Frontier-Based Obstacle Probability Mapping for Autonomous Exploration
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Solution Overview
Problem
Conventional methods for establishing maps in new regions by smart devices are time-consuming and resource-intensive, especially when dealing with large and complex target regions, as they require manual user control to detect obstacle presence probabilities.
Innovation Solution
A method where a smart device or control terminal autonomously detects the presence probability of obstacles in unknown positions by determining target frontiers, moving to them, and updating the map, using grid maps and topological maps with distance sensors and navigation algorithms, thereby reducing the need for manual user intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a user manually controls a smart device to move through a region to establish a map, then the map can be updated with obstacle information, but a lot of time is consumed and the operation is complex
Solution Approach 1:
The smart device autonomously determines frontiers and controls its own movement to detect obstacles, eliminating the need for manual user control. The device independently updates the map by processing sensor data and navigating to unknown regions, making the system self-sufficient in map establishment tasks
Solution Approach 2:
The system pre-processes map data to identify frontiers (boundaries between known and unknown regions) before actual detection begins. By pre-calculating navigation paths to these frontiers, the device is ready for immediate autonomous exploration, reducing the time required for map establishment
2Measurement precision
If a user manually controls the smart device to detect obstacle presence in unknown positions, then accurate obstacle information is obtained, but the operation becomes complex and time-consuming
Solution Approach 1:
The smart device automatically determines frontiers from map data and independently controls its movement to explore unknown regions. The system processes sensor data to calculate obstacle presence probabilities without user intervention, significantly simplifying operation while maintaining detection accuracy
Solution Approach 2:
The system dynamically adjusts navigation behavior based on real-time sensor feedback and map updates. The device automatically re-plans paths when new frontiers are identified or obstacles are detected, adapting its operation without requiring manual user input
3Productivity
If the smart device autonomously determines frontiers and navigates to unknown positions, then map establishment time is reduced, but the device complexity increases
Solution Approach 1:
The autonomous navigation system is divided into distinct functional modules: frontier determination module, path planning module, sensor data processing module, and map updating module. Each module handles a specific aspect of the autonomous operation, making the overall complex system more manageable and maintainable
Solution Approach 2:
The smart device integrates multiple functions into a single platform: navigation, obstacle detection, sensor data processing, and map management. This multi-functional approach consolidates what would otherwise require separate systems, balancing autonomy capabilities with device complexity
4Measurement precision
If the device updates the map continuously with obstacle presence probability, then the map accuracy is improved, but storage resources are occupied
Solution Approach 1:
Instead of uniformly updating the entire map, the system focuses computational and storage resources on frontier regions and areas with high uncertainty. Obstacle presence probability is calculated and stored selectively for unknown positions, reducing overall storage requirements while maintaining map accuracy in critical areas
Data Source
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AI summary
The present disclosure provides a method and device OF detecting a presence probability of obstacle in an unknown position, belonging to the field of a computer technology. The method includes: determining at least one frontier included in a currently established map during a mobile detection process for establishing a map for a target region, wherein the frontier is a position point which is in an unoccupied position and is adjacent to a border between the unoccupied position and an unknown position in the map; determining a target frontier satisfying a preset detection condition from the at least one frontier based on the position information of the at least one frontier; and controlling a smart device to move to the target frontier, and detecting a presence probability of obstacle in the unknown position included in the map. According to the method and the device of the present disclosure, the time can be saved for users.