Autonomous Mobile Navigation Using a Safe Region Beyond Sensor View
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Solution Overview
Problem
Traditional systems for autonomous mobile devices (AMDs) face challenges in safely navigating outside their sensor field-of-view due to the need for expensive additional sensors and increased computational requirements, or limitations in physical design that restrict movement without colliding with obstacles.
Innovation Solution
The use of a 'safe region' determined by sensor data to ensure safe movement outside the sensor field-of-view, which aggregates geographical areas recently observed to be obstacle-free, allowing the AMD to maintain a safe path without additional sensors and complex design constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If additional sensors are added to enable safe movement outside sensor field-of-view, then navigation safety is improved, but device cost and complexity increase
Solution Approach 1:
The system performs preliminary mapping of the environment using sensors during periods when the AMD is within the sensor field-of-view. This pre-acquired spatial information is stored and later used to guide movement outside the field-of-view, eliminating the need for additional sensors to detect obstacles in all directions simultaneously.
Solution Approach 2:
The system creates a digital representation (map) of the physical environment that copies the spatial layout and obstacle positions. This virtual model allows the AMD to navigate outside the sensor field-of-view by referencing the copied environmental data rather than requiring direct sensor observation in all directions.
2Measurement precision
If computational power is increased to process sensor data for safe movement, then navigation accuracy is improved, but energy consumption and processing latency increase
Solution Approach 1:
The computational task is divided into segments: during field-of-view periods, sensor data is processed and stored as segmented spatial information; during outside-field-of-view movement, pre-computed path segments are executed with minimal real-time processing. This segmentation reduces peak computational demands and energy consumption.
Solution Approach 2:
Environmental maps and obstacle positions are pre-processed and stored during periods when the AMD is within sensor range. This preliminary computation eliminates the need for intensive real-time processing during critical navigation phases, reducing both latency and energy consumption.
3Reliability
If physical design is constrained to maintain sensor field-of-view coverage, then collision avoidance is improved, but movement flexibility and speed are reduced
Solution Approach 1:
The system pre-maps the environment and identifies safe movement zones before the AMD needs to move outside the sensor field-of-view. This allows the AMD to execute flexible movements such as moving away from the sensor or navigating complex paths without requiring physical design changes to maintain continuous field-of-view coverage.
Solution Approach 2:
By creating a virtual model of the environment, the system decouples physical design constraints from navigation capabilities. The AMD can move flexibly in three-dimensional space while the copied environmental data provides collision avoidance guidance, eliminating the need for physical designs that maintain constant sensor coverage.
4Productivity
If movement outside sensor field-of-view is enabled, then navigation efficiency is improved, but safety risks increase without additional sensors
Solution Approach 1:
The system performs preliminary environmental mapping and identifies safe zones before the AMD moves outside the sensor field-of-view. This advance preparation ensures that even without continuous sensor observation, the AMD navigates only through pre-validated safe areas, maintaining high productivity while ensuring movement safety.
Data Source
AI summary
An autonomous mobile device (AMD) may move around a physical space while performing tasks. The AMD may have sensors with a field of view (FOV) facing forward. As the AMD moves forward, a safe region is determined based on data from those forward-facing sensors. The safe region describes a geographical area that is clear of obstacles during recent travel. If the AMD moves outside of the FOV, such as moving backwards, a check is made as to whether the AMD remains within the safe region. If a portion of the AMD moves beyond the safe region, the AMD stops. This prevents the AMD from colliding with obstacles that are outside of the safe region and also outside of the FOV while moving.