Systems and methods for enabling navigation in environments with dynamic objects
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Solution Overview
Problem
Indoor mobile industrial robots struggle with navigation due to their inability to recognize dynamic changes in their environment and rely on static features for navigation, which can be unreliable when these features are moved or absent.
Innovation Solution
A navigation system that uses remote sensors to determine features in the environment, allocates weights to these features based on their static or dynamic nature, and uses these weights to determine the position of the robot unit relative to the features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Stability of the object's composition
If the robot navigation system relies on static features for positioning and mapping, then the navigation stability is improved, but the system fails when features are moved or changed in dynamic environments
Solution Approach 1:
The system dynamically classifies features as static or dynamic based on temporal analysis of sensor data. Features are re-evaluated across multiple time points, and their classification can change over time. This allows the navigation system to adapt to environmental changes while maintaining stability through weighted feature selection, resolving the contradiction between navigation stability and environmental adaptability.
2Loss of information
If the system uses all detected features for navigation, then the coverage of environmental information is improved, but the reliability of position determination decreases due to dynamic features
Solution Approach 1:
The system applies different quality weights to different features based on their static/dynamic classification. Static features receive higher weights for position determination, while dynamic features receive lower weights or are excluded. This local differentiation allows the system to maintain comprehensive environmental information coverage while ensuring reliable position determination through selective feature weighting.
Solution Approach 2:
The system performs partial action by selectively using only the most reliable static features for position determination, rather than using all detected features. This partial utilization of features ensures high reliability in positioning while still maintaining awareness of the complete environmental landscape through full feature detection and classification.
3Device complexity
If the navigation system assumes all features are static, then the algorithm complexity is reduced, but the system cannot cope with moved or new objects
Solution Approach 1:
The system implements periodic re-evaluation of feature characteristics at multiple time points. Instead of a single complex dynamic analysis, the system periodically checks feature positions and updates classifications based on temporal patterns. This periodic approach maintains relatively simple algorithms while effectively handling dynamic objects through repeated observations and weight adjustments.
Data Source
AI summary
An indoor mobile industrial robot system is configured to provide a weight to a detected object within an operating environment, where the weight relates to how static the feature is. The indoor mobile industrial robot system includes a mechanism configured to translate reflected light energy and positional information into a set of data points representing the detected object having at least one of Cartesian and/or polar coordinates, and an intensity. If any discrete data point within the set of data points representing the detected object has an intensity at or above a defined threshold the entire set of data points is converted into a weight and potentially classified representing a static feature, otherwise such set of data points is classified as representing a dynamic feature having a lower weight.