Robot Navigation Path Planning for Stability
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Solution Overview
Problem
Current navigation systems for robots often result in the robot getting stuck or vibrating during movement due to inadequate path planning, which affects the smoothness and stability of autonomous navigation.
Innovation Solution
A navigation method and apparatus that determine a global planning path, an initial local path based on a local costmap, and generate a navigation control instruction tailored to the robot's model, incorporating obstacle information and using algorithms like A* and model predictive control to ensure accurate and smooth movement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a simple path planning algorithm is used, then the navigation system is easier to implement, but the robot gets stuck or vibrated during movement
Solution Approach 1:
The patent divides the path planning into two segments: global path planning (using A* algorithm for overall route) and local path planning (using dynamic window approach for detailed trajectory). This segmentation allows each level to optimize independently, improving both implementation clarity and navigation stability.
Solution Approach 2:
The patent implements dynamic path adjustment by continuously updating the local costmap based on robot position and obstacles. The local planning range adapts to the robot's current state, enabling real-time adjustments that prevent getting stuck and reduce vibrations while maintaining stable navigation.
2Measurement precision
If the local planning range is increased, then the robot can see further ahead and plan better, but the computational complexity increases
Solution Approach 1:
The patent applies different planning qualities at different ranges: global planning uses A* algorithm for overall route with lower computational demand, while local planning uses dynamic window approach with higher precision for immediate trajectory. The local costmap focuses computational resources only on the immediate planning range rather than the entire environment.
Solution Approach 2:
The patent uses partial action by planning only the immediate local range rather than the entire global path at each step. This partial local planning approach provides sufficient accuracy for smooth navigation while keeping computational complexity manageable, avoiding the excessive action of re-planning the entire global path continuously.
3Productivity
If the robot follows the global planning path strictly, then the overall route is optimized, but the robot cannot avoid dynamic obstacles
Solution Approach 1:
The patent segments path planning into global (A* algorithm for route optimization) and local (dynamic window approach for obstacle avoidance) levels. The global path provides efficient overall routing while the local planner handles dynamic obstacle avoidance, achieving both productivity and safety.
Solution Approach 2:
The patent implements feedback by continuously updating the local costmap based on real-time robot position and obstacle detection. This feedback mechanism allows the local planner to adjust the trajectory in response to dynamic obstacles while maintaining the global path's efficiency, preventing collisions without sacrificing navigation productivity.
Data Source
AI summary
Provided are a navigation method and apparatus, a storage medium, and a device. The method includes the steps below. A global planning path in a target map is determined according to a current position of a robot and an end position in a navigation request. An initial local path corresponding to the global planning path is determined based on a local planning range. The local planning range corresponds to the boundary of a local costmap. The local costmap includes obstacle information within the local planning range. A local planning path corresponding to the initial local path is generated according to the initial local path and the local costmap. A navigation control instruction of the robot is generated according to the local planning path and a preset robot model corresponding to the robot.