Autonomous Navigation Through Variable Obstacle Regions
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Solution Overview
Problem
Traditional systems for autonomous navigation in dynamic physical spaces face challenges in coping with variable obstacles, such as doors that open and close, leading to outdated path planning and potential navigation issues, resulting in longer routes and increased battery consumption.
Innovation Solution
The system determines and utilizes information about variable regions by inflating the occupancy map to designate areas associated with variable obstacles as traversable, allowing for more efficient path planning and movement, using data from sensors and visitation history to update navigation maps in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional occupancy map systems are used for autonomous navigation, then navigation safety is maintained by avoiding all detected obstacles, but path efficiency deteriorates due to outdated path planning and longer routes
Solution Approach 1:
The system dynamically updates the occupancy map by detecting variable regions where obstacles may have moved or changed state. Instead of treating all occupied cells as permanent obstacles, the system identifies regions with historical visitation data and updates their status in real-time, allowing the autonomous device to adapt its path planning to current conditions rather than relying on static or outdated obstacle information.
Solution Approach 2:
The system changes the parameter representation of obstacle regions by introducing variable region data that tracks historical occupancy states. Cells in variable regions are updated based on recent sensor data and visitation history, transforming static obstacle maps into dynamic representations that reflect current navigable conditions, thereby improving path efficiency without compromising safety.
2Reliability
If conservative navigation avoiding all obstacles is used, then collision risk is reduced, but energy consumption increases due to longer routes
Solution Approach 1:
The system changes the occupancy status parameter for cells in variable regions based on updated sensor data and visitation history. By dynamically updating whether a cell is marked as occupied or unoccupied, the system enables shorter, more energy-efficient paths while maintaining collision avoidance through continuous monitoring and safe navigation protocols.
3Measurement precision
If real-time occupancy map updates are implemented, then navigation accuracy improves by accounting for variable obstacles, but system complexity increases
Solution Approach 1:
The system segments the occupancy map into static regions and variable regions. Static regions contain permanent obstacles that do not change, while variable regions are dynamically updated based on sensor data and visitation history. This segmentation allows the system to apply different update strategies to different regions, improving obstacle detection accuracy for variable regions without unnecessarily complicating the handling of static regions.
Solution Approach 2:
The system performs preliminary identification of variable regions using historical visitation data before real-time navigation. By pre-marking regions where obstacles are likely to change based on past observations, the system reduces the computational burden during real-time operation, as these regions are already flagged for monitoring without requiring complex real-time analysis of every occupied cell.
Data Source
AI summary
An autonomous mobile device (AMD) moves through a physical space without human intervention. Data from sensors on the AMD is used to determine an occupancy map indicative of obstacles and their locations in the physical space. Obstacles may be static, variable, or dynamic. For example, walls are static, doors which open and close are variable, and people moving through the environment are dynamic. Regions in the occupancy map that the AMD has visited but which are shown by the occupancy map as containing obstacles are identified as being variable regions. To determine possible paths through the physical space during autonomous movement, the variable regions are considered to be unoccupied by obstacles and traversable. Information about how often the variable region transitions between traversable and impassible states may also be used to determine the possible paths.