Mobile Robot Route Planning With Redundant Node Filtering
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Solution Overview
Problem
Existing route planning algorithms for mobile robots, such as the A* algorithm and dynamic window algorithm, suffer from inefficiencies due to redundant nodes and excessive turning points, leading to suboptimal route planning that is not suitable for actual robot movement.
Innovation Solution
A method combining the A* algorithm for global route planning and dynamic window algorithm for local optimization, involving redundant node filtering to create a smoother and more efficient route by using raster map data, where the A* algorithm plans a global route and the dynamic window algorithm further optimizes it section by section.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Length of moving object
If the A* algorithm is used for global route planning, then the route length is optimized, but the route contains redundant nodes and excessive turning points
Solution Approach 1:
The patent extracts and removes redundant nodes from the route generated by the A* algorithm. By identifying nodes that do not contribute to optimal pathfinding and removing them, the route complexity is reduced while maintaining the optimized route length achieved by A*.
Solution Approach 2:
Instead of directly generating a simple route, the patent first generates an optimized route using A* and then inverts the approach by filtering out redundant elements. This two-step inversion process transforms a complex optimized route into a simpler final route.
2Reliability
If the dynamic window algorithm is used for local obstacle avoidance, then the robot movement posture is optimized, but too many nodes need to be traversed to reach the destination
Solution Approach 1:
The patent segments the global route into multiple sections and applies the dynamic window algorithm only to specific segments where local optimization is needed, rather than applying it to the entire route. This reduces the number of nodes traversed while maintaining movement posture optimization in critical areas.
Solution Approach 2:
The patent applies local quality optimization by using the dynamic window algorithm selectively on specific segments of the route rather than uniformly across the entire path. This ensures high movement posture quality where needed while improving overall route efficiency.
3Shape
If the global route is optimized by filtering redundant nodes, then the route smoothness is improved, but the calculation complexity increases
Solution Approach 1:
The patent performs preliminary filtering of redundant nodes during the route planning phase before execution. By removing redundant nodes in advance, the route smoothness is improved and the computational burden during actual robot navigation is reduced.
4Adaptability or versatility
If more turning points are included in the route, then the route can adapt to obstacles, but the robot movement becomes less efficient
Solution Approach 1:
The patent extracts and removes unnecessary turning points from the route while preserving those that are essential for obstacle avoidance. This reduces the total number of turns and improves movement efficiency while maintaining adequate adaptability to obstacles.
Data Source
AI summary
The present disclosure discloses a method for route optimization based on dynamic window and redundant node filtering, comprising using an existing raster map data set to determine the coordinate information of a starting position and a destination position of movement, and to mark a destination node and an obstacle node in the raster map; using A* algorithm to plan a global route; globally optimizing the global route planned by A* algorithm, and filtering redundant nodes out; combining a dynamic window algorithm to perform the local optimization section by section on the optimized global route so as to obtain a final global route. According to the present disclosure, the combination of algorithms reduces a single movement duration of a mobile robot and improves the smoothness of the movement route curve. At the same time, the problems of the robot occurring on the route during the static driving are alleviated.