Mobile Robot Path Planning with Episodic Map Updates
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Solution Overview
Problem
Traditional path planning algorithms for mobile robots, such as A* and RRT, struggle in complex environments due to limited adaptability and low efficiency, resulting in non-ideal path planning, while episodic memory-based methods lack comprehensive understanding of the cognitive map, leading to suboptimal path planning.
Innovation Solution
The proposed method involves establishing an empirical map and episodic cognitive map using RatSLAM, extracting road edges with a Canny operator, judging connectivity, and fusing detected potential paths with the original cognitive map to update and improve the path planning using a potential path detection network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional path planning algorithms (A*, RRT) are used, then the algorithm is simple to implement, but the path planning effect is non-ideal in complex environments
Solution Approach 1:
The patent combines multiple algorithms (A*, RRT, and neural network-based potential path detection) into a hybrid path planning system. The neural network detects potential paths from historical data, which are then integrated with traditional algorithms to improve overall path planning performance in complex environments while maintaining implementation feasibility.
Solution Approach 2:
The system performs preliminary path detection using a trained neural network before executing traditional path planning algorithms. By pre-identifying potential paths from historical navigation data, the system prepares candidate paths that guide subsequent planning operations, improving efficiency and effectiveness in complex environments.
2Productivity
If episodic memory-based path planning is used, then the robot can plan paths based on existing cognitive maps, but the robot lacks understanding of the map and the path may not be globally shortest
Solution Approach 1:
The system uses historical navigation data and path planning results as feedback to continuously train and update the neural network. This feedback mechanism enables the robot to learn from past experiences, improve its understanding of the cognitive map over time, and progressively discover more optimal paths through iterative learning.
Solution Approach 2:
The patent replaces traditional mechanical/pathological map analysis methods with a neural network-based cognitive system. The neural network processes spatial relationships and path information more effectively, enabling deeper map understanding and better path optimization compared to conventional approaches.
3Reliability
If the robot explores potential safe paths to perfect the cognitive map, then better paths can be planned, but the searching process increases time consumption
Solution Approach 1:
The neural network is pre-trained offline using historical navigation data, so that during actual path planning, it can quickly detect potential paths without extensive real-time searching. This preliminary training phase separates the time-consuming learning process from the time-critical navigation operation, reducing online time consumption.
Solution Approach 2:
Instead of exhaustively searching all possible paths, the system uses the neural network to detect a subset of high-probability potential paths that are most likely to be safe and optimal. This partial search approach focuses computational resources on the most promising candidates, achieving good path quality with reduced searching time.
Data Source
AI summary
The present invention discloses a path planning method, including the following steps: establishing an empirical map and a corresponding episodic cognitive map using a RatSLAM algorithm based on an episodic memory model; extracting a road edge in a historical memory image with a Canny operator; performing conversion to a world coordinate system from a pixel coordinate system based on the road edge, and preliminarily judging connectivity according to slope of the road edge; continuously injecting energy into the potential path detection network according to continuous observation of a potential path, so as to further judge the road connectivity; fusing the detected potential path and the original episodic cognitive map, and correspondingly updating the empirical map; and planning a path based on the updated episodic cognitive map. The potential safe path in an environment may be detected, and a better path may be planned based on the updated episodic memory model.