planning for Real-Time Obstacle Avoidance
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Solution Overview
Problem
Current UAV path planning methods are inadequate for dynamic and unknown environments, as they fail to accurately detect obstacles in real-time, leading to potential collisions.
Innovation Solution
A UAV path planning method and apparatus that utilizes a depth sensor and flight control system to acquire and process depth maps, perform depth compensation, and determine optimal flight directions by creating grid maps and calculating cost functions to avoid obstacles in dynamic environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If global path planning method is used, then collision prevention is improved, but real-time performance deteriorates in dynamic environments
Solution Approach 1:
The patent divides path planning into two independent segments: global path planning for overall route determination and local path planning for real-time obstacle avoidance. The local planning module processes depth map data to identify obstacles and generate immediate avoidance maneuvers, while the global planner provides the overall trajectory. This segmentation allows each module to operate at appropriate time scales, with local planning handling real-time dynamics without burdening the global planner.
Solution Approach 2:
The patent implements dynamic local path planning that continuously adapts to changing environmental conditions. The system uses depth sensors to capture real-time depth maps, dynamically identifies obstacles based on current flight state and environmental data, and generates adaptive avoidance paths. This dynamic approach allows the UAV to respond to moving obstacles and changing terrain in real-time, maintaining both safety and real-time performance.
2Reliability
If global path planning method is used, then collision prevention is improved, but obstacle detection accuracy deteriorates in unknown environments
Solution Approach 1:
The patent performs preliminary depth compensation processing on depth map data before obstacle identification. The system pre-calculates compensation values based on the UAV's pitch angle and applies these to correct depth measurements in advance. This preliminary action ensures that obstacle detection is performed on already-corrected data, improving detection accuracy in unknown environments without adding computational delay during critical decision-making.
Solution Approach 2:
The patent replaces traditional mechanical or simple optical sensing systems with a depth sensor-based vision system. The depth sensor captures depth information to generate depth maps, which are then processed to identify obstacles. This substitution provides more accurate three-dimensional spatial information compared to traditional sensors, enabling better obstacle detection and characterization in unknown environments.
3Measurement precision
If depth compensation is performed, then obstacle detection accuracy is improved, but processing complexity increases
Solution Approach 1:
The patent changes the parameter space by transforming the problem from three-dimensional depth correction to two-dimensional pixel row shifting. Instead of performing complex 3D geometric transformations, the system calculates a pitch angle and uses it to determine a shift in pixel rows in the depth map. This parameter change simplifies the compensation process while maintaining accuracy, reducing computational complexity from full 3D transformation to simple 2D array manipulation.
Solution Approach 2:
The patent applies depth compensation selectively based on the UAV's flight state. The system calculates pitch angle and determines whether compensation is needed based on current orientation. Only the necessary portions of the depth map requiring correction are processed, rather than uniformly processing the entire map. This local approach reduces overall processing complexity while maintaining detection accuracy where it matters most.
Data Source
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AI summary
Embodiments of the present application relate to the technical field of robot control, and in particular, to an unmanned aerial vehicle (UAV) path planning method and apparatus and a UAV. The UAV path planning method includes: acquiring a depth map of an environment in front of a UAV; acquiring a grid map centered on a body of the UAV according to the depth map; determining candidate flight directions for the UAV according to the grid map; determining an optimal flight direction for the UAV from the candidate flight directions; and controlling the UAV to fly in the optimal flight direction to avoid an obstacle in the environment in front of the UAV. In this way, the embodiments of the present application can accurately determine an obstacle that suddenly appears in an unknown environment and a dynamic environment, so as to achieve real-time path planning.